WO2020224613A1

WO2020224613A1 - Unified calculation method for inter prediction with refinement

Info

Publication number: WO2020224613A1
Application number: PCT/CN2020/088927
Authority: WO
Inventors: Hongbin Liu; Li Zhang; Kai Zhang; Jizheng Xu; Yue Wang
Original assignee: Beijing ByteDance Network Technology Co Ltd; ByteDance Inc
Current assignee: Beijing ByteDance Network Technology Co Ltd; ByteDance Inc
Priority date: 2019-05-07
Filing date: 2020-05-07
Publication date: 2020-11-12
Anticipated expiration: 2021-11-07
Also published as: CN113767634A; CN113767634B

Abstract

A method ofvisual media processing includes performing a conversion between a current block of visual media data and a corresponding coded representation of the current video block, wherein the conversion of the current block includes computing a spatial gradient based on sample differences of spatially neighboring samples and/or a temporal gradient based on temporally neighboring samples associated with the current block, and wherein a technique used for computing the spatial gradient is same as a technique used for computing the temporal gradient.

Description

UNIFIED CALCULATION METHOD FOR INTER PREDICTION WITH REFINEMENT

CROSS REFERENCE TO RELATED APPLICATIONS

Under the applicable patent law and/or rules pursuant to the Paris Convention, this application is made to timely claim the priority to and benefit of International Patent Application No. PCT/CN2019/085796, filed on May 7, 2019. For all purposes under the law, the entire disclosure of the aforementioned application is incorporated by reference as part of the disclosure of this application.

TECHNICAL FIELD

This document is related to video and image coding and decoding technologies.

BACKGROUND

Digital video accounts for the largest bandwidth use on the internet and other digital communication networks. As the number of connected user devices capable of receiving and displaying video increases, it is expected that the bandwidth demand for digital video usage will continue to grow.

SUMMARY

In one example aspect, a method of visual media processing is disclosed. The method includes performing a conversion between a current block of visual media data and a corresponding coded representation of the current video block, wherein the conversion of the current block includes computing a spatial gradient based on sample differences of spatially neighboring samples and/or a temporal gradient based on temporally neighboring samples associated with the current block, and wherein a technique used for computing the spatial gradient is same as a technique used for computing the temporal gradient

In another example aspect, a method of visual media processing is disclosed. The method includes performing a conversion between a current block of visual media data and a corresponding coded representation of the current video block, wherein the conversion of the current block includes computing a spatial gradient based on computing sample differences of spatially neighboring samples associated with the current block, and wherein a left-shifting or a right-shifting of the sample differences of the spatially neighboring samples is applied prior to computing the sample differences

In another example aspect, the above-described methods may be implemented by a video decoder apparatus that comprises a processor.

In another example aspect, the above-described methods may be implemented by a video encoder apparatus that comprises a processor.

In yet another example aspect, these methods may be embodied in the form of processor-executable instructions and stored on a computer-readable program medium.

These, and other, aspects are further described in the present document.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of bilateral matching.

FIG. 2 shows an example of template matching.

FIG. 3 shows an example of unilateral motion estimation (ME) in Frame-Rate Up Conversion (FRUC) .

FIG. 4 shows an example of optical flow trajectory.

FIGs. 5A and 5B show examples of bi-directional optical flow (BIO) without block extension.

FIG. 6 shows an example of bilateral matching with 6 points search.

FIG. 7 shows examples of an adaptive integer search pattern and a half sample search pattern.

FIG. 8 is a block diagram of an example of a video processing apparatus.

FIG. 9 shows a block diagram of an example implementation of a video encoder.

FIG. 10 is a flowchart for an example of a video processing method.

FIG. 11 is a flowchart for an example of a video processing method.

FIG. 12 is a flowchart for an example of a video processing method.

FIG. 13 is a flowchart for an example of a video processing method.

FIG. 14 is a flowchart for an example of a video processing method.

FIG. 15 is a flowchart for an example of a video processing method.

FIG. 16 is a flowchart for an example of a video processing method.

FIG. 17 is a block diagram of an example video processing system in which disclosed techniques may be implemented.

FIG. 18 is a flowchart for an example of a visual media processing method.

FIG. 19 is a flowchart for an example of a visual media processing method.

DETAILED DESCRIPTION

To improve compression ratio of video, researchers are continually looking for new techniques by which to encode video. The present document provides various techniques that can be used by a decoder of video bitstreams to improve the quality of decompressed or decoded digital video. Furthermore, a video encoder may also implement these techniques during the process of encoding in order to reconstruct decoded frames used for further encoding.

Section headings are used in the present document for improving readability and do not limit the scope of techniques and embodiments described in each section only to that section. Furthermore, while certain terms from various existing video codec standards are used, the disclosed technologies are not limited only to these video standards or their successors and are applicable to other video codec standards. Furthermore, in some cases, techniques are disclosed using corresponding coding steps, and it will be understood that, at a decoder, the corresponding decoding steps in reverse order will be performed. In addition, coding may also be used to perform transcoding in which a video is represented from one coded representation (e.g., one bitrate) to another coded representation (e.g., a different bitrate) .

1. Summary

This patent document is related to video coding/decoding technologies. Specifically, it is related to motion compensation in video coding/decoding. It may be applied to the existing video coding standard like HEVC, or the standard (Versatile Video Coding) to be finalized. It may be also applicable to future video coding standards or video codec.

2. Initial discussion

Video coding standards have evolved primarily through the development of the well-known ITU-T and ISO/IEC standards. The ITU-T produced H. 261 and H. 263, ISO/IEC produced MPEG-1 and MPEG-4 Visual, and the two organizations jointly produced the H. 262/MPEG-2 Video and H. 264/MPEG-4 Advanced Video Coding (AVC) and H. 265/HEVC standards. Since H. 262, the video coding standards are based on the hybrid video coding structure wherein temporal prediction plus transform coding are utilized. To explore the future video coding technologies beyond HEVC, Joint Video Exploration Team (JVET) was founded by VCEG and MPEG jointly in 2015. Since then, many new methods have been adopted by JVET and put into the reference software named Joint Exploration Model (JEM) . In April 2018, the Joint Video Expert Team (JVET) between VCEG (Q6/16) and ISO/IEC JTC1 SC29/WG11 (MPEG) was created to work on the VVC standard targeting at 50%bitrate reduction compared to HEVC.

The latest version of VVC draft, i.e., Versatile Video Coding (Draft 2) could be found at: http: //phenix. it-sudparis. eu/jvet/doc_end_user/documents/11_Ljubljana/wg11/JVET-K1001-v7. zip. The latest reference software of VVC, named VTM, could be found at: https: //vcgit. hhi. fraunhofer. de/jvet/VVCSoftware_VTM/tags/VTM-2.1.

FIG. 9 is a block diagram of an example implementation of a video encoder. FIG. 9 shows that the encoder implementation has a feedback path built in in which the video encoder also performs video decoding functionality (reconstructing compressed representation of video data for use in encoding of next video data) .

2.1 Pattern matched motion vector derivation

Pattern matched motion vector derivation (PMMVD) mode is a special merge mode based on Frame-Rate Up Conversion (FRUC) techniques. With this mode, motion information of a block is not signalled but derived at decoder side.

A FRUC flag is signalled for a CU when its merge flag is true. When the FRUC flag is false, a merge index is signalled and the regular merge mode is used. When the FRUC flag is true, an additional FRUC mode flag is signalled to indicate which method (bilateral matching or template matching) is to be used to derive motion information for the block.

At encoder side, the decision on whether using FRUC merge mode for a CU is based on RD cost selection as done for normal merge candidate. That is the two matching modes (bilateral matching and template matching) are both checked for a CU by using RD cost selection. The one leading to the minimal cost is further compared to other CU modes. If a FRUC matching mode is the most efficient one, FRUC flag is set to true for the CU and the related matching mode is used.

Motion derivation process in FRUC merge mode has two steps. A CU-level motion search is first performed, then followed by a Sub-CU level motion refinement. At CU level, an initial motion vector is derived for the whole CU based on bilateral matching or template matching. First, a list of MV candidates is generated and the candidate which leads to the minimum matching cost is selected as the starting point for further CU level refinement. Then a local search based on bilateral matching or template matching around the starting point is performed and the MV results in the minimum matching cost is taken as the MV for the whole CU. Subsequently, the motion information is further refined at sub-CU level with the derived CU motion vectors as the starting points.

For example, the following derivation process is performed for a W×H CU motion information derivation. At the first stage, MV for the whole W×H CU is derived. At the second stage, the CU is further split into M×M sub-CUs. The value of M is calculated as in (16) , D is a predefined splitting depth which is set to 3 by default in the JEM. Then the MV for each sub-CU is derived as:

As shown in FIG. 1, the bilateral matching is used to derive motion information of the current CU by finding the closest match between two blocks along the motion trajectory of the current CU in two different reference pictures. Under the assumption of continuous motion trajectory, the motion vectors MV0 and MV1 pointing to the two reference blocks shall be proportional to the temporal distances, i.e., TD0 and TD1, between the current picture and the two reference pictures. As a special case, when the current picture is temporally between the two reference pictures and the temporal distance from the current picture to the two reference pictures is the same, the bilateral matching becomes mirror based bi-directional MV.

As shown in FIG. 2, template matching is used to derive motion information of the current CU by finding the closest match between a template (top and/or left neighbouring blocks of the current CU) in the current picture and a block (same size to the template) in a reference picture. Except the aforementioned FRUC merge mode, the template matching is also applied to AMVP mode. In the JEM, as done in HEVC, AMVP has two candidates. With template matching method, a new candidate is derived. If the newly derived candidate by template matching is different to the first existing AMVP candidate, it is inserted at the very beginning of the AMVP candidate list and then the list size is set to two (meaning remove the second existing AMVP candidate) . When applied to AMVP mode, only CU level search is applied.

● CU level MV candidate set

The MV candidate set at CU level can include:

● Original AMVP candidates if the current CU is in AMVP mode,

● all merge candidates,

● several MVs in the interpolated MV field, which is introduced in section 2.1.1.3, and

● top and left neighbouring motion vectors

When using bilateral matching, each valid MV of a merge candidate is used as an input to generate a MV pair with the assumption of bilateral matching. For example, one valid MV of a merge candidate is (MVa, refa) at reference list A. Then the reference picture refb of its paired bilateral MV is found in the other reference list B so that refa and refb are temporally at different sides of the current picture. If such a refb is not available in reference list B, refb is determined as a reference which is different from refa and its temporal distance to the current picture is the minimal one in list B. After refb is determined, MVb is derived by scaling MVa based on the temporal distance between the current picture and refa, refb.

Four MVs from the interpolated MV field are also added to the CU level candidate list. More specifically, the interpolated MVs at the position (0, 0) , (W/2, 0) , (0, H/2) and (W/2, H/2) of the current CU are added.

When FRUC is applied in AMVP mode, the original AMVP candidates are also added to CU level MV candidate set.

At the CU level, up to 15 MVs for AMVP CUs and up to 13 MVs for merge CUs are added to the candidate list.

● Sub-CU level MV candidate set

The MV candidate set at sub-CU level can include:

● an MV determined from a CU-level search,

● top, left, top-left and top-right neighbouring MVs,

● scaled versions of collocated MVs from reference pictures,

● up to 4 ATMVP candidates, and

● up to 4 STMVP candidates.

The scaled MVs from reference pictures are derived as follows. All the reference pictures in both lists are traversed. The MVs at a collocated position of the sub-CU in a reference picture are scaled to the reference of the starting CU-level MV.

ATMVP and STMVP candidates are limited to the four first ones.

At the sub-CU level, up to 17 MVs are added to the candidate list.

● Generation of interpolated MV field

Before coding a frame, interpolated motion field is generated for the whole picture based on unilateral ME. Then the motion field may be used later as CU level or sub-CU level MV candidates.

First, the motion field of each reference pictures in both reference lists is traversed at 4×4 block level. For each 4×4 block, if the motion associated to the block passing through a 4×4 block in the current picture (as shown in FIG. 3) and the block has not been assigned any interpolated motion, the motion of the reference block is scaled to the current picture according to the temporal distance TD0 and TD1 (the same way as that of MV scaling of TMVP in HEVC) and the scaled motion is assigned to the block in the current frame. If no scaled MV is assigned to a 4×4 block, the block’s motion is marked as unavailable in the interpolated motion field.

● Interpolation and matching cost

When a motion vector points to a fractional sample position, motion compensated interpolation can be performed. To reduce complexity, bi-linear interpolation instead of regular 8-tap HEVC interpolation is used for both bilateral matching and template matching.

The calculation of matching cost is a bit different at different steps. When selecting the candidate from the candidate set at the CU level, the matching cost is the absolute sum difference (SAD) of bilateral matching or template matching. After the starting MV is determined, the matching cost C of bilateral matching at sub-CU level search is calculated as follows:

where w is a weighting factor which is empirically set to 4, MV and MV ^s indicate the current MV and the starting MV, respectively. SAD is still used as the matching cost of template matching at sub-CU level search.

In FRUC mode, MV is derived by using luma samples only. The derived motion will be used for both luma and chroma for MC inter prediction. After MV is decided, final MC is performed using 8-taps interpolation filter for luma and 4-taps interpolation filter for chroma.

● MV refinement

MV refinement is a pattern based MV search with the criterion of bilateral matching cost or template matching cost. In the JEM, two search patterns are supported –an unrestricted center-biased diamond search (UCBDS) and an adaptive cross search for MV refinement at the CU level and sub-CU level, respectively. For both CU and sub-CU level MV refinement, the MV is directly searched at quarter luma sample MV accuracy, and this is followed by one-eighth luma sample MV refinement. The search range of MV refinement for the CU and sub-CU step are set equal to 8 luma samples.

● Selection of prediction direction in template matching FRUC merge mode

In the bilateral matching merge mode, bi-prediction is always applied since the motion information of a CU is derived based on the closest match between two blocks along the motion trajectory of the current CU in two different reference pictures. There is no such limitation for the template matching merge mode. In the template matching merge mode, the encoder can choose among uni-prediction from list0, uni-prediction from list1 or bi-prediction for a CU. The selection is based on a template matching cost as follows:

If costBi <= factor *min (cost0, cost1)

bi-prediction is used;

Otherwise, if cost0 <= cost1

uni-prediction from list0 is used;

Otherwise,

uni-prediction from list1 is used;

where cost0 is the SAD of list0 template matching, cost1 is the SAD of list1 template matching and costBi is the SAD of bi-prediction template matching. The value of factor is equal to 1.25, which means that the selection process is biased toward bi-prediction.

The inter prediction direction selection is only applied to the CU-level template matching process.

2.2 Hybrid intra and inter prediction

In JVET-L0100, multi-hypothesis prediction is proposed, wherein hybrid intra and inter prediction is one way to generate multiple hypotheses.

When the multi-hypothesis prediction is applied to improve intra mode, multi-hypothesis prediction combines one intra prediction and one merge indexed prediction. In a merge CU, one flag is signaled for merge mode to select an intra mode from an intra candidate list when the flag is true. For luma component, the intra candidate list is derived from 4intra prediction modes including DC, planar, horizontal, and vertical modes, and the size of the intra candidate list can be 3 or 4 depending on the block shape. When the CU width is larger than the double of CU height, horizontal mode is exclusive of the intra mode list and when the CU height is larger than the double of CU width, vertical mode is removed from the intra mode list. One intra prediction mode selected by the intra mode index and one merge indexed prediction selected by the merge index are combined using weighted average. For chroma component, DM is always applied without extra signaling. The weights for combining predictions are described as follow. When DC or planar mode is selected, or the CB width or height is smaller than 4, equal weights are applied. For those CBs with CB width and height larger than or equal to 4, when horizontal/vertical mode is selected, one CB is first vertically/horizontally split into four equal-area regions. Each weight set, denoted as (w_intra _i, w_inter _i) , where i is from 1 to 4 and (w_intra ₁, w_inter ₁) = (6, 2) , (w_intra ₂, w_inter ₂) = (5, 3) , (w_intra ₃, w_inter ₃) = (3, 5) , and (w_intra ₄, w_inter ₄) = (2, 6) , will be applied to a corresponding region. (w_intra ₁, w_inter ₁) is for the region closest to the reference samples and (w_intra ₄, w_inter ₄) is for the region farthest away from the reference samples. Then, the combined prediction can be calculated by summing up the two weighted predictions and right-shifting 3 bits. Moreover, the intra prediction mode for the intra hypothesis of predictors can be saved for reference of the following neighboring CUs.

2.3 Bi-directional optical flow

In BIO, motion compensation is first performed to generate the first predictions (in each prediction direction) of the current block. The first predictions are used to derive the spatial gradient, the temporal gradient and the optical flow of each subblock/pixel within the block, which are then used to generate the second prediction, i.e., the final prediction of the subblock/pixel. The details are described as follows.

Bi-directional Optical flow (BIO) is sample-wise motion refinement which is performed on top of block-wise motion compensation for bi-prediction. The sample-level motion refinement doesn’t use signalling.

Let I ^(k) be the luma value from reference k (k=0, 1) after block motion compensation, and

are horizontal and vertical components of the I ^(k) gradient, respectively. Assuming the optical flow is valid, the motion vector field (v _x, v _y) is given by theequation:

Combining this optical flow equation with Hermite interpolation for the motion trajectory of each sample results in a unique third-order polynomial that matches both the function values I ^(k) and derivatives

at the ends. The value of this polynomial at t=0 is the BIO prediction:

Here, τ ₀ and τ ₁ denote the distances to the reference frames as shown on FIG. 4. Distances τ ₀ and τ ₁ are calculated based on POC for Ref0 and Ref1: τ ₀=POC (current) -POC (Ref0) , τ ₁= POC (Ref1) -POC (current) . If both predictions come from the same time direction (either both from the past or both from the future) then the signs are different (i.e., τ ₀·τ ₁＜0) . In this case, BIO is applied only if the prediction is not from the same time moment (i.e., τ ₀≠τ ₁) , both referenced regions have non-zero motion (MVx ₀, MVy ₀, MVx ₁, MVy ₁≠0) and the block motion vectors are proportional to the time distance (MVx ₀/MVx ₁=MVy ₀/MVy ₁=-τ ₀/τ ₁) .

The motion vector field (v _x, v _y) is determined by minimizing the difference Δ between values in points A and B (intersection of motion trajectory and reference frame planes on FIG. 9) . Model uses only first linear term of a local Taylor expansion for Δ as:

All values in Equation 5 depend on the sample location (i′, j′) , which was omitted from the notation so far. Assuming the motion is consistent in the local surrounding area, the value of Δ can be minimized inside the (2M+1) × (2M+1) square window Ω centered on the currently predicted point (i, j) , where M is equal to 2:

For this optimization problem, the JEM uses a simplified approach making first a minimization in the vertical direction and then in the horizontal direction. This results in

where,

In order to avoid division by zero or a very small value, regularization parameters r and m are introduced in Equations 7 and 8where:

r=500·4 ^d-8 (10)

m=700·4 ^d-8 (11)

Here d is bit depth of the video samples.

In order to keep the memory access for BIO the same as for regular bi-predictive motion compensation, all prediction and gradients values, I ^(k) ,

are calculated only for positions inside the current block. In Equation 9, (2M+1) × (2M+1) square window Ω centered in currently predicted point on a boundary of predicted block can access positions outside of the block (as shown in FIG. 5 (a) ) . In the JEM, values of I ^(k) ,

outside of the block are set to be equal to the nearest available value inside the block. For example, this can be implemented as padding, as shown in FIG. 5 (b) .

With BIO, it’s possible that the motion field can be refined for each sample. To reduce the computational complexity, a block-based design of BIO is used in the JEM. The motion refinement is calculated based on 4×4 block. In the block-based BIO, the values of s _n in Equation 9 of all samples in a 4×4 block are aggregated, and then the aggregated values of s _n in are used to derived BIO motion vectors offset for the 4×4 block. More specifically, the following formula is used for block-based BIO derivation:

where b _k denotes the set of samples belonging to the k-th 4x4 block of the predicted block. s _n in Equations 7 and 8 are replaced by ( (s _n, bk) >>4) to derive the associated motion vector offsets.

In some cases, MV regiment of BIO might be unreliable due to noise or irregular motion. Therefore, in BIO, the magnitude of MV regiment is clipped to a threshold value thBIO. The threshold value is determined based on whether the reference pictures of the current picture are all from one direction. If all the reference pictures of the current picture are from one direction, the value of the threshold is set to 12×2 ^14-d; otherwise, it is set to 12×2 ^13-d.

Gradients for BIO are calculated at the same time with motion compensation interpolation using operations consistent with HEVC motion compensation process (2D separable FIR) . The input for this 2D separable FIR is the same reference frame sample as for motion compensation process and fractional position (fracX, fracY) according to the fractional part of block motion vector. In case of horizontal gradient

signal first interpolated vertically using BIOfilterS corresponding to the fractional position fracY with de-scaling shift d-8, then gradient filter BIOfilterG is applied in horizontal direction corresponding to the fractional position fracX with de-scaling shift by 18-d. In case of vertical gradient

first gradient filter is applied vertically using BIOfilterG corresponding to the fractional position fracY with de-scaling shift d-8, then signal displacement is performed using BIOfilterS in horizontal direction corresponding to the fractional position fracX with de-scaling shift by 18-d. The length of interpolation filter for gradients calculation BIOfilterG and signal displacement BIOfilterF is shorter (6-tap) in order to maintain reasonable complexity. Table 1 shows the filters used for gradients calculation for different fractional positions of block motion vector in BIO. Table 2 shows the interpolation filters used for prediction signal generation in BIO.

Table 1: Filters for gradients calculation in BIO

Fractional pel position	Interpolation filter for gradient (BIOfilterG)
0	{8, -39, -3, 46, -17, 5}
1/16	{8, -32, -13, 50, -18, 5}
1/8	{7, -27, -20, 54, -19, 5}
3/16	{6, -21, -29, 57, -18, 5}
1/4	{4, -17, -36, 60, -15, 4}
5/16	{3, -9, -44, 61, -15, 4}
3/8	{1, -4, -48, 61, -13, 3}
7/16	{0, 1, -54, 60, -9, 2}
1/2	{-1, 4, -57, 57, -4, 1}

Table 2: Interpolation filters for prediction signal generation in BIO

Fractional pel position	Interpolation filter for prediction signal (BIOfilterS)
0	{0, 0, 64, 0, 0, 0}
1/16	{1, -3, 64, 4, -2, 0}
1/8	{1, -6, 62, 9, -3, 1}
3/16	{2, -8, 60, 14, -5, 1}
1/4	{2, -9, 57, 19, -7, 2}
5/16	{3, -10, 53, 24, -8, 2}
3/8	{3, -11, 50, 29, -9, 2}
7/16	{3, -11, 44, 35, -10, 3}
1/2	{3, -10, 35, 44, -11, 3}

In the JEM, BIO is applied to all bi-predicted blocks when the two predictions are from different reference pictures. When LIC is enabled for a CU, BIO is disabled.

In the JEM, OBMC is applied for a block after normal MC process. To reduce the computational complexity, BIO is not applied during the OBMC process. This means that BIO is only applied in the MC process for a block when using its own MV and is not applied in the MC process when the MV of a neighboring block is used during the OBMC process.

A two-stage early termination method is used to conditionally disable the BIO operations depending on the similarity between the two prediction signals. The early termination is first applied at the CU-level and then at the sub-CU-level. Specifically, the proposed method first calculates the SAD between the L0 and L1 prediction signals at the CU level. Given that the BIO is only applied to luma, only the luma samples can be considered for the SAD calculation. If the CU-level SAD is no larger than a predefined threshold, the BIO process is completely disabled for the whole CU. The CU-level threshold is set to 2 ^(BDepth-9) per sample. If the BIO process is not disabled at the CU level, and if the current CU includes multiple sub-CUs, the SAD of each sub-CU inside the CU will be calculated. Then, the decision on whether to enable or disable the BIO process is made at the sub-CU-level based on a predefined sub-CU-level SAD threshold, which is set to 3*2 ^(BDepth-10) per sample.

2.4 Specification for BDOF in VVC

Specification of BDOF (in JVET-N1001-v2) is as follows:

8.5.7.4 Bidirectional optical flow prediction process

Inputs to this process are:

– two variables nCbW and nCbH specifying the width and the height of the current coding block,

– two (nCbW+2) x (nCbH+2) luma prediction sample arrays predSamplesL0 and predSamplesL1,

– the prediction list utilization flags predFlagL0 and predFlagL1,

– the reference indices refIdxL0 and refIdxL1,

– the bidirectional optical flow utilization flags bdofUtilizationFlag [xIdx] [yIdx] with xIdx=0.. (nCbW>>2) -1, yIdx=0.. (nCbH>>2) -1.

Output of this process is the (nCbW) x (nCbH) array pbSamples of luma prediction sample values.

Variables bitDepth, shift1, shift2, shift3, shift4, offset4, and mvRefineThres are derived as follows:

– The variable bitDepth is set equal to BitDepth _Y.

– The variable shift1 is set to equal to Max (2, 14-bitDepth) .

– The variable shift2 is set to equal to Max (8, bitDepth-4) .

– The variable shift3 is set to equal to Max (5, bitDepth-7) .

– The variable shift4 is set equal to Max (3, 15-bitDepth) and the variable offset4is set equal to 1<< (shift4-1) .

– The variable mvRefineThres is set equal to Max (2, 1<< (13-bitDepth) ) .

For xIdx=0.. (nCbW>>2) -1 and yIdx=0.. (nCbH>>2) -1, the following applies:

– The variable xSb is set equal to (xIdx<<2) +1 and ySb is set equal to (yIdx<<2) +1.

– If bdofUtilizationFlag [xSbIdx] [yIdx] is equal to FALSE, for x=xSb-1.. xSb+2, y=ySb-1.. ySb+2, the prediction sample values of the current subblock are derived as follows:

pbSamples [x] [y] = Clip3 (0, (2 ^bitDepth) -1, (predSamplesL0 [x+1] [y+1] +offset2+ (8-852)

predSamplesL1 [x+1] [y+1] ) >>shift2)

– Otherwise (bdofUtilizationFlag [xSbIdx] [yIdx] is equal to TRUE) , the prediction sample values of the current subblock are derived as follows:

– For x=xSb-1.. xSb+4, y=ySb-1.. ySb+4, the following ordered steps apply:

1. The locations (h _x, v _y) for each of the corresponding sample locations (x, y) inside the prediction sample arrays are derived as follows:

h _x=Clip3 (1, nCbW, x) (8-853)

v _y=Clip3 (1, nCbH, y) (8-854)

2.T he variables gradientHL0 [x] [y] , gradientVL0 [x] [y] , gradientHL1 [x] [y] and gradientVL1 [x] [y] are derived as follows:

gradientHL0 [x] [y] = (predSamplesL0 [h _x+1] [v _y] -predSampleL0 [h _x-1] [v _y] ) > >shift1 (8-855)

gradientVL0 [x] [y] = (predSampleL0 [h _x] [v _y+1] -predSampleL0 [h _x] [v _y-1] ) > >shift1 (8-856)

gradientHL1 [x] [y] = (predSamplesL1 [h _x+1] [v _y] -predSampleL1 [h _x-1] [v _y] ) > >shift1 (8-857)

gradientVL1 [x] [y] = (predSampleL1 [h _x] [v _y+1] -predSampleL1 [h _x] [v _y-1] ) > >shift1 (8-858)

3. The variables temp [x] [y] , tempH [x] [y] and tempV [x] [y] are derived as follows:

diff [x] [y] = (predSamplesL0 [h _x] [v _y] >>shift2) - (predSamplesL1 [h _x] [v _y] >>shif t2) (8-859)

tempH [x] [y] = (gradientHL0 [x] [y] +gradientHL1 [x] [y] ) >>shift3 (8-860)

tempV [x] [y] = (gradientVL0 [x] [y] +gradientVL1 [x] [y] ) >>shift3 (8-861)

– The variables sGx2, sGy2, sGxGy, sGxdI and sGydI are derived as follows:

sGx2=Σ _iΣ _j (tempH [xSb+i] [ySb+j] *tempH [xSb+i] [ySb+j] ) with i, j=-1.. 4 (8-862)

sGy2=Σ _iΣ _j (tempV [xSb+i] [ySb+j] *tempV [xSb+i] [ySb+j] ) with i, j=-1.. 4 (8-863)

sGxGy=Σ _iΣ _j (tempH [xSb+i] [ySb+j] *tempV [xSb+i] [ySb+j] ) with i, j-1.. 4 (8-864)

sGxdI=Σ _iΣ _j (-tempH [xSb+i] [ySb+j] *diff [xSb+i] [ySb+j] ) with i, j=-1.. 4 (8-865)

sGydI=Σ _iΣ _j (-tempV [xSb+i] [ySb+j] *diff [xSb+i] [ySb+j] ) with i, j=-1.. 4 (8-866)

– The horizontal and vertical motion offset of the current subblock are derived as:

v _x=sGx2>0? Clip3 (-mvRefineThres, mvRefineThres, (8-867)

- (sGxdI<<3) >>Floor (Log2 (sGx2) ) ) : 0

v _y=sGy2>0? Clip3 (-mvRefineThres, mvRefineThres, ( (sGydI<<3) - (8-868)

( (v _x*sGxGy _m) <<12+v _x*sGxGy _s) >>1) >>Floor (Log2 (sGx2) ) ): 0

– For x=xSb-1.. xSb+2, y=ySb-1.. ySb+2, the prediction sample values of the current sub-block are derived as follows:

bdofOffset=Round ( (v _x* (gradientHL1 [x+1] [y+1] -gradientHL0 [x+1] [y+1] ) ) >>1) (8-869)

+Round ( (v _y* (gradientVL1 [x+1] [y+1] -gradientVL0 [x+1] [y+1] ) ) >>1)

[Ed. (JC) : Round () operation is defined for float input. The Round () operation seems redundant here since the input is an integer value. To be confirmed by the proponent]

pbSamples [x] [y] =Clip3 (0, (2 ^bitDepth) -1, (predSamplesL0 [x+1] [y+1] +offset4+ (8-870)

predSamplesL1 [x+1] [y+1] +bdofOffset) >>shift4)

The spatial gradient is calculated as follows:

On the other hand, temporal gradient is calculated as follows:

Thus, the calculation of spatial gradient and temporal gradient is not aligned.

2.5 Decoder-side motion vector refinement

In bi-prediction operation, for the prediction of one block region, two prediction blocks, formed using a motion vector (MV) of list0 and a MV of list1, respectively, are combined to form a single prediction signal. In JVET-K0217, the decoder-side motion vector refinement (DMVR) method, the two motion vectors of the bi-prediction are further refined by a bilateral matching process.

In the proposed method DMVR is applied only in Merge and Skip modes, if the following condition is true:

(POC -POC0) * (POC -POC1) <0,

where POC is the picture order count of current to be encoded picture, POC0 and POC1 are picture order counts of the references for the current picture.

The signaled merge candidate pair is used as input to DMVR process and are denoted initial motion vectors (MV0, MV1) . The search points that are searched by DMVR obey the motion vector difference mirroring condition. In other words any point that is checked by DMVR, denoted by candidate motion vector pair (MV0′, MV1′) obey the following two equations:

MV0′=MV0+MV _diff

MV1′=MV1-MV _diff

where MV _diff represents the points in the search space in one of the reference pictures.

After the construction of the search space the uni-lateral predictions are constructed using regular 8-tap DCTIF interpolation filter. Bilateral matching cost function is calculated by using MRSAD (mean removed sum of absolute differences) between the two predictions (FIG. 6) and the search point resulting in the minimum cost is selected as the refined MV pair. For the MRSAD calculation 16 bit precision of samples is used (which is the output of the interpolation filtering) , and no clipping and no rounding operations are applied before MRSAD calculation. The reason for not applying rounding and clipping is to reduce internal buffer requirement.

FIG. 6 shows an example of bilateral matching with 6 points search.

In the proposed method the integer precision search points are chosen by the Adaptive pattern method. The cost, corresponding to the central points (pointed by the initial motion vectors) is calculated firstly. The other 4 costs (in sign shape) is calculated by the two predictions, located at the opposite sides of each other by the central point. Last 6 ^th point at the angle is chosen by the gradient of the previous calculated costs (FIG. 7) .

The output of the DMVR process is the refined motion vector pair corresponding to the minimal cost.

If after one iteration the minimum cost is achieved at the central point of the search space, i.e. the motion vectors are not changed, and the refinement process is terminated. Otherwise, the best cost further is regarded as center, and the process continues, while the minimal cost does not correspond to the central point and the search range is not exceeded.

Half sample precision search is applied only if application of half-pel search does not exceed the search range. In this case only 4 MRSAD calculations are performed, corresponding to plus shape points around the central one, which is chosen as the best during the integer precision search. At the end the refined motion vector pair is output that correspond to the minimal cost point.

Some simplifications and improvements are further proposed in JVET-L0163.

● Reference sampling padding

Reference sample padding is applied in order to extend the reference sample block that is pointed by the initial motion vector. If the size of the coding block are given by “w” and “h” , then it is assumed that a block of size w+7 and h+7 is retrieved from the reference picture buffer. The retrieved buffer is then extended by 2 samples in each direction by repetitive sample padding using the nearest sample. Afterwards the extended reference sample block is used to generate the final prediction once the refined motion vector is obtained (which can deviate from the initial motion vector 2 samples in each direction) .

It is noted that this modification eliminates the external memory access requirement of DMVR completely without any coding loss.

● Bilinear interpolation instead of 8-tap DCTIF

According to the proposal bilinear interpolation is applied during the DMVR search process, which means that the predictions used in MRSAD computation are generated using bilinear interpolation. Once the final refined motion vectors are obtained regular 8-tap DCTIF interpolation filter is applied to generate final predictions.

● Disabling of DMVR for small blocks

DMVR is disabled for blocks 4x4, 4x8 and 8x4.

● Early termination based on MV difference between merge candidates

An additional condition is imposed on DMVR to confine the MV refinement process. With it, DMVR is conditionally disabled when the below condition is satisfied.

The MV difference between the selected merge candidate and any of the previous ones in the same merge list is less than a pre-defined threshold (that is, 1.4-, 1/2-and 1-pixel-wide intervals for CUs with less than 64 pixels, less than 256 pixels and at least 256 pixels, respectively) .

● Early termination based on SAD cost at the center search coordinate

The sum of absolute difference (SAD) between the two prediction signals (L0 and L1 prediction) using the initial motion vectors of the current CU is calculated. If the SAD is no larger than a predefined threshold, i.e., 2 ^(BDepth-9) per sample, the DMVR is skipped; otherwise, the DMVR is still applied to refine the two motion vectors of the current block.

● DMVR application condition

The DMVR application condition is (POC –POC1) x (POC-POC2) <0 as it is implemented in BMS2.1 is replaced by the new condition (POC –POC1) == (POC2 –POC) . This means that DMVR is applied only if reference pictures are in opposite time directions and are equidistant to current picture.

● MRSAD computation using every second row

The MRSAD cost is computed only for odd numbered rows of a block, the even numbered samples rows are not considered. Accordingly, the number of operations for the MRSAD calculation is halved.

2.6 Related method

In the patent application identified by Application No. PCT/CN2018/098691 (which is incorporated by reference herein) , entitled “Motion Refinement for Visual Media Coding, ” filed August4, 2018, a MV update method and a two-step inter prediction method are proposed. The derived MV between reference block 0 and reference block 1 in BIO are scaled and added to the original motion vector of list 0 and list 1. Meanwhile, the updated MV is used to perform motion compensation and a second inter prediction is generated as the final prediction. The temporal gradient is modified by removing the mean difference between reference block 0 and reference block 1.

Further, in the patent application identified by Application No. PCT/CN2018/114931 (which is incorporated by reference herein) , entitled “Sub-block Based Prediction Methods and System, ” filed November 10, 2018, one set of MVs is generated for chroma component.

2.7 DMVR in VVC draft 4

The usage of DMVR in JVET-M1001_v7 (VVC working draft 4, version 7) is defined as follows:

– When all of the following conditions are true, dmvrFlag is set equal to 1:

– sps_dmvr_enabled_flag is equal to 1

– Current block is not coded with triangular prediction mode, AMVR affine mode, sub-block mode (including merge affine mode, and ATMVP mode)

– merge_flag [xCb] [yCb] is equal to 1

– both predFlagL0 [0] [0] and predFlagL1 [0] [0] are equal to 1

– mmvd_flag [xCb] [yCb] is equal to 0

– DiffPicOrderCnt (currPic, RefPicList [0] [refIdxL0] ) is equal to DiffPicOrderCnt (RefPicList [1] [refIdxL1] , currPic )

– cbHeight is greater than or equal to 8

– cbHeight*cbWidth is greater than or equal to 64

3. Examples of Problems Solved by Embodiments

In BIO, difference between two reference blocks or sub-blocks are calculated in the early termination stage, meanwhile, the temporal gradient is also calculated. Because the temporal gradient is actually the difference (or right shifted difference) between two reference pixels, calculating both the difference and the temporal gradient is not meaningful.

In DMVR, the MRSAD calculation is used to decide the refine motion vector of one block.

In BIO, the SAD calculation is used to decide whether BIO should be enabled/disabled for one block or one sub-block using all samples of one block/one sub-block which increases the computation complexity.

The calculation method is different for spatial gradient and temporal gradient.

4. Examples of embodiments

Denote SATD as sum of absolute transformed differences, MRSATD as mean removed sum of absolute transformed differences, and SSE as sum of squares error, and MRSSE as mean removed sum of squares error.

The detailed techniques below should be considered as examples to explain general concepts. These techniques should not be interpreted in a narrow way. Furthermore, these inventions can be combined in any manner.

In the following discussion, SatShift (x, n) is defined as

Shift (x, n) is defined as Shift (x, n) = (x+offset0) >>n.

In one example, offset0 and/or offset1 are set to (1<<n) >>1 or (1<< (n-1) ) . In another example, offset0 and/or offset1 are set to 0.

In another example, offset0=offset1= ( (1<<n) >>1) -1 or ( (1<< (n-1) ) ) -1.

In gradient calculation of BDOF, difference between two neighboring (either spatial neighboring or temporal neighboring) or/and non-adjacent samples may be calculated, and right-shift may be performed during the gradient calculation. Suppose the two neighboring samples are neig0 and neig1, and the right shift value is shift1, and the gradient to be calculated is grad. Note that shift1 may be different for spatial gradient and temporal gradient.

1. It is proposed to align the method used in calculating spatial gradient and temporal gradient.

a. In one example, gradient is calculated according to the shifted sample differences.

i. Alternatively, gradient is calculated according to the modified sample (e.g., via shifting) differences.

b. In one example, in gradient calculation, subtraction may be performed before right shift. E.g., grad = (neig0 –neig1) >> shift1.

c. In one example, in gradient calculation, subtraction may be performed after right shift. E.g., grad = (neig0 >> shift1) – (neig1 >> shift1) .

d. In one example, in gradient calculation, subtraction may be performed before right shift and an offset may be added before right shift. E.g., grad = (neig0 –neig1 + offset) >> shift1. The offset may be equal to 1 << (shift1 –1) or 1<<shift1>>1.

e. In one example, in gradient calculation, subtraction may be performed after right shift and an offset may be added before right shift. E.g., grad = ( (neig0 + offset) >> shift1) – ( (neig1 + offset) >> shift1) . The offset may be equal to 1 << (shift1 –1) or 1<<shift1>>1.

f. In one example, the gradient may be calculated as SatShift (neig0 –neig1, shift1) .

i. Alternatively, the gradient may be calculated as SatShift (neig0, shift1) -SatShift (neig1, shift1) .

2. It is proposed to use other criteria to decide the enabling/disabling of BIO or/and DMVR in the early termination stage, such as SATD or MRSATD or SSE or MRSSE or mean value difference or gradient values.

a. In one example, the block level and sub-block level enabling/disabling decisions may choose different rules, e.g., one with SAD and the other with SATD.

b. In one example, for a block/sub-block, if the gradient values (horizontal and/or vertical) or the averaged gradient values or the range of gradient values satisfy a condition, (e.g., larger than a threshold or outside a given range) , BIO and/or DMVR may be disabled.

c. It is proposed that the criteria used to decide the enabling/disabling BIO/DMVR may be signaled from the encoder to the decoder in VPS/SPS/PPS/slice header/tile group header .

3. It is proposed to use other criteria to decide the refined motion vector of one block in DMVR process, such as SATD or MRSATD or SSE or MRSSE to replace MRSAD.

a. In one example, the refined motion vector of one sub-block in DMVR process, such as SATD or MRSATD or SSE or MRSSE to replace MRSAD.

b. In one example, if SATD (or MRSATD) is applied, the whole block is split into M×N sub-blocks and SATD (or MRSATD) is calculated for each sub-block. The SATDs (or MRSATDs) for all or some of the sub-blocks are summed up to get the SATD (or MRSATD) value for the whole block.

4. BIO or/and DMVR may be disabled when mean value difference of two reference blocks of one block is larger than a threshold (T1) .

a. BIO may be disabled when mean value difference of two reference sub-blocks of one sub-block is larger than a threshold (T2) .

b. The thresholds T1 and/or T2 may be pre-defined.

c. The thresholds T1 and/or T2 may be dependent on the block dimension.

5. It is proposed that in the early termination stage of BIO, before calculating the difference (e.g., SAD/SATD/SSE etc. ) between the two reference blocks/sub-blocks, the reference blocks or/and sub-blocks may be first modified.

a. In one example, mean of the reference block or/and sub-block may be calculated and then subtracted by the reference block or/and sub-block.

b. In one example, methods disclosed in App. No. PCT/CN2018/096384, (which is incorporated by reference herein) , entitled “Motion Prediction Based on Updated Motion Vectors, ” filed on July 20, 2018, may be used to calculate the mean value of the reference block or/and sub-block, i.e., mean value is calculated for some representative positions.

6. It is proposed that in the early termination stage of BIO or/and DMVR, the difference (e.g., SAD/SATD/SSE/MRSAD/MRSATD/MRSSE etc. ) between the two reference blocks or/and sub-blocks may be calculated only for some representative positions.

a. In one example, only difference of even rows is calculated for the block or/and sub-block.

b. In one example, only difference of four corner samples of one block/sub-block is calculated for the block or/and sub-block.

c. In one example, the methods disclosed in U.S. Provisional Application No. 62/693,412, (which is incorporated by reference herein) entitled “Decoder Side Motion Vector Derivation in Video Coding, ” filed July 2, 2018, may be used to select the representative positions.

d. In one example, the difference (e.g., SAD/SATD/SSE/MRSAD/MRSATD/MRSSE etc. ) between the two reference blocks may be calculated only for some representative sub-blocks.

e. In one example, the difference (e.g., SAD/SATD/SSE/MRSAD/MRSATD/MRSSE etc. ) calculated for representative positions or sub-blocks are summed up to get the difference for the whole block/sub-block.

7. It is proposed that temporal gradient (temporal gradient at position (x, y) is defined as G (x, y) =P0 (x, y) -P1 (x, y) , where P0 (x, y) and P1 (x, y) represent the prediction at (x, y) from two different reference pictures) or modified temporal gradient is used as the difference (instead of SAD) in the early termination stage of BIO, and the threshold used in early termination may be adjusted accordingly.

a. In one example, absolute sum of the temporal gradients is calculated and used as the difference of the two reference blocks or/and sub-blocks.

b. In one example, absolute sum of the temporal gradients is calculated only on some representative positions for the block or/and sub-block.

8. It is proposed that the temporal gradient modification process may be performed adaptively for different blocks/sub-blocks.

a. In one example, the temporal gradient is modified only when the absolute mean difference (or SAD/SATD/SSE etc. ) between the two reference blocks is greater than a threshold T, for example, T = 4.

b. In one example, the temporal gradient is modified only when the absolute mean difference (or SAD/SATD/SSE etc. ) between the two reference blocks is less than a threshold T, for example, T = 20.

c. In one example, the temporal gradient is modified only when the absolute mean difference (or SAD/SATD/SSE etc. ) between the two reference blocks is in the range of [T1, T2] , for example, T1 = 4, T2 = 20.

d. In one example, if the absolute mean difference (or SAD/SATD/SSE etc. ) between the two reference blocks is greater than a threshold T (for example, T = 40) , BIO is disabled.

e. In one example, these thresholds may be predefined implicitly.

f. In one example, these thresholds may be signaled in SPS/PPS/picture/slice/tile level.

g. In one example, these thresholds may be different for different CU, LCU, slice, tile or picture.

i. In one example, these thresholds may be designed based on decoded/encoded pixel values.

ii. In one example, these thresholds may be designed differently for different reference pictures.

h. In one example, the temporal gradient is modified only when (absolute) mean of the two (or anyone of the two) reference blocks is greater than a threshold T, for example, T = 40.

i. In one example, the temporal gradient is modified only when (absolute) mean of the two (or anyone of the two) reference blocks is smaller than a threshold T, for example, T = 100.

j. In one example, the temporal gradient is modified only when (absolute) mean of the two (or anyone of the two) reference blocks are in the range of [T1, T2] , for example, T1 = 40, T2 = 100.

k. In one example, the temporal gradient is modified only when (absolute) mean of the two (or anyone of the two) reference blocks is greater/less than the absolute mean difference (or SAD/SATD etc. ) multiplied by T, in one example, T = 4.5.

l. In one example, the temporal gradient is modified only when (absolute) mean of the two (or anyone of the two) reference blocks is in the range of the absolute mean difference (or SAD/SATD etc. ) multiplied by [T1, T2] , in one example, T1 = 4.5, T2 = 7.

9. It is proposed that in hybrid intra and inter prediction mode, the two inter reference blocks may be modified when calculating the spatial gradients in BIO, or they may be modified before performing the entire BIO procedure.

a. In one example, the intra prediction block and the inter prediction block in each prediction direction are weighted averaged (using same weighting method as in hybrid inter and inter prediction) to generate two new prediction blocks, denoted as wAvgBlkL0 and wAvgBlkL1, which are used to derive the spatial gradients in BIO.

b. In one example, wAvgBlkL0 and wAvgBlkL1 are used to generate the prediction block of the current block, denoted as predBlk. Then, wAvgBlkL0, wAvgBlkL1 and predBlk are further used for the BIO procedure, and the refined prediction block generated in BIO is used as the final prediction block.

10. It is proposed that a DMVR or/and BIO flag may be signaled at block level to indicate whether DMVR or/and BIO is enabled for the block.

a. In one example, such flag may be signaled only for AMVP mode, and in merge mode, such flag may be inherited from spatial or/and temporal neighboring blocks.

b. In one example, whether BIO or/and DMVR is enabled or not may be decided jointly by the signaled flag and the on-the-fly decision (for example, the decision based on SAD in the early termination stage) . The signaled flag may indicate whether the on-the-fly decision is correct or not.

c. Such flag is not signaled for uni-predicted blocks.

d. Such flag may be not signaled for bi-predicted blocks whose two reference pictures are both preceding pictures or following pictures in display order.

e. Such flag may be not signaled for bi-predicted blocks if POC_diff (curPic, ref0) is not equal to POC_diff (ref1, curPic) , wherein POC_diff () calculates the POC difference between two pictures, and ref0 and ref1 are the reference pictures of current picture.

f. Such a flag is not signaled for intra coded blocks. Alternatively, furthermore, such a flag is not signaled for blocks coded with the hybrid intra and inter prediction mode.

Alternatively, such a flag is not signaled for current picture referencing block, i.e. the reference picture is the current picture.

g. Whether to signal the flag may depend on the block dimension. For example, if the block size is smaller than a threshold, such a flag is not signaled. Alternatively, if the block width and/or height is equal to or larger than a threshold, such a flag is not signaled.

h. Whether to signal the flag may depend on the motion vector precision. For example, if the motion vector is in integer precision, such a flag is not signaled.

i. If such a flag is not signaled, it may be derived to be true or false implicitly.

j. A flag may be signaled at slice header/tile header/PPS/SPS/VPS to indicate whether this method is enabled or not.

k. Such signaling method may depend on the temporal layer of the picture, for example, it may be disabled for picture with high temporal layer.

l. Such signaling method may depend on the QP of the picture, for example, it may be disabled for picture with high QP.

11. Instead of checking both block height and block size, it is proposed to decide whether to enable or disable DMVR according to the block height only.

a. In one example, DMVR may be enabled when the block height is greater than T1 (e.g., T1= 4) .

b. In one example, DMVR may be enabled when the block height is equal to or greater than T1 (e.g., T1= 8) .

12. The above methods which are applied to DMVR/BIO may be only applicable to other decoder-side motion vector derivation (DMVD) methods, such as prediction refinement based on optical flow for the affine mode.

a. In one example, the condition check for usage determination of DMVR and BIO may be aligned, such as whether block height satisfies same threshold.

i. In one example, DMVR and BIO may be enabled when the block height is equal to or greater than T1 (e.g., T1= 8) .

ii. In one example, DMVR and BIO may be enabled when the block height is greater than T1 (e.g., T1= 4) .

5. Embodiment

5.1 Embodiment #1

The usage of DMVR in JVET-M1001_v7 (VVC working draft 4, version 7) is modified as follows. The text in bolded, capitalized font will be deleted from JVET-M1001_v7:

– When all of the following conditions are true, dmvrFlag is set equal to 1:

– sps_dmvr_enabled_flag is equal to 1

– merge_flag [xCb] [yCb] is equal to 1

– both predFlagL0 [0] [0] and predFlagL1 [0] [0] are equal to 1

– mmvd_flag [xCb] [yCb] is equal to 0

– DiffPicOrderCnt (currPic, RefPicList [0] [refIdxL0] ) is equal to DiffPicOrderCnt (RefPicList [1] [refIdxL1] , currPic)

– cbHeight is greater than or equal to 8

– CBHEIGHT*CBWIDTH IS GREATER THAN OR EQUAL TO 64

That means, “cbHeight*cbWidth is greater than or equal to 64” is deleted.

5.2 Embodiment #2

The newly added parts are highlighted in bold face italics, and the deleted part are in bolded, capitalized font.

i. One example

8.5.7.4 Bidirectional optical flow prediction process

Inputs to this process are:

– the prediction list utilization flags predFlagL0 and predFlagL1,

– the reference indices refIdxL0 and refIdxL1,

– The variable bitDepth is set equal to BitDepth _Y.

– The variable shift1 is set to equal to Max (2, 14-bitDepth) .

– The variable shift2 is set to equal to Max (8, bitDepth-4) .

– The variable shift3 is set to equal to Max (5, bitDepth-7) .

– The variable mvRefineThres is set equal to Max (2, 1<< (13-bitDepth) ) .

For xIdx=0.. (nCbW>>2) -1 and yIdx=0.. (nCbH>>2) -1, the following applies:

predSamplesL1 [x+1] [y+1] ) >>shift2)

– For x=xSb-1.. xSb+4, y=ySb-1.. ySb+4, the following ordered steps apply:

4. The locations (h _x, v _y) for each of the corresponding sample locations (x, y) inside the prediction sample arrays are derived as follows:

h _x=Clip3 (1, nCbW, x) (8-853)

v _y=Clip3 (1, nCbH, y) (8-854)

5. The variables gradientHL0 [x] [y] , gradientVL0 [x] [y] , gradientHL1 [x] [y] and gradientVL1 [x] [y] are derived as follows:

6. The variables temp [x] [y] , tempH [x] [y] and tempV [x] [y] are derived as follows:

DIFF [X] [Y] = (PREDSAMPLESL0 [HX] [VY] >>SHIFT2) - (PREDSAMPLESL 1 [HX] [VY] >>SHIFT2) (8-859)

diff [x] [y] = (predSamplesL0 [h _x] [v _y] -predSamplesL1 [h _x] [v _y] ) >>shift2 (8-859)

tempH [x] [y] = (gradientHL0 [x] [y] +gradientHL1 [x] [y] ) >>shift3 (8-860)

tempV [x] [y] = (gradientVL0 [x] [y] +gradientVL1 [x] [y] ) >>shift3 (8-861)

– The variables sGx2, sGy2, sGxGy, sGxdI and sGydI are derived as follows:

v _x=sGx2>0? Clip3 (-mvRefineThres, mvRefineThres, (8-867)

-(sGxdI<<3) >>Floor (Log2 (sGx2) ) ) : 0

v _y=sGy2>0? Clip3 (-mvRefineThres, mvRefineThres, ( (sGydI<<3) - (8-868)

( (v _x*sGxGy _m) <<12+v _x*sGxGy _s) >>1) >>Floor (Log2 (sGx2) ) ): 0

+Round ( (v _y* (gradientVL1 [x+1] [y+1] -gradientVL0 [x+1] [y+1] ) ) >>1)

predSamplesL1 [x+1] [y+1] +bdofOffset) >>shift4)

ii. One example

8.5.7.4 Bidirectional optical flow prediction process

Inputs to this process are:

– the prediction list utilization flags predFlagL0 and predFlagL1,

– the reference indices refIdxL0 and refIdxL1,

– The variable bitDepth is set equal to BitDepth _Y.

– The variable shift1 is set to equal to Max (2, 14-bitDepth) .

– The variable shift2 is set to equal to Max (8, bitDepth-4) .

– The variable shift3 is set to equal to Max (5, bitDepth-7) .

– The variable shift4is set equal to Max (3, 15-bitDepth) and the variable offset4 is set equal to 1<< (shift4-1) .

– The variable mvRefineThres is set equal to Max (2, 1<< (13-bitDepth) ) .

For xIdx=0.. (nCbW>>2) -1 and yIdx=0.. (nCbH>>2) -1, the following applies:

predSamplesL1 [x+1] [y+1] ) >>shift2)

– For x=xSb-1.. xSb+4, y=ySb-1.. ySb+4, the following ordered steps apply:

7. The locations (h _x, v _y) for each of the corresponding sample locations (x, y) inside the prediction sample arrays are derived as follows:

h _x=Clip3 (1, nCbW, x) (8-853)

v _y=Clip3 (1, nCbH, y) (8-854)

8. The variables gradientHL0 [x] [y] , gradientVL0 [x] [y] , gradientHL1 [x] [y] and gradientVL1 [x] [y] are derived as follows:

GRADIENTHL0 [X] [Y] = (PREDSAMPLESL0 [HX+1] [VY] -PREDSAMPLEL0 [HX-1] [VY] ) >>SHIFT1 (8-855)

GRADIENTVL0 [X] [Y] = (PREDSAMPLEL0 [HX] [VY+1] -PREDSAMPLEL0 [HX] [VY-1] ) >>SHIFT1 (8-856)

GRADIENTHL1 [X] [Y] = (PREDSAMPLESL1 [HX+1] [VY] -PREDSAMPLEL1 [HX-1] [VY] ) >>SHIFT1 (8-857)

GRADIENTVL1 [X] [Y] = (PREDSAMPLEL1 [HX] [VY+1] -PREDSAMPLEL1 [HX] [VY-1] ) >>SHIFT1 (8-858)

gradientHL0 [x] [y] = (predSamplesL0 [h _x+1] [v _y] >>shift1) - (predSampleL0 [h _x -1] [v _y] >>shift1) (8-855)

gradientVL0 [x] [y] = (predSampleL0 [h _x] [v _y+1] >>shift1) – (predSampleL0 [h _x] [v _y-1] >>shift1) (8-856)

gradientHL1 [x] [y] = (predSamplesL1 [h _x+1] [v _y] >>shift1) – (predSampleL1 [h _x-1] [v _y] >>shift1) (8-857)

gradientVL1 [x] [y] = (predSampleL1 [h _x] [v _y+1] >>shift1) – (predSampleL1 [h _x] [v _y-1] >>shift1) (8-858)

9. The variables temp [x] [y] , tempH [x] [y] and tempV [x] [y] are derived as follows:

tempH [x] [y] = (gradientHL0 [x] [y] +gradientHL1 [x] [y] ) >>shift3 (8-860)

tempV [x] [y] = (gradientVL0 [x] [y] +gradientVL1 [x] [y] ) >>shift3 (8-861)

– The variables sGx2, sGy2, sGxGy, sGxdI and sGydI are derived as follows:

v _x=sGx2>0? Clip3 (-mvRefineThres, mvRefineThres, (8-867)

-(sGxdI<<3) >>Floor (Log2 (sGx2) ) ) : 0

v _y=sGy2>0? Clip3 (-mvRefineThres, mvRefineThres, ( (sGydI<<3) - (8-868)

( (v _x*sGxGy _m) <<12+v _x*sGxGy _s) >>1) >>Floor (Log2 (sGx2) ) ): 0

+Round ( (v _y* (gradientVL1 [x+1] [y+1] -gradientVL0 [x+1] [y+1] ) ) >>1)

predSamplesL1 [x+1] [y+1] +bdofOffset) >>shift4)

iii. One example

8.5.7.4 Bidirectional optical flow prediction process

Inputs to this process are:

– the prediction list utilization flags predFlagL0 and predFlagL1,

– the reference indices refIdxL0 and refIdxL1,

Variables bitDepth, shift1, shift2, shift3, shift4, offset4, offset5, offset6, and mvRefineThres are derived as follows:

– The variable bitDepth is set equal to BitDepth _Y.

– The variable shift1 is set to equal to Max (2, 14-bitDepth) .

– The variable shift2 is set to equal to Max (8, bitDepth-4) .

– The variable shift3 is set to equal to Max (5, bitDepth-7) .

– The variable shift4is set equal to Max (3, 15-bitDepth) and the variable offset4is set equal to 1<< (shift4-1) .

– The variable mvRefineThres is set equal to Max (2, 1<< (13-bitDepth) ) .

– The variable offset5 is set equal to (1<< (shift1–1) ) .

– The variable offset6 is set equal to (1<< (shift2–1) ) .

For xIdx=0.. (nCbW>>2) -1 and yIdx=0.. (nCbH>>2) -1, the following applies:

predSamplesL1 [x+1] [y+1] ) >>shift2)

– For x=xSb-1.. xSb+4, y=ySb-1.. ySb+4, the following ordered steps apply:

10. The locations (h _x, v _y) for each of the corresponding sample locations (x, y) inside the prediction sample arrays are derived as follows:

h _x=Clip3 (1, nCbW, x) (8-853)

v _y=Clip3 (1, nCbH, y) (8-854)

11. The variables gradientHL0 [x] [y] , gradientVL0 [x] [y] , gradientHL1 [x] [y] and gradientVL1 [x] [y] are derived as follows:

gradientHL0 [x] [y] = (predSamplesL0 [h _x+1] [v _y] -predSampleL0 [h _x-1] [v _y] +offset5) >>shift1 (8-855)

gradientVL0 [x] [y] = (predSampleL0 [h _x] [v _y+1] -predSampleL0 [h _x] [v _y-1] +offset5) >>shift1 (8-856)

gradientHL1 [x] [y] = (predSamplesL1 [h _x+1] [v _y] -predSampleL1 [h _x-1] [v _y] +offset5) >>shift1 (8-857)

gradientVL1 [x] [y] = (predSampleL1 [h _x] [v _y+1] -predSampleL1 [h _x] [v _y-1] +offset5) >>shift1 (8-858)

12. The variables temp [x] [y] , tempH [x] [y] and tempV [x] [y] are derived as follows:

diff [x] [y] = (predSamplesL0 [h _x] [v _y] -predSamplesL1 [h _x] [v _y] +offset6) >>shift2 (8-859)

tempH [x] [y] = (gradientHL0 [x] [y] +gradientHL1 [x] [y] ) >>shift3 (8-860)

tempV [x] [y] = (gradientVL0 [x] [y] +gradientVL1 [x] [y] ) >>shift3 (8-861)

– The variables sGx2, sGy2, sGxGy, sGxdI and sGydI are derived as follows:

v _x=sGx2>0? Clip3 (-mvRefineThres, mvRefineThres, (8-867)

-(sGxdI<<3) >>Floor (Log2 (sGx2) ) ) : 0

v _y=sGy2>0? Clip3 (-mvRefineThres, mvRefineThres, ( (sGydI<<3) - (8-868)

( (v _x*sGxGy _m) <<12+v _x*sGxGy _s) >>1) >>Floor (Log2 (sGx2) ) ): 0

+Round ( (v _y* (gradientVL1 [x+1] [y+1] -gradientVL0 [x+1] [y+1] ) ) >>1)

predSamplesL1 [x+1] [y+1] +bdofOffset) >>shift4)

FIG. 8 is a block diagram of a video processing apparatus 800. The apparatus 800 may be used to implement one or more of the methods described herein. The apparatus 800 may be embodied in a smartphone, tablet, computer, Internet of Things (IoT) receiver, and so on. The apparatus 800 may include one or more processors 802, one or more memories 804 and video processing hardware 806. The processor (s) 802 may be configured to implement one or more methods described in the present document. The memory (memories) 804 may be used for storing data and code used for implementing the methods and techniques described herein. The video processing hardware 806 may be used to implement, in hardware circuitry, some techniques described in the present document. The video processing hardware 806 may be partially or completely includes within the processor (s) 802 in the form of dedicated hardware, or graphical processor unit (GPU) or specialized signal processing blocks.

FIG. 10 is a flowchart for a method 1000 of processing a video. The method 1000 includes performing a determination (1005) of characteristics of a first video block, the characteristics including differences between reference blocks associated with the first video block, the differences including one or more of a sum of absolute transformed differences (SATD) , a mean removed sum of absolute transformed differences (MRSATD) , a sum of squares error (SSE) , a mean removed sum of squares error (MRSSE) , a mean value difference, or gradient values, determining (1010) an operational state of one or both of a bi-directional optical flow (BIO) technique or a decoder-side motion vector refinement (DMVR) technique based on the characteristics of the first video block, the operational state being one of enabled or disabled, and performing (1015) further processing of the first video block consistent with the operational state of one or both of the BIO technique or the DMVR technique.

FIG. 11 is a flowchart for a method 1100 of processing a video. The method 1100 includes modifying (1105) a first reference block to generate a first modified reference block, and a second reference block to generate a second modified reference block, the first reference block and the second reference block associated with a first video block, performing (1110) differences between the first modified reference block and the second modified reference block, the differences including one or more of a sum of absolute transformed differences (SATD) , a mean removed sum of absolute transformed differences (MRSATD) , a sum of squares error (SSE) , a mean removed sum of squares error (MRSSE) , a mean value difference, or gradient values, and performing (1115) further processing of the first video block based on the differences between the first modified reference block and the second modified reference block.

FIG. 12 is a flowchart for a method 1200 of processing a video. The method 1200 includes determining (1205) differences between a portion of a first reference block and a portion of a second reference block that are associated with a first video block, the differences including one or more of a sum of absolute transformed differences (SATD) , a mean removed sum of absolute transformed differences (MRSATD) , a sum of squares error (SSE) , a mean removed sum of squares error (MRSSE) , a mean value difference, or gradient values, and performing (1210) further processing of the first video block based on the differences.

FIG. 13 is a flowchart for a method 1300 of processing a video. The method 1300 includes determining (1305) a temporal gradient or a modified temporal gradient using reference pictures associated with a first video block, the temporal gradient or the modified temporal gradient indicative of differences between the reference pictures, and performing (1310) further processing of the first video block using a bi-directional optical flow (BIO) coding tool in accordance with the differences.

FIG. 14 is a flowchart for a method 1400 of processing a video. The method 1400 includes determining (1405) a temporal gradient using reference pictures associated with a first video block, modifying (1410) the temporal gradient to generate a modified temporal gradient, and performing (1415) further processing of the first video block using the modified temporal gradient.

FIG. 15 is a flowchart for a method 1500 of processing a video. The method 1500 includes modifying (1505) one or both of a first inter reference block and a second inter reference block associated with a first video block, determining (1510) a spatial gradient in accordance with a bi-directional optical flow coding tool (BIO) using one or both of the modified first inter reference block or the modified second inter reference block, and performing (1515) further processing of the first video block based on the spatial gradient.

FIG. 16 is a flowchart for a method 1600 of processing a video. The method 1600 includes performing (1605) a determination that a flag signaled at a block level indicates that one or both of a decoder-side motion vector refinement (DMVR) or a bi-directional optical flow (BIO) is to be enabled for a first video block, and performing (1610) further processing of the first video block, the processing including applying one or both of DMVR or BIO consistent with the flag.

With reference to methods1000, 1100, 1200, 1300, 1400, 1500, and 1600, some examples of determining use of bi-directional optical flow (BIO) or decoder-side motion vector refinement (DMVR) are described in Section 4 of the present document. For example, as described in Section 4, differences between reference blocks can be determined and the differences can be used to enable or disable BIO or DMVR.

With reference to

methods

1000, 1100, 1200, 1300, 1400, 1500, and 1600, a video block may be encoded in the video bitstream in which bit efficiency may be achieved by using a bitstream generation rule related to motion information prediction.

The methods can include wherein the operational state of the BIO technique or the DMVR technique is different between a block-level and a sub-block level.

The methods can include determining that one or more of the gradient values, an average of the gradient values, or a range of the gradient values are within a threshold range, wherein determining the operational state is based on the determination the gradient values, the average of the gradient values, or the range of the gradient values are within the threshold range.

The methods can include wherein determining the operational state is further based on information signaled from an encoder to a decoder in a video parameter set (VPS) , a sequence parameter set (SPS) , a picture parameter set (PPS) , a tile group header, or a slice header.

The methods can include determining a refined motion vector of the first video block based on the SATD, MRSATD, SSE, or MRSSE, and wherein performing further processing is based on the refined motion vector.

The methods can include wherein determining the refined motion vector is based on SATD or MRSATD, the method further comprising: determining SATD or MRSATD for each sub-block of the first video block; and generating SATD or MRSATD for the first video block based on a summation of the SATD or MRSATD for each sub-block, wherein further processing of the first video block is based on the generated SATD or MRSATD.

The methods can include determining that a mean value difference of two reference blocks of the first video block is larger than a threshold value, and wherein one or both of BIO or DMVR is in a disabled operational state based on the mean value difference of the two reference blocks.

The methods can include determining that a mean value difference of two reference sub-blocks of a sub-block of the first video block is larger than a threshold value, and wherein one or both of BIO or DMVR is in a disabled operational state based on the mean value difference of the two reference sub-blocks.

The methods can include wherein the threshold value is pre-defined.

The methods can include determining dimensions of the first video block, and wherein the threshold value is based on the dimensions of the first video block.

The methods can include wherein modifying the first reference block and the second reference block includes subtracting a mean of the first reference block from the first reference block.

The methods can include wherein the portions of the first reference block and the second reference block include even rows.

The methods can include wherein the portions of the first reference block and the second reference block include corner samples.

The methods can include wherein the portions of the first reference block and the second reference block include representative sub-blocks.

The methods can include wherein differences between the representative sub-blocks are summed to generate a difference for the first reference block or the second reference block.

The methods can include wherein the differences are related to an absolute sum of the temporal gradient.

The methods can include wherein modifying the temporal gradient is based on an absolute mean difference between the reference blocks being greater than a threshold value.

The methods can include wherein the threshold value is 4.

The methods can include wherein modifying the temporal gradient is based on an absolute mean difference between the reference blocks being less than a threshold value.

The methods can include wherein the threshold value is 20.

The methods can include wherein modifying the temporal gradient is based on an absolute mean difference between the reference blocks being within a threshold range.

The methods can include wherein BIO is in a disabled operational state based on the absolute mean difference being greater than a threshold value.

The methods can include wherein the threshold value or the threshold range is indicated in VPS, SPS, PPS, a picture, a slice, or a tile level.

The methods can include wherein the threshold value or the threshold range is different for different coding units (CUs) , largest coding units (LCUs) , slices, tiles, or pictures.

The methods can include wherein the threshold value or the threshold range is based on a decoded or encoded pixel value.

The methods can include wherein the threshold value or the threshold range is based on a reference picture.

The methods can include wherein determining the spatial gradient includes determining a weighted average of an intra prediction block and an inter prediction block in each prediction direction.

The methods can include wherein the flag is provided in advanced motion vector prediction (AMVP) mode, and in merge mode the flag is inherited from one or both of spatial neighboring blocks or temporal neighboring blocks.

The methods can include wherein the flag is not signaled for uni-predicted blocks.

The methods can include wherein the flag is not signaled for bi-predicted blocks with reference pictures that are preceding pictures or following pictures in display order.

The methods can include wherein the flag is not signaled for bi-predicted blocks.

The methods can include wherein the flag is not signaled for intra coded blocks.

The methods can include wherein the flag is not signaled for blocks coded with hybrid intra and inter prediction mode.

The methods can include wherein the flag is signaled based on a dimension of the first video block.

The methods can include wherein the flag is signaled in a VPS, a SPS, or a PPS.

The methods can include wherein the flag is based on a temporal layer of a picture associated with the first video block.

The methods can include wherein the flag is based on a quantization parameter (QP) of a picture associated with the first video block.

FIG. 17 is a block diagram showing an example video processing system 1700 in which various techniques disclosed herein may be implemented. Various implementations may include some or all of the components of the system 1700. The system 1700 may include input 1702 for receiving video content. The video content may be received in a raw or uncompressed format, e.g., 8 or 10 bit multi-component pixel values, or may be in a compressed or encoded format. The input 1702 may represent a network interface, a peripheral bus interface, or a storage interface. Examples of network interface include wired interfaces such as Ethernet, passive optical network (PON) , etc. and wireless interfaces such as Wi-Fi or cellular interfaces.

The system 1700 may include a coding component 1704 that may implement the various coding or encoding methods described in the present document. The coding component 1704 may reduce the average bitrate of video from the input 1702 to the output of the coding component 1704 to produce a coded representation of the video. The coding techniques are therefore sometimes called video compression or video transcoding techniques. The output of the coding component 1704 may be either stored, or transmitted via a communication connected, as represented by the component 1706. The stored or communicated bitstream (or coded) representation of the video received at the input 1702 may be used by the component 1708 for generating pixel values or displayable video that is sent to a display interface 1710. The process of generating user-viewable video from the bitstream representation is sometimes called video decompression. Furthermore, while certain video processing operations are referred to as “coding” operations or tools, it will be appreciated that the coding tools or operations are used at an encoder and corresponding decoding tools or operations that reverse the results of the coding will be performed by a decoder.

Examples of a peripheral bus interface or a display interface may include universal serial bus (USB) or high definition multimedia interface (HDMI) or Displayport, and so on. Examples of storage interfaces include SATA (serial advanced technology attachment) , PCI, IDE interface, and the like. The techniques described in the present document may be embodied in various electronic devices such as mobile phones, laptops, smartphones or other devices that are capable ofperforming digital data processing and/or video display.

It will be appreciated that the disclosed techniques may be embodied in video encoders or decoders to improve compression efficiency when the coding units being compressed have shaped that are significantly different than the traditional square shaped blocks or rectangular blocks that are half-square shaped. For example, new coding tools that use long or tall coding units such as 4x32 or 32x4 sized units may benefit from the disclosed techniques.

In some implementations, a method of video processing may be performed as follows:

using, during a conversion between a video block and a bitstream representation of the video block, a filtering method for calculating a spatial gradient and a temporal gradient, and

performing the conversion using the filtering.

Here, the conversion includes generating the bitstream representation from pixel values of the video block or generating the pixels values from the bitstream representation.

In some embodiments, the spatial and temporal gradients are calculated using shifted sample differences.

In some embodiments, the spatial and temporal gradients are calculated using modified samples.

Additional details of this method are provided in item 1 discussed in Section 4.

FIG. 18 is a flowchart for an example of a visual media processing method. Steps of this flowchart are discussed in connection with example embodiment 1 discussed in Section 4 of this document. At step 1802, the process performs a conversion between a current block of visual media data and a corresponding coded representation of the current video block, wherein the conversion of the current block includes computing a spatial gradient based on sample differences of spatially neighboring samples and/or a temporal gradient based on temporally neighboring samples associated with the current block, and wherein a technique used for computing the spatial gradient is same as a technique used for computing the temporal gradient.

FIG. 19 is a flowchart for an example of a visual media processing method. Steps of this flowchart are discussed in connection with example embodiment 1 discussed in Section 4 of this document. At step 1902, the process performs a conversion between a current block of visual media data and a corresponding coded representation of the current video block, wherein the conversion of the current block includes computing a spatial gradient based on computing sample differences of spatially neighboring samples associated with the current block, and wherein a left-shifting or a right-shifting of the sample differences of the spatially neighboring samples is applied prior to computing the sample differences.

Some embodiments of the present document are now presented in clause-based format.

1. A method of visual media processing, comprising:

performing a conversion between a current block of visual media data and a corresponding coded representation of the current video block,

wherein the conversion of the current block includes computing a spatial gradient based on sample differences of spatially neighboring samples and/or a temporal gradient based on temporally neighboring samples associated with the current block, and

wherein a technique used for computing the spatial gradient is same as a technique used for computing the temporal gradient.

2. The method of clause 1, wherein the technique used for computing the spatial gradient and/or the temporal gradient includes a left-shifting or a right-shifting of the sample differences of the spatially neighboring samples and/or the temporally neighboring samples.

3. The method of clause 2, wherein the spatially neighboring samples and/or the temporally neighboring samples are left-shifted or right-shifted prior to calculating the sample differences.

4. The method of clause 2, wherein the sample differences of the spatially neighboring samples and/or the temporally neighboring samples are computed prior to the left-shifting or the right-shifting of the sample differences.

5. The method of clause 4, wherein an offset is added to the sample differences prior to the left-shifting or the right-shifting of the sample differences.

6. The method of clause 3, wherein an offset is added to the spatially neighboring samples or the temporally neighboring samples prior to the left-shifting or the right-shifting.

7. The method of any one or more of clauses 5-6, wherein the offset is expressed as 1 << (shift1 –1) , wherein shift1 denotes an amount of the left-shifting or the right-shifting.

8. The method of any one or more of clauses 5-6, wherein the offset is expressed as 1<<shift1>>1, wherein shift1 denotes an amount of the left-shifting or the right-shifting.

9. The method of any one or more of clauses 1-8, wherein the spatial gradient and/or the temporal gradient is expressed as SatShift (neig0 –neig1, shift1) , wherein SatShift (x, n) is defined as

wherein offset0 and/or offset1 are set to (1<<n) >>1 and/or (1<< (n-1) ) and/or zero, wherein neig0 and neig1 denote sample values of two neighboring samples of a sample included in the current block of visual media data, and wherein shift1 denotes an amount of left-shifting or right-shifting of the two neighboring samples.

10. The method of any one or more of clauses 1-8, wherein the spatial gradient and/or the temporal gradient is expressed as SatShift (neig0, shift1) -SatShift (neig1, shift1) , wherein SatShift (x, n) is defined as

11. The method of any one or more of clauses 1-10, wherein the spatially neighboring samples are included in a prediction block of the current block corresponding to a prediction list.

12. The method of clause 11, wherein the prediction block is a first prediction block in a first prediction list and/or a second prediction block in a second prediction list.

13. The method of any one or more of clauses 1-10, wherein the temporal gradient is based on at least two temporal neighboring samples associated with the current block which are in a first prediction block of the current block corresponding to a first prediction list and in a second prediction block of the current block corresponding to a second prediction list.

14. The method of any one or more of clauses 1-9, wherein the spatial gradient is computed in a horizontal direction and/or a vertical direction.

15. The method of any one or more of clauses 1-13, wherein the conversion includes a use of a bi-directional optical flow (BDOF) technique.

16. A method of visual media processing, comprising:

wherein the conversion of the current block includes computing a spatial gradient based on computing sample differences of spatially neighboring samples associated with the current block, and

wherein a left-shifting or a right-shifting of the sample differences of the spatially neighboring samples is applied prior to computing the sample differences.

17. A video decoding apparatus comprising a processor configured to implement a method recited in one or more of clauses 1 to 16.

18. A video encoding apparatus comprising a processor configured to implement a method recited in one or more of clauses 1 to 16.

19. A computer program product having computer code stored thereon, the code, when executed by a processor, causes the processor to implement a method recited in any of clauses 1 to 16.

20. A method, apparatus or system described in the present document.

In the present document, the term “video processing” or “visual media processing” may refer to video encoding, video decoding, video compression or video decompression. For example, video compression algorithms may be applied during conversion from pixel representation of a video to a corresponding bitstream representation or vice versa. The bitstream representation of a current video block may, for example, correspond to bits that are either co-located or spread in different places within the bitstream, as is defined by the syntax. For example, a macroblock may be encoded in terms of transformed and coded error residual values and also using bits in headers and other fields in the bitstream. Furthermore, during conversion, a decoder may parse a bitstream with the knowledge that some fields may be present, or absent, based on the determination, as is described in the above solutions. Similarly, an encoder may determine that certain syntax fields are or are not to be included and generate the coded representation accordingly by including or excluding the syntax fields from the coded representation.

The disclosed and other solutions, examples, embodiments, modules and the functional operations described in this document can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this document and their structural equivalents, or in combinations of one or more of them. The disclosed and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more them. The term “data processing apparatus” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus.

A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document) , in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code) . A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

The processes and logic flows described in this document can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit) .

Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random-access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

While this patent document contains many specifics, these should not be construed as limitations on the scope of any subject matter or of what may be claimed, but rather as descriptions of features that may be specific to particular embodiments of particular techniques. Certain features that are described in this patent document in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Moreover, the separation of various system components in the embodiments described in this patent document should not be understood as requiring such separation in all embodiments.

Only a few implementations and examples are described and other implementations, enhancements and variations can be made based on what is described and illustrated in this patent document.

Claims

A method of visual media processing, comprising:

performing a conversion between a current block of visual media data and a corresponding coded representation of the current video block,

wherein the conversion of the current block includes computing a spatial gradient based on sample differences ofspatially neighboring samples and/or a temporal gradient based on temporally neighboring samples associated with the current block, and

wherein a technique used for computing the spatial gradient is same as a technique used for computing the temporal gradient.
The method of claim 1, wherein the technique used for computing the spatial gradient and/or the temporal gradient includes a left-shifting or a right-shifting of the sample differences of the spatially neighboring samples and/or the temporally neighboring samples.
The method of claim 2, wherein the spatially neighboring samples and/or the temporally neighboring samples are left-shifted or right-shifted prior to calculating the sample differences.
The method of claim 2, wherein the sample differences of the spatially neighboring samples and/or the temporally neighboring samples are computed prior to the left-shifting or the right-shifting of the sample differences.
The method of claim 4, wherein an offset is added to the sample differences prior to the left-shifting or the right-shifting of the sample differences.
The method of claim 3, wherein an offset is added to the spatially neighboring samples or the temporally neighboring samples prior to the left-shifting or the right-shifting.
The method of any one or more of claims 5-6, wherein the offset is expressed as 1 <<(shift1 –1) , wherein shift1 denotes an amount of the left-shifting or the right-shifting.
The method of any one or more of claims 5-6, wherein the offset is expressed as 1<<shift1>>1, wherein shift1 denotes an amount of the left-shifting or the right-shifting.
The method of any one or more of claims 1-8, wherein the spatial gradient and/or the temporal gradient is expressed as SatShift (neig0 –neig1, shift1) , wherein SatShift (x, n) is defined as

wherein offset0 and/or offset1 are set to (1<<n) >>1 and/or (1<< (n-1) ) and/or zero, wherein neig0 and neig1 denote sample values of two neighboring samples of a sample included in the current block of visual media data, and wherein shift1 denotes an amount of left-shifting or right-shifting of the two neighboring samples.
The method of any one or more of claims 1-8, wherein the spatial gradient and/or the temporal gradient is expressed as SatShift (neig0, shift1) -SatShift (neig1, shift1) , wherein SatShift (x, n) is defined as

wherein offset0 and/or offset1 are set to (1<<n) >>1 and/or (1<< (n-1) ) and/or zero, wherein neig0 and neig1 denote sample values of two neighboring samples of a sample included in the current block of visual media data, and wherein shift1 denotes an amount of left-shifting or right-shifting of the two neighboring samples.
The method of any one or more of claims 1-10, wherein the spatially neighboring samples are included in a prediction block of the current block corresponding to a prediction list.
The method of claim 11, wherein the prediction block is a first prediction block in a first prediction list and/or a second prediction block in a second prediction list.
The method of any one or more of claims 1-10, wherein the temporal gradient is based on at least two temporal neighboring samples associated with the current block which are in a first prediction block of the current block corresponding to a first prediction list and in a second prediction block of the current block corresponding to a second prediction list.
The method of any one or more of claims 1-9, wherein the spatial gradient is computed in a horizontal direction and/or a vertical direction.
The method of any one or more of claims 1-13, wherein the conversion includes a use of a bi-directional optical flow (BDOF) technique.
A method of visual media processing, comprising:

performing a conversion between a current block of visual media data and a corresponding coded representation of the current video block,

wherein the conversion of the current block includes computing a spatial gradient based on computing sample differences of spatially neighboring samples associated with the current block, and

wherein a left-shifting or a right-shifting of the sample differences of the spatially neighboring samples is applied prior to computing the sample differences.
A video decoding apparatus comprising a processor configured to implement a method recited in one or more of claims 1 to 16.
A video encoding apparatus comprising a processor configured to implement a method recited in one or more of claims 1 to 16.
A computer program product having computer code stored thereon, the code, when executed by a processor, causes the processor to implement a method recited in any of claims 1 to 16.
A method, apparatus or system described in the present document.