CN106204493A - A kind of method eliminating video block effect - Google Patents

Abstract

The present invention provides a kind of method eliminating video block effect, relates to technical field of image processing, it is intended to solve the problem that in prior art, the picture quality of deblocking effect is the highest.Described method includes: characteristic extraction step, and every two field picture is carried out blocking effect feature extraction；Plane filtering step, according to the difference of the blocking effect feature extracted, carries out plane block-eliminating effect filtering to every two field picture；Three-dimensional filtering step, in conjunction with every two field picture and the image of default frame number that is adjacent, carries out three-dimensional block-eliminating effect filtering to described every two field picture.

Description

A Method of Eliminating Video Block Effect

technical field

The invention relates to the technical field of image processing, in particular to a method for eliminating video block effects.

Background technique

Block-based transform coding is widely used in image compression coding. However, with the reduction of the bit rate, the quantization becomes more and more rough, and there will be discontinuity at the boundary of the block, forming an obvious defect of the reconstructed image, which is called block effect.

In order to reduce the blocking effect, some blocking effect removal algorithms can be applied to the image. For example, the iterative algorithm based on the convex projection set (POCS) theory, the spatial filtering algorithm, etc., have removed the block effect to a certain extent. However, how to use an algorithm as simple as possible to obtain higher image quality is an urgent problem to be solved by those skilled in the art.

Contents of the invention

The main purpose of the present invention is to propose a method for eliminating video blocking effect, aiming at solving the problem of low image quality caused by deblocking effect in the prior art.

In order to achieve the above object, a method for eliminating video blocking effect provided by the present invention includes: a feature extraction step, performing block effect feature extraction on each frame of image; The frame image is subjected to planar deblocking filtering; the three-dimensional filtering step is to combine each frame of image and images with a preset number of frames adjacent to it, and perform three-dimensional deblocking filtering on each frame of image.

Optionally, the feature extraction step includes: using an edge detection algorithm to perform block effect feature extraction on each frame of image.

Optionally, the plane filtering step includes: a feature division sub-step, dividing the extracted block effect features into horizontal direction block effect features and vertical direction block effect features; a plane filtering sub-step, respectively performing Deblocking filtering in the horizontal direction and deblocking filtering in the vertical direction.

Optionally, the plane filtering sub-step includes: a frequency division sub-step, when performing deblocking filtering in the horizontal direction or deblocking filtering in the vertical direction, divide the image blocks in each frame of image into high-frequency A feature block and a low-frequency feature block; a sub-step of frequency division filtering, using different filters for the high-frequency feature block and the low-frequency feature block to perform frequency-domain filtering.

Optionally, before the frequency division sub-step, the plane filtering sub-step further includes: a template setting sub-step, respectively setting filter templates for the deblocking filtering in the horizontal direction and the deblocking filtering in the vertical direction The frequency division sub-step specifically includes using the filter template to divide the image blocks in each frame of image into high-frequency feature blocks when performing deblocking filtering in the horizontal direction or deblocking filtering in the vertical direction and low-frequency feature blocks.

Optionally, the sub-step of frequency division filtering includes: performing smoothing filtering on the high-frequency feature blocks using a first Gaussian smoothing filtering method, and smoothing filtering on the low-frequency feature blocks using a second Gaussian smoothing filtering method; wherein, The variance of the first Gaussian smoothing filtering method is greater than a first preset threshold, the variance of the second Gaussian smoothing filtering method is smaller than a second preset threshold, and the first preset threshold is greater than or equal to the second preset threshold threshold.

Optionally, the three-dimensional filtering step includes: a motion frame detection sub-step, performing motion frame detection on each frame of image and images with a preset number of frames adjacent to it; a time dimension filtering sub-step, according to the detection result, the Each frame of image is filtered in time dimension.

Optionally, the moving frame detection sub-step includes: a frame difference calculation sub-step, performing frame difference calculation on the current frame and its adjacent preset frame number images; a moving frame discrimination sub-step, when the accumulated frame difference is greater than In the case of a preset frame difference, it is determined that the current frame is a motion-insignificant frame, and in the case of a cumulative frame difference value less than or equal to the preset frame difference, it is determined that the current frame is a motion-significant frame.

Optionally, the time dimension filtering sub-step includes: for the high-frequency feature blocks in frames with no significant motion, using the current frame and its adjacent frames to perform weighted sum smoothing calculation; for the high-frequency feature blocks in frames with significant motion Low-frequency feature blocks, using the current frame and its adjacent frames for weighted sum smoothing calculations.

Optionally, the preset number of frames is 4 frames.

The method for eliminating video blockiness provided by the embodiment of the present invention can perform blockiness feature extraction according to different blockiness features of each image block in each frame of image, and perform plane removal on each frame of image according to different blockiness features. Block effect filtering, so that the deblocking effect in the same frame image is more targeted, and then three-dimensional filtering is performed by combining the relationship between each frame image and several adjacent frame images, so that the two-dimensional texture of the image can be taken into account at the same time Information and three-dimensional space-time motion information, save more image detail features, and effectively improve image quality.

Description of drawings

FIG. 1 is a flow chart of a method for eliminating video blocking effects provided by an embodiment of the present invention.

FIG. 2 is a specific flow chart of a step in the method for eliminating video blocking effects provided by an embodiment of the present invention.

FIG. 3 is a specific flowchart of a step in FIG. 2 .

FIG. 4 is another specific flowchart of a step in FIG. 2 .

FIG. 5 is a specific flow chart of another step in the method for eliminating video blocking effects provided by an embodiment of the present invention.

FIG. 6 is a detailed flow chart of a method for eliminating video blocking effects provided by an embodiment of the present invention.

detailed description

It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

As shown in Figure 1, an embodiment of the present invention provides a method for eliminating video blocking effects, including:

S11, a feature extraction step, performing block effect feature extraction on each frame of image;

S12, the planar filtering step, performing planar deblocking filtering on each frame of image according to the extracted blockiness features;

S13, a three-dimensional filtering step, performing three-dimensional deblocking filtering on each frame of image by combining each frame of image and images of a preset number of adjacent frames.

The method for eliminating video blockiness provided by the embodiment of the present invention can perform blockiness feature extraction according to different blockiness features of each image block in each frame of image, and perform plane removal on each frame of image according to different blockiness features. Block effect filtering, so that the deblocking effect in the same frame image is more targeted, and then three-dimensional filtering is performed by combining the relationship between each frame image and several adjacent frame images, so that the two-dimensional texture of the image can be taken into account at the same time Information and three-dimensional space-time motion information, save more image detail features, and effectively improve image quality.

It should be noted that when the image block effect occurs, the corresponding frequency domain coefficients will change significantly. Therefore, the method for eliminating the video block effect provided by the embodiment of the present invention is performed after transforming the image into the frequency domain, for example The original image signal is transformed into the frequency domain by discrete cosine transform or the like.

Specifically, in step S11, various algorithms may be used to extract block effect features from the image. From the perspective of image effect, the occurrence of block effect means that a new edge appears in the image, therefore, optionally, in one embodiment of the present invention, an edge detection algorithm can be used to perform block effect feature on each frame of image Extraction, the edge information of the image is used as the feature value.

After feature extraction, plane filtering can be performed on each frame of image. Specifically, as shown in FIG. 2, in step S12, the plane filtering step may include:

S121, a feature division sub-step, dividing the extracted block effect features into horizontal block effect features and vertical block effect features;

S122, the sub-step of planar filtering, respectively performing deblocking filtering in the horizontal direction and deblocking filtering in the vertical direction on each frame of image.

That is to say, in view of the directional characteristics of the feature block effect in the frequency domain, the extracted block effect features are divided into horizontal direction and vertical direction, and then deblocking filtering is performed on these two directions, so that the plane filtering More targeted.

Optionally, in the plane filtering sub-step, when performing deblocking filtering in the horizontal direction or vertical direction, image features may be further distinguished, and different filtering methods may be adopted for different image features to further improve the filtering effect.

For example, as shown in Figure 3, in one embodiment of the present invention, the sub-steps of plane filtering may include:

S1221, the frequency division sub-step, when performing deblocking filtering in the horizontal direction or deblocking filtering in the vertical direction, divide the image blocks in each frame of image into high-frequency feature blocks and low-frequency feature blocks;

S1222, the sub-step of frequency division filtering, using different filters for the high-frequency feature block and the low-frequency feature block to perform frequency-domain filtering.

Optionally, the separation of high-frequency feature blocks and low-frequency feature blocks may be achieved by using a filter template to filter each frame of image. For example, as shown in Figure 4, in another embodiment of the present invention, the plane filtering sub-step may specifically include:

S1223. Set filter templates for the deblocking filtering in the horizontal direction and the deblocking filtering in the vertical direction respectively;

S1224. When performing deblocking filtering in the horizontal direction or deblocking filtering in the vertical direction, use the filtering template to divide the image blocks in each frame of image into high-frequency feature blocks and low-frequency feature blocks;

S1225. Use different filters to perform frequency domain filtering on the high-frequency feature block and the low-frequency feature block.

It is understandable that because different quantization errors are superimposed on different image blocks, the correlation between image blocks is destroyed, and discontinuity will appear at the boundary of the block. When the quantization error is too large, this discontinuity exceeds the human eye. When the recognition threshold is exceeded, an error visible to the human eye occurs. In order to eliminate the block effect, the image can be smoothed and filtered to try to make the boundary of the image block continuous.

Optionally, a Gaussian smoothing filtering method may be used to perform smoothing filtering on the image block. Specifically, for the case where high-frequency feature blocks have been distinguished from low-frequency feature blocks, Gaussian smoothing filters with different parameters or adaptive coefficients can be used to perform corresponding filtering.

For example, in the sub-step of frequency division filtering, the first Gaussian smoothing method can be used to smooth filter the high-frequency feature blocks, and the second Gaussian smoothing method can be used to smooth filter the low-frequency feature blocks; wherein, the first Gaussian smoothing The variance of the filtering method is greater than a first preset threshold, the variance of the second Gaussian smoothing filtering method is smaller than a second preset threshold, and the first preset threshold is greater than or equal to the second preset threshold.

That is to say, when eliminating the block effect in the horizontal direction or the block effect in the vertical direction, the filter template can be set according to the characteristics of the parameters of the image block in the transform domain, and the image block can be divided into high-frequency feature blocks and low-frequency feature blocks, and then different The Gaussian smoothing filter effect is performed on the adaptive coefficient of the high-frequency feature block, and the Gaussian smoothing filter with a large variance is selected for the high-frequency feature block, and the Gaussian smoothing filter with a small variance is selected for the low-frequency feature block.

After the planar filtering is completed, further, each frame of image can be further filtered in combination with the motion of the object in the image, so as to complete the three-dimensional filtering. Among them, the movement of the object in the image can be judged by the dynamic changes of several adjacent frames of images.

For example, as shown in Figure 5, step S13 may include:

S131, the motion frame detection sub-step is to perform motion frame detection on each frame image and the images of the preset number of frames adjacent to it;

S132, the time dimension filtering sub-step, performing time dimension filtering on each frame of image according to the detection result.

Among them, the preset number of frames can be determined according to the requirements of the image processing effect, for example, 2 to 8 frames, and in one embodiment of the present invention, it is 4 frames, so that counting the current frame, a total of 5 frames of images participate in the motion frame detection.

Optionally, the motion frame detection sub-step may specifically include:

The frame difference calculation sub-step is to perform frame difference calculation on the images of the current frame and its adjacent preset frames;

The motion frame discrimination sub-step is to determine that the current frame is an insignificant motion frame when the accumulated frame difference is greater than the preset frame difference, and to determine if the accumulated frame difference is less than or equal to the preset frame difference The current frame is a motion salient frame.

Correspondingly, the time dimension filtering sub-step may specifically include:

For the high-frequency feature block in the motion insignificant frame, use the current frame and its adjacent frames to perform weighted sum smoothing calculation;

For the low-frequency feature block in the frame with significant motion, the current frame and its adjacent frames are used to perform weighted sum smoothing calculation.

The method for eliminating video blocking effect provided by the present invention will be described in detail below through specific embodiments.

As shown in Figure 6, in this embodiment, the method for eliminating video blocking effects may include the following steps:

S201, performing block effect feature extraction on each frame of image;

S202, dividing the extracted block effect features into horizontal block effect features and vertical block effect features;

S203. Set filter templates for the deblocking filtering in the horizontal direction and the deblocking filtering in the vertical direction respectively;

S204. When performing deblocking filtering in the horizontal direction or deblocking filtering in the vertical direction, use the filtering template to divide the image blocks in each frame of image into high-frequency feature blocks and low-frequency feature blocks;

S205, use the first Gaussian smoothing filtering method to perform smoothing filtering on the high-frequency feature blocks, and use the second Gaussian smoothing filtering method to perform smoothing filtering on the low-frequency feature blocks; wherein, the variance of the first Gaussian smoothing filtering method is greater than the first preset threshold, the variance of the second Gaussian smoothing filtering method is less than a second preset threshold, and the first preset threshold is greater than or equal to the second preset threshold;

S206, performing frame difference calculation on the current frame and the images of the preset number of adjacent frames;

S207. If the cumulative frame difference is greater than the preset frame difference, determine that the current frame is a motion-insignificant frame, and if the cumulative frame difference is less than or equal to the preset frame difference, determine the current frame is the motion salient frame;

S208, for the high-frequency feature blocks in the motion insignificant frame, use the current frame and its adjacent frames to perform weighted sum smoothing calculation; for the low-frequency feature blocks in the motion significant frame, use the current frame and its adjacent frames to perform weighted sum smoothing calculation and smooth calculations.

The method for eliminating video blocking effect provided by this embodiment firstly detects different features of blocking effect in two-dimensional space, then performs smoothing in two-dimensional space, and then combines three-dimensional space motion information to further perform smoothing and filtering of different feature blocks, thus It can take into account the two-dimensional texture information and three-dimensional space-time motion information of the image at the same time, save more image detail features, and greatly improve the image quality.

It should be noted that, in this document, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product in essence or the part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM, disk, CD) contains several instructions to make a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in various embodiments of the present invention.

The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the description of the present invention and the contents of the accompanying drawings, or directly or indirectly used in other related technical fields , are all included in the scope of patent protection of the present invention in the same way.

Claims (10)

1. A method for eliminating video blocking effect, comprising: The feature extraction step is to extract block effect features for each frame of image; The planar filtering step is to perform planar deblocking filtering on each frame image according to the extracted blockiness features; In the three-dimensional filtering step, performing three-dimensional deblocking filtering on each frame of image by combining each frame of image and images of a preset number of frames adjacent to it. 2. the method for eliminating video blocking effect according to claim 1, is characterized in that, described feature extraction step comprises: An edge detection algorithm is used to extract block effect features from each frame of image. 3. the method for eliminating video block effect according to claim 1, is characterized in that, described plane filtering step comprises: The feature division sub-step divides the extracted block effect features into horizontal block effect features and vertical block effect features; The planar filtering sub-step is to respectively perform deblocking filtering in the horizontal direction and deblocking filtering in the vertical direction on each frame of the image. 4. the method for eliminating video block effect according to claim 1, is characterized in that, described plane filtering sub-step comprises: The frequency division sub-step is to divide the image blocks in each frame of the image into high-frequency feature blocks and low-frequency feature blocks when performing deblocking filtering in the horizontal direction or deblocking filtering in the vertical direction; In the sub-step of frequency division filtering, different filters are used to perform frequency domain filtering on the high-frequency feature block and the low-frequency feature block. 5. The method for eliminating video block effects according to claim 4, wherein, before the frequency division sub-step, the plane filtering sub-step also includes: Template setting sub-step, respectively setting filter templates for the deblocking filtering in the horizontal direction and the deblocking filtering in the vertical direction; The frequency division sub-step specifically includes when performing deblocking filtering in the horizontal direction or deblocking filtering in the vertical direction, using the filtering template to divide the image blocks in each frame of image into high-frequency feature blocks and Low frequency feature block. 6. the method for eliminating video block effect according to claim 4, is characterized in that, described frequency division filtering sub-step comprises: Use the first Gaussian smoothing filtering method to perform smoothing filtering on the high-frequency feature blocks, and use the second Gaussian smoothing filtering method to perform smoothing filtering on the low-frequency feature blocks; wherein, the variance of the first Gaussian smoothing filtering method is greater than that of the first Gaussian smoothing filtering method. A preset threshold, the variance of the second Gaussian smoothing filtering method is smaller than a second preset threshold, and the first preset threshold is greater than or equal to the second preset threshold. 7. The method for eliminating video block effects according to claim 4, wherein the three-dimensional filtering step comprises: The motion frame detection sub-step is to perform motion frame detection on each frame image and the images of the preset number of frames adjacent to it; The time dimension filtering sub-step is to perform time dimension filtering on each frame of image according to the detection result. 8. the method for eliminating video block effect according to claim 7, is characterized in that, described motion frame detection sub-step comprises: The frame difference calculation sub-step is to perform frame difference calculation on the images of the current frame and its adjacent preset frames; The motion frame discrimination sub-step is to determine that the current frame is an insignificant motion frame when the accumulated frame difference is greater than the preset frame difference, and to determine if the accumulated frame difference is less than or equal to the preset frame difference The current frame is a motion salient frame. 9. The method for eliminating video blockiness according to claim 8, wherein the time dimension filtering sub-step comprises: For the high-frequency feature block in the motion insignificant frame, use the current frame and its adjacent frames to perform weighted sum smoothing calculation; For the low-frequency feature block in the frame with significant motion, the current frame and its adjacent frames are used to perform weighted sum smoothing calculation. 10. The method for eliminating video blockiness according to any one of claims 1 to 9, wherein the preset number of frames is 4 frames.