CN105744157A - Image pixel sampling value conversion method and device as well as sampling value processing method and device - Google Patents

Abstract

The present invention provides a method and device for image pixel sampling value conversion and sampling value processing, which are used to convert high bit depth pixel sampling values into low bit depth sampling values, including: dividing the value range of high bit depth pixel sampling values into one or more segments, and determine the metadata of the segment endpoints, wherein the metadata includes the high-bit-depth pixel sample values of the segment endpoints and the corresponding low-bit-depth sample values; according to the metadata data, determining the segment where the high-bit-depth pixel sampling value in the image is located; and calculating the low-bit-depth sampling value corresponding to the high-bit-depth pixel sampling value according to the metadata of the segmentation endpoint. By converting high-bit-depth pixel sampling values into low-bit-depth sampling values, image color distortion and detail loss in the conversion between HDR and standard dynamic range video are reduced.

Description

Method and device for image pixel sampling value conversion and sampling value processing

technical field

The invention belongs to the field of video processing, in particular to a method and device for image pixel sampling value conversion and sampling value processing.

Background technique

Traditional low dynamic range images and videos can only reflect a limited brightness range, while high dynamic range (HighDynamicRange, HDR) images and videos can reflect a larger brightness range, greatly expand contrast and color, and display real scenes more realistically .

In the processing of HDR images and videos, HDR images and videos need to be converted with standard dynamic range or low dynamic range videos to adapt to applications under different conditions and occasions.

In the process of realizing the present invention, the inventors found that in the mutual conversion between HDR and standard dynamic range video, if the traditional linear conversion method is used to convert the dynamic range of the image, there are problems such as image color distortion and loss of detail.

Contents of the invention

In order to solve the deficiencies in the prior art, the present invention proposes an image sampling value conversion method for converting high bit depth pixel sampling values into low bit depth sampling values, including: converting the high bit depth pixel sampling value dividing the range into one or more segments, determining metadata for segment endpoints, wherein the metadata includes high bit-depth pixel sample values and corresponding low bit-depth sample values for the segment endpoints; according to the According to the metadata, determine the segment where the high bit depth pixel sampling value in the image is located; according to the metadata of the segmentation endpoint, calculate the low bit depth sampling value corresponding to the high bit depth pixel sampling value; wherein, the bit depth is Indicates the number of binary sign bits used for the pixel sample value.

Preferably, the value range of the high bit depth pixel sampling value is divided into multiple segments, and determining the metadata of the segment endpoints includes: analyzing the image to determine the statistical histogram information of the image; according to the Statisticalizing the histogram information to determine the parameters of the adaptive quantization function; calculating the low bit depth sampling value corresponding to the high bit depth pixel sampling value of the end point of the segment according to the adaptive quantization function.

Preferably, the normalized cumulative histogram distribution function value corresponding to the high bit-depth pixel sampling value is calculated according to the statistical histogram information, and used as a parameter of the adaptive quantization function.

Preferably, the parameters of the adaptive quantization function also include adjustable numerical parameters.

Preferably, the calculating the low bit depth sampling value corresponding to the high bit depth pixel sampling value according to the metadata of the segmentation endpoint includes: setting the low bit depth sampling value as the segmentation endpoint Interpolation of the metadata.

Optionally, setting the low bit depth sampling value as an interpolation value of the metadata of the segmentation endpoint includes: calculating the slope value of the segmentation according to the metadata of the segmentation endpoint; calculating the high the change value of the bit-depth sample value relative to the high bit-depth sample value in the segment endpoint metadata; set the rate of change value to the product of the change value and the slope value; set the low bit-depth sample value corresponding to the high bit-depth sample value The bit-depth sample value is set to the sum or difference of the rate-of-change value and the low-bit-depth sample value in the segment endpoint metadata.

Optionally, after determining the data element, further comprising: encoding the metadata, and writing the encoded bits into at least one of the following data units in the code stream: parameter set, slice header information, auxiliary information Unit, a user-defined data unit, describes a sub-information unit.

Corresponding to the image sampling value conversion method, the present invention also provides an image sampling value processing method, which is used to convert low-bit-depth pixel sampling values into high-bit-depth sampling values, including: according to metadata, determine the Segment endpoint, which divides the value range of low-bit-depth pixel sampling values into one or more segments, wherein the metadata includes the low-bit-depth pixel sampling values of the segment endpoints and the corresponding high-bit-depth Sampling value; according to the metadata, determine the segment where the low bit depth pixel sampling value in the image is located; according to the metadata of the segmentation endpoint, calculate the high bit depth sampling value corresponding to the low bit depth pixel sampling value; wherein , the bit depth is the number of binary sign bits used to represent the pixel sample value.

Optionally, before determining the segmentation endpoint according to the metadata, it also includes: parsing the code stream, and obtaining the metadata from at least one of the following data units of the code stream, including: parameter set, fragment header information , an auxiliary information unit, a user-defined data unit, and a description sub-information unit.

Preferably, the calculating the high bit depth sampling value corresponding to the low bit depth pixel sampling value according to the metadata of the segmentation endpoint includes: setting the high bit depth sampling value as the segmentation endpoint Interpolation of the metadata.

Optionally, setting the high bit depth sampling value as an interpolation value of the metadata of the segment endpoint includes: calculating the slope value of the segment according to the metadata of the segment endpoint; calculating the low the change value of the bit depth sample value relative to the low bit depth sample value in the segment endpoint metadata; the rate of change value is set to the product of the change value and the slope value; the low bit depth sample value corresponding to The high bit depth sample value is set as the sum or difference of the rate of change value and the high bit depth sample value in the segment endpoint metadata.

The present invention also provides an image pixel sampling value conversion device, which is used to convert high bit depth pixel sampling values into low bit depth sampling values, comprising: a first determination unit, which divides the value range of high bit depth pixel sampling values into one or more segments, and determine the metadata of the segment endpoints, wherein the metadata includes the high bit-depth pixel sample values of the segment endpoints and the corresponding low bit-depth sample values; the second determining unit , according to the metadata, determine the segment where the high-bit-depth pixel sampling value in the image is located; the first calculation unit calculates the low-bit-depth sampling value corresponding to the high-bit-depth pixel sampling value according to the metadata of the segmentation endpoint ; Wherein, the bit depth is the number of binary sign bits used to represent the pixel sample value.

Optionally, the conversion device further includes: a writing unit, which encodes the metadata, and writes the encoded bits into at least one of the following data units in the code stream: parameter set, slice header information, auxiliary information Unit, a user-defined data unit, describes a sub-information unit.

The present invention also provides an image pixel sampling value processing device, which is used for converting low bit depth pixel sampling values into high bit depth sampling values, including: a third determining unit, determining segmentation endpoints according to metadata, converting low bit depth The value range of the depth pixel sample value is divided into one or more segments, wherein the metadata includes the low bit depth pixel sample value of the segment endpoint and the corresponding high bit depth sample value; the fourth determination The unit determines the segment where the low bit depth pixel sampling value in the image is located according to the metadata; the second calculation unit calculates the high bit depth corresponding to the low bit depth pixel sampling value according to the metadata of the segmentation endpoint A sample value; wherein, the bit depth is the number of binary sign bits used to represent the pixel sample value.

Optionally, the third determining unit further includes: a parsing unit that parses the code stream, and obtains the metadata from at least one of the following data units of the code stream, including: parameter set, slice header information, auxiliary Information unit, user-defined data unit, description sub-information unit.

The beneficial effects brought by the technical scheme provided by the invention are:

By converting high-bit-depth pixel sampling values into low-bit-depth sampling values, image color distortion and detail loss in the conversion between HDR and standard dynamic range video are reduced.

Description of drawings

In order to illustrate the technical solution of the present invention more clearly, the accompanying drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. Ordinary technicians can also obtain other drawings based on these drawings on the premise of not paying creative work.

Fig. 1 is a schematic flow chart of an image pixel sampling value conversion method provided by the present invention;

Fig. 2 is a schematic flow chart of a method for processing image sampling values provided by the present invention;

Fig. 3 is a schematic structural diagram of an image pixel sampling value conversion device provided by the present invention;

Fig. 4 is a schematic structural diagram of an image pixel sample value processing device provided by the present invention.

detailed description

In order to make the structure and advantages of the present invention clearer, the structure of the present invention will be further described below in conjunction with the accompanying drawings.

Embodiment one

The present invention provides a method for converting image pixel sampling values, which is used to convert high bit depth pixel sampling values into low bit depth sampling values, as shown in Figure 1, wherein the bit depth is used to represent the pixel sampling values Binary sign digits, the main steps include:

Step 11. Divide the value range of the high-bit-depth pixel sampling value into multiple segments, and determine the metadata of the segmentation endpoints, wherein the metadata includes the high-bit-depth pixel sampling values and the sum of the segmentation endpoints. The corresponding low bit depth sample value.

A method for determining the segmentation endpoint metadata is: analyzing the image to determine the statistical histogram information of the image; according to the statistical histogram information, determining the parameters of the adaptive quantization function; according to the adaptive quantization function, calculating The low-bit-depth sample value corresponding to the high-bit-depth pixel sample value at the end point of the segment. Wherein, according to the statistical histogram information, calculate the normalized cumulative histogram distribution function value corresponding to the high bit depth pixel sampling value, and use it as a parameter of the adaptive quantization function.

Step 12. According to the metadata, determine the segment where the sampling value of the high bit depth pixel in the image is located.

Step 13: Calculate the low bit depth sample value corresponding to the high bit depth pixel sample value according to the metadata of the segmentation endpoint.

One approach is to compute an interpolation of metadata for the segment endpoints from the high bit-depth pixel sample values, setting the low bit-depth sample values as the interpolated values. One way to calculate the difference is to: calculate the slope value of the segment based on the metadata of the segment endpoint; calculate the slope value of the high bit depth sample value relative to the high bit depth sample value in the segment endpoint metadata Change value; set the rate of change value as the product of the change value and the slope value; set the low bit depth sample value corresponding to the high bit depth sample value as the rate of change value and the segmentation endpoint element The sum or difference of the low bit depth sampled values in the data.

According to practical applications, such as video coding applications, when metadata information needs to be transmitted to the receiving end, the metadata is encoded, and the encoded bits are written into at least one of the following data units in the code stream: parameter set, slice header information, Auxiliary information unit, user-defined data unit, description sub-information unit.

In this embodiment, the method of the present invention is used to convert a 16-bit HDR video image into a 10-bit standard dynamic range video image.

In implementation, the three channels of Y`, Cb, and Cr of the 16bitHDR video image are processed independently, and converted into 10bit video images respectively according to the method of the present invention. Each channel is processed in the following specific ways:

1) Segment the value range (ie 0-65535) of the 16-bit HDR video image pixel sampling value, here it is evenly divided into 64 segments, each segment width is 1024, and 65 segmentation endpoints are recorded, namely 0, 1024, 2048, ..., 65535.

2) Statistical image histogram information.

The distribution information of pixel sampling values of the current channel image is counted, and the normalized cumulative histogram distribution function CDF(x) is obtained. The specific calculation method is as follows:

$\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; D.</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; x</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; P</span>}\mathrm{<span\; class="notranslate">\; D.</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; P</span>}\mathrm{<span\; class="notranslate">\; D.</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}$

Wherein, x is the 16bit pixel sampling value of the current image, PDF(l) is a histogram probability distribution function, N ₁ represents the number of pixels whose pixel sampling value is 1 in the current image, and N is the total number of pixels in the current image.

3) Take the CDF(x) obtained in 2) as a parameter, and set the numerical parameters a and b to determine the adaptive conversion function. The specific function form is:

$\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&times;</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; x</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; D.</span>}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}$

Wherein, R _max1 represents the maximum value of the dynamic range of high bit depth, which is 65535 in this embodiment, R _max2 represents the maximum value of the dynamic range of low bit depth, which is 1023 in this embodiment, x is the current 16bit image pixel sampling value, y is the 10bit image pixel sampling value corresponding to x.

4) According to the adaptive conversion function obtained in 3), calculate the 10-bit image pixel sampling value corresponding to the 16-bit pixel sampling value of each segment endpoint. And the 65 endpoints and their corresponding 10bit image pixel sampling values are saved as metadata, which can be used to reconstruct 16bit images from 10bit images.

5) According to the metadata obtained in 4), determine the segmentation endpoint of the segment where the 16-bit image pixel sampling value is located.

6) According to the metadata of the segmentation endpoint, an interpolation method is used to calculate the 10-bit pixel sampling value corresponding to the 16-bit pixel sampling value, so as to realize the conversion of the 16-bit image into a 10-bit image. One way to calculate the interpolation is:

$\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>$

Among them, x ₁ and x ₂ are the 16-bit pixel sampling values in the segment endpoint metadata, y ₁ and y ₂ are the 10-bit pixel sampling values corresponding to x ₁ and x ₂ in the metadata respectively, and x is at x ₁ The 16-bit pixel sampling value between x and x ₂ , and y is the 10-bit pixel sampling value corresponding to x. is the slope value of the segment, is the rate of change value.

In this embodiment, the metadata can also be encoded, and the encoded bits can be written into at least one of the following data units in the code stream: parameter set, slice header information, auxiliary information unit, user-defined data unit , describing the sub-information unit.

Embodiment two

Corresponding to the image sampling value conversion method, the present invention also provides an image sampling value processing method for converting low bit depth pixel sampling values into high bit depth sampling values, as shown in Figure 2, wherein, The bit depth is the number of binary sign bits used to represent the pixel sample value, and the main steps include:

Step 21. Determine segment endpoints according to the metadata, and divide the value range of the low-bit-depth pixel sample values into multiple segments, wherein the metadata includes the low-bit-depth pixel sample values and The corresponding high bit depth sample value.

Step 22, according to the metadata, determine the segment where the low bit depth pixel sampling value in the image is located;

Step 23. According to the metadata of the segmentation endpoint, calculate the high bit depth sample value corresponding to the low bit depth pixel sample value;

One approach is to compute an interpolation of metadata for the segment endpoints based on the low bit-depth pixel sample values, setting the high bit-depth sample values as the interpolated values. One method of calculating the difference is: calculating the slope value of the segment according to the metadata of the segment endpoint; calculating the relative value of the low bit depth sample value in the metadata of the segment endpoint set the rate of change value as the product of the change value and the slope value; set the high bit depth sample value corresponding to the low bit depth sample value as the rate of change value and the segmentation endpoint The sum or difference of the high bit depth sample values in the metadata.

According to an actual application, such as a video decoding application, when metadata needs to be obtained from a code stream, the code stream is parsed, and the metadata is obtained from at least one of the following data units of the code stream, including: a parameter set, Fragment header information, auxiliary information unit, user-defined data unit, and description sub-information unit.

This embodiment uses the method of the present invention to realize 10bit standard dynamic range video image conversion 16bitHDR video image

During the processing, the three channels of the 10bit video image are processed independently, and are respectively converted into 16bit video images according to the method of the present invention. Each channel is processed as follows:

1) According to the metadata, determine the end point of each segment. This example uses 64 segments.

2) According to the metadata, determine the segment endpoint of the segment where the 10-bit image pixel sampling value is located.

3) According to the metadata of the segmentation endpoint, the interpolation method is used to calculate the 16-bit pixel sampling value corresponding to the 10-bit pixel sampling value, so as to realize the conversion of the 10-bit image into a 16-bit image. One way to calculate the difference is:

$\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>$

Among them, x ₁ and x ₂ are the 16-bit pixel sampling values in the segment endpoint metadata, y ₁ and y ₂ are the 10-bit pixel sampling values corresponding to x ₁ and x ₂ in the metadata respectively, and x is at x ₁ The 16-bit pixel sampling value between x and x ₂ , and y is the 10-bit pixel sampling value corresponding to x. is the slope value of the segment, is the rate of change value.

In this embodiment, before determining the segmentation endpoint, the metadata may be obtained by parsing the code stream, including: parsing the code stream, and obtaining the metadata from at least one of the following data units of the code stream , including: parameter set, slice header information, auxiliary information unit, user-defined data unit, and description sub-information unit.

Embodiment Three

The present invention also provides an image pixel sample value conversion device 3, which is used to convert a high bit depth pixel sample value into a low bit depth sample value, as shown in FIG. 3, wherein the bit depth represents the pixel sample value The number of binary sign bits used for the value. The device contains the following modules:

The first determination unit 31 is input with high-bit image data, divides the value range of the input high-bit-depth image pixel sampling value into a plurality of segments, and determines the metadata of the segment endpoints, wherein the metadata includes all The high-bit-depth pixel sampling value of the segmentation endpoint and the corresponding low-bit-depth sampling value; output the metadata of each segmentation endpoint to the input terminal of the second determination unit 32 .

The second determination unit 32, the input terminal is connected to the output terminal of the first determination unit 31, according to the input metadata, determine the segment where the high bit depth pixel sampling value in the image is located, and output the metadata of the two endpoints of the segment to the second determination unit 32 An input terminal of the calculation unit 33 .

The first calculation unit 33, the input end of which is connected to the output end of the second determination unit 32, calculates the low bit depth sampling value corresponding to the high bit depth pixel sampling value according to the metadata of the input segmentation endpoint, and the obtained low bit depth Depth sampled value output.

Optionally, the conversion device also includes:

The writing unit 34 encodes the metadata, and writes the encoded bits into at least one of the following data units in the code stream: parameter set, slice header information, auxiliary information unit, user-defined data unit, descriptor information unit.

Embodiment Four

The present invention also provides an image pixel sampling value processing device 4, which is used to convert low bit depth pixel sampling values into high bit depth sampling values, as shown in Fig. 4, wherein the bit depth represents the pixel sampling value The number of signed bits in binary used by the value, the device contains the following modules:

The third determining unit 41 obtains metadata according to the input data, and determines segment endpoints according to the metadata, and divides the value range of the low-bit-depth pixel sampling value into a plurality of segments, wherein the metadata includes the segment The low-bit-depth pixel sample value of the segment endpoint and the corresponding high-bit-depth sample value; output the segment endpoint metadata to the input terminal of the fourth determination unit 42 .

The fourth determination unit 42, the input terminal is connected to the output terminal of the third determination unit 41, according to the input segment endpoint metadata, determine the segment where the low bit depth pixel sampling value in the image is located, and divide the metadata of the two endpoints of the segment into The data are output to the input of the second computing unit 43 .

The second calculation unit 43, the input terminal is connected to the output terminal of the fourth determination unit 42, and calculates the high bit depth sampling value corresponding to the low bit depth pixel sampling value according to the input metadata of the two endpoints of the segment, and the obtained High bit depth sampled value output.

Optionally, the third determination unit 41 further includes:

The parsing unit 44, parsing the code stream, obtains the metadata from at least one of the following data units of the code stream, including: parameter set, slice header information, auxiliary information unit, user-defined data unit, and description sub-information unit .

The present invention provides an image pixel sampling value conversion and sampling value processing method and device, which are used to convert high bit depth pixel sampling values into low bit depth sampling values, including: dividing the value range of high bit depth pixel sampling values into one or more segments, and determine the metadata of the segment endpoints, wherein the metadata includes the high-bit-depth pixel sample values of the segment endpoints and the corresponding low-bit-depth sample values; according to the metadata data, determining the segment where the high-bit-depth pixel sampling value in the image is located; and calculating the low-bit-depth sampling value corresponding to the high-bit-depth pixel sampling value according to the metadata of the segment endpoint. By converting high-bit-depth pixel sampling values into low-bit-depth sampling values, image color distortion and detail loss in the conversion between HDR and standard dynamic range video are reduced.

Those skilled in the art can understand that all or part of the steps in the above method can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk or an optical disk, and the like. Optionally, all or part of the steps in the foregoing embodiments may also be implemented using one or more integrated circuits. Correspondingly, each module/unit in the foregoing embodiments may be implemented in the form of hardware, or may be implemented in the form of software function modules. This application is not limited to any specific form of combination of hardware and software.

The above descriptions are only preferred examples of the present invention, and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (15)

1. An image pixel sampling value conversion method, for converting a high bit depth pixel sampling value into a low bit depth sampling value, characterized in that, comprising: Divide the value range of the high bit-depth pixel sample value into one or more segments, and determine the metadata of the segment endpoints, wherein the metadata includes the high bit-depth pixel sample values of the segment endpoints and their Corresponding low bit depth sample values; According to the metadata, determine the segment where the high bit depth pixel sampling value in the image is located; calculating a low bit depth sample value corresponding to the high bit depth pixel sample value according to the metadata of the segmentation endpoint; Wherein, the bit depth is the number of binary sign bits used to represent the pixel sample value. 2. The method according to claim 1, wherein the value range of the high bit depth pixel sampling value is divided into a plurality of segments, and the metadata of the segment endpoints are determined, including: Analyzing the image to determine statistical histogram information of the image; determining parameters of an adaptive quantization function according to the statistical histogram information; According to the adaptive quantization function, the low bit depth sample value corresponding to the high bit depth pixel sample value of the end point of the segment is calculated. 3. The method according to claim 2, further comprising: According to the statistical histogram information, calculate the normalized cumulative histogram distribution function value corresponding to the high bit depth pixel sampling value, and use it as a parameter of the adaptive quantization function. 4. The method according to claim 3, further comprising: The parameters of the adaptive quantization function also include adjustable numerical parameters. 5. The method according to claim 1, wherein the calculating the low bit depth sampling value corresponding to the high bit depth pixel sampling value according to the metadata of the segmentation endpoint comprises: Sets the low bit depth sample value as an interpolated value of the segment endpoint's metadata. 6. The method of claim 5, comprising: calculating a slope value for the segment based on the metadata of the segment endpoint; calculating a change in the high-bit-depth sampled value relative to the high-bit-depth sampled value in the segment endpoint metadata; setting the rate of change value to be the product of said change value and said slope value; The low bit-depth sample value corresponding to the high bit-depth sample value is set as the sum or difference of the change rate value and the low bit-depth sample value in the segment endpoint metadata. 7. The method of claim 1, further comprising: The metadata is encoded, and the encoded bits are written into at least one of the following data units in the code stream: parameter set, slice header information, auxiliary information unit, user-defined data unit, and description sub-information unit. 8. An image sampling value processing method, for converting a low bit depth pixel sampling value into a high bit depth sampling value, characterized in that, comprising: According to the metadata, determine the segmentation endpoint, and divide the value range of the low-bit-depth pixel sampling value into one or more segments, wherein the metadata includes the low-bit-depth pixel sampling value and the sum of the segmentation endpoint. The corresponding high bit depth sampling value; According to the metadata, determine the segment where the low bit depth pixel sampling value in the image is located; calculating a high bit depth sample value corresponding to the low bit depth pixel sample value according to the metadata of the segmentation endpoint; Wherein, the bit depth is the number of binary sign bits used to represent the pixel sample value. 9. The method according to claim 8, further comprising: before determining the segmentation endpoint according to the metadata: Parse the code stream, and obtain the metadata from at least one of the following data units of the code stream, including: parameter set, slice header information, auxiliary information unit, user-defined data unit, and description sub-information unit. 10. The method according to claim 8, wherein the calculating the high bit depth sampling value corresponding to the low bit depth pixel sampling value according to the metadata of the segmentation endpoint comprises: The high bit-depth sample value is set as an interpolation of the segment endpoint's metadata. 11. The method of claim 10, comprising: calculating a slope value for the segment based on the metadata of the segment endpoint; calculating a change in the low-bit-depth sampled value relative to the low-bit-depth sampled value in the segment endpoint metadata; setting the rate of change value to be the product of said change value and said slope value; The high bit-depth sample value corresponding to the low bit-depth sample value is set as the sum or difference of the change rate value and the high bit-depth sample value in the segment endpoint metadata. 12. A device for converting image pixel sampled values, used to convert high bit depth pixel sampled values into low bit depth sampled values, comprising: The first determination unit divides the value range of high bit-depth pixel sampling values into one or more segments, and determines the metadata of the segment endpoints, wherein the metadata includes the high bit-depth pixels of the segment endpoints Sampled values and their corresponding low-bit-depth sampled values; The second determining unit, according to the metadata, determines the segment where the sampling value of the high bit depth pixel in the image is located; The first calculation unit calculates the low bit depth sample value corresponding to the high bit depth pixel sample value according to the metadata of the segmentation endpoint; Wherein, the bit depth is the number of binary sign bits used to represent the pixel sample value. 13. The device according to claim 12, further comprising: A writing unit, encoding the metadata, and writing the encoded bits into at least one of the following data units in the code stream: parameter set, slice header information, auxiliary information unit, user-defined data unit, and descriptor information unit. 14. An image pixel sampling value processing device for converting a low bit depth pixel sampling value into a high bit depth sampling value, characterized in that it comprises: The third determination unit determines the segmentation endpoints according to the metadata, and divides the value range of the low bit depth pixel sample value into one or more segments, wherein the metadata includes the low bit depth of the segmentation endpoints Pixel sample values and their corresponding high bit depth sample values; A fourth determination unit, according to the metadata, determines the segment where the sampling value of the low-bit-depth pixel in the image is located; The second calculation unit calculates the high bit depth sample value corresponding to the low bit depth pixel sample value according to the metadata of the segmentation endpoint; Wherein, the bit depth is the number of binary sign bits used to represent the pixel sample value. 15. The method according to claim 14, wherein the third determining unit further comprises: The parsing unit parses the code stream, and obtains the metadata from at least one of the following data units of the code stream, including: parameter set, slice header information, auxiliary information unit, user-defined data unit, and description sub-information unit.