KR20220137552A - Video Coding Method and Apparatus Using Pre-processing and Post-processing - Google Patents

Abstract

A video coding method and apparatus using pre-processing and post-processing are disclosed. The present embodiment provides a video coding method and apparatus that include: modeling a noise model or a subjective quality model for a current video; pre-processing and post-processing on an image based on the model; and signaling information of the model.

Description

Video Coding Method and Apparatus Using Pre-processing and Post-processing

The present disclosure relates to a video coding method and apparatus using pre-processing and post-processing.

The content described below merely provides background information related to the present embodiment and does not constitute the prior art.

Since video data has a large amount of data compared to audio data or still image data, it requires a lot of hardware resources including memory to store or transmit itself without compression processing.

Accordingly, in general, when storing or transmitting video data, an encoder is used to compress and store or transmit the video data, and a decoder receives, decompresses, and reproduces the compressed video data. As such a video compression technique, there are H.264/AVC, High Efficiency Video Coding (HEVC), and the like, and Versatile Video Coding (VVC), which improves encoding efficiency by about 30% or more compared to HEVC.

However, as the size, resolution, and frame rate of an image are gradually increasing, and the amount of data to be encoded is increasing accordingly, a new compression technique with higher encoding efficiency and higher image quality improvement than existing compression techniques is required.

With respect to a video coding method and apparatus, a series of processes applied to an input image before an encoder encodes the input image is defined as a preprocessing process. In addition, a series of processes applied to the restored image before the decoder stores or displays the restored image is defined as a post-processing process. On the other hand, since the input image contains noise or its resolution is too high, encoding efficiency or image quality may be reduced from the standpoint of the encoder/decoder. Therefore, in order to improve encoding efficiency and improve image quality, effective pre-processing and post-processing processes need to be considered.

The present disclosure models a noise model or a subjective image quality model for a current video, performs pre-processing and post-processing on an image based on the model, and video coding for signaling information of the model It is an object to provide a method and apparatus.

According to an embodiment of the present disclosure, there is provided an image decoding method performed by an image decoding apparatus, the method comprising: generating a restored image by decoding a bitstream; decoding a parameter of a perceptual model from the bitstream, wherein the perceptual model is a model reflecting perceptual visual characteristics in terms of perceptual quality; generating an improved image by post-processing the reconstructed image using the parameters of the cognitive model; and outputting a final reconstructed image using the improved image and the reconstructed image.

According to another embodiment of the present disclosure, a decoder for decoding a bitstream to generate a reconstructed image, and decoding a perceptual model-based image quality improvement method and a parameter of the perceptual model from the bitstream, wherein the perceptual model is a perceptual quality It is a model that reflects cognitive visual characteristics in perspective; a cognitive quality improver for generating an improved image by post-processing the reconstructed image using the image quality improvement method and parameters of the cognitive model; and a summer for outputting a final reconstructed image using the enhanced image and the reconstructed image.

According to another embodiment of the present disclosure, in an image encoding method performed by an image encoding apparatus, an input image is analyzed in terms of perceived quality to determine removable elements according to a perceptual model. step, wherein the cognitive model is a model reflecting cognitive visual characteristics in terms of the cognitive quality; estimating parameters of the cognitive model; pre-processing the input image by removing the removable elements from the input image using the parameters of the cognitive model; generating a bitstream by encoding the preprocessed input image; and encoding the parameters of the cognitive model and combining them with the bitstream.

As described above, according to the present embodiment, a video coding method and apparatus for modeling a noise model or a subjective quality model for a current video, pre-processing and post-processing an image based on the model, and signaling information of the model By providing , there is an effect that it becomes possible to improve encoding efficiency and improve image quality.

1 is an exemplary block diagram of an image encoding apparatus that can implement techniques of the present disclosure.
2 is a diagram for explaining a method of dividing a block using a QTBTTT structure.
3A and 3B are diagrams illustrating a plurality of intra prediction modes including wide-angle intra prediction modes.
4 is an exemplary diagram of a neighboring block of the current block.
5 is an exemplary block diagram of an image decoding apparatus capable of implementing the techniques of the present disclosure.
6 is an exemplary diagram illustrating an image encoding apparatus and an image decoding apparatus including pre-processing and post-processing according to an embodiment of the present disclosure.
7 is an exemplary diagram illustrating a bidirectional filter used for pre-processing and post-processing according to an embodiment of the present disclosure.
8 is an exemplary diagram illustrating an image encoding apparatus and an image decoding apparatus including pre-processing and post-processing according to another embodiment of the present disclosure.
9 is an exemplary diagram illustrating an image encoding apparatus and an image decoding apparatus including pre-processing and post-processing according to another embodiment of the present disclosure.
10 is an exemplary diagram illustrating an image encoding method including pre-processing according to an embodiment of the present disclosure.
11 is an exemplary diagram illustrating an image decoding method including post-processing according to an embodiment of the present disclosure.
12 is an exemplary diagram illustrating an image encoding method including pre-processing according to another embodiment of the present disclosure.
13 is an exemplary diagram illustrating an image decoding method including post-processing according to another embodiment of the present disclosure.

Hereinafter, some embodiments of the present disclosure will be described in detail with reference to exemplary drawings. In adding reference numerals to the components of each drawing, it should be noted that the same components are given the same reference numerals as much as possible even though they are indicated on different drawings. In addition, in the description of the present embodiments, if it is determined that a detailed description of a related well-known configuration or function may obscure the gist of the present embodiments, the detailed description thereof will be omitted.

1 is an exemplary block diagram of an image encoding apparatus that can implement techniques of the present disclosure. Hereinafter, an image encoding apparatus and sub-components of the apparatus will be described with reference to FIG. 1 .

The image encoding apparatus includes a picture division unit 110 , a prediction unit 120 , a subtractor 130 , a transform unit 140 , a quantization unit 145 , a reordering unit 150 , an entropy encoding unit 155 , and an inverse quantization unit. 160 , an inverse transform unit 165 , an adder 170 , a loop filter unit 180 , and a memory 190 may be included.

Each component of the image encoding apparatus may be implemented as hardware or software, or a combination of hardware and software. In addition, the function of each component may be implemented as software and the microprocessor may be implemented to execute the function of software corresponding to each component.

One image (video) is composed of one or more sequences including a plurality of pictures. Each picture is divided into a plurality of regions, and encoding is performed for each region. For example, one picture is divided into one or more tiles and/or slices. Here, one or more tiles may be defined as a tile group. Each tile or/slice is divided into one or more Coding Tree Units (CTUs). And each CTU is divided into one or more CUs (Coding Units) by a tree structure. Information applied to each CU is encoded as a syntax of the CU, and information commonly applied to CUs included in one CTU is encoded as a syntax of the CTU. In addition, information commonly applied to all blocks in one slice is encoded as a syntax of a slice header, and information applied to all blocks constituting one or more pictures is a picture parameter set (PPS) or a picture. encoded in the header. Furthermore, information commonly referenced by a plurality of pictures is encoded in a sequence parameter set (SPS). In addition, information commonly referred to by one or more SPSs is encoded in a video parameter set (VPS). Also, information commonly applied to one tile or tile group may be encoded as a syntax of a tile or tile group header. Syntaxes included in the SPS, PPS, slice header, tile, or tile group header may be referred to as high-level syntax.

The picture divider 110 determines the size of a coding tree unit (CTU). Information on the size of the CTU (CTU size) is encoded as a syntax of the SPS or PPS and transmitted to the video decoding apparatus.

The picture divider 110 divides each picture constituting an image into a plurality of coding tree units (CTUs) having a predetermined size, and then repeatedly divides the CTUs using a tree structure. (recursively) divide. A leaf node in the tree structure becomes a coding unit (CU), which is a basic unit of encoding.

As a tree structure, a quadtree (QT) in which a parent node (or parent node) is divided into four child nodes (or child nodes) of the same size, or a binary tree (BinaryTree) in which a parent node is divided into two child nodes , BT), or a ternary tree (TT) in which a parent node is divided into three child nodes in a 1:2:1 ratio, or a structure in which two or more of these QT structures, BT structures, and TT structures are mixed have. For example, a QuadTree plus BinaryTree (QTBT) structure may be used, or a QuadTree plus BinaryTree TernaryTree (QTBTTT) structure may be used. Here, BTTT may be collectively referred to as a Multiple-Type Tree (MTT).

2 is a diagram for explaining a method of dividing a block using a QTBTTT structure.

As shown in FIG. 2 , the CTU may be first divided into a QT structure. The quadtree splitting may be repeated until the size of a splitting block reaches the minimum block size (MinQTSize) of a leaf node allowed in QT. A first flag (QT_split_flag) indicating whether each node of the QT structure is split into four nodes of a lower layer is encoded by the entropy encoder 155 and signaled to the image decoding apparatus. If the leaf node of the QT is not larger than the maximum block size (MaxBTSize) of the root node allowed in the BT, it may be further divided into any one or more of the BT structure or the TT structure. A plurality of division directions may exist in the BT structure and/or the TT structure. For example, there may be two directions in which the block of the corresponding node is divided horizontally and vertically. As shown in FIG. 2 , when MTT splitting starts, a second flag (mtt_split_flag) indicating whether or not nodes are split, and a flag indicating additional splitting direction (vertical or horizontal) if split and/or split type (Binary) or Ternary) is encoded by the entropy encoder 155 and signaled to the video decoding apparatus.

Alternatively, before encoding the first flag (QT_split_flag) indicating whether each node is split into four nodes of a lower layer, a CU split flag (split_cu_flag) indicating whether the node is split is encoded it might be When the CU split flag (split_cu_flag) value indicates that it is not split, the block of the corresponding node becomes a leaf node in the split tree structure and becomes a coding unit (CU), which is a basic unit of coding. When the CU split flag (split_cu_flag) value indicates to be split, the image encoding apparatus starts encoding from the first flag in the above-described manner.

When QTBT is used as another example of the tree structure, there are two types of splitting the block of the corresponding node into two blocks of the same size horizontally (i.e., symmetric horizontal splitting) and vertically splitting (i.e., symmetric vertical splitting). branches may exist. A split flag (split_flag) indicating whether each node of the BT structure is split into blocks of a lower layer and split type information indicating a split type are encoded by the entropy encoder 155 and transmitted to the image decoding apparatus. On the other hand, there may be additionally a type in which the block of the corresponding node is divided into two blocks having an asymmetric shape. The asymmetric form may include a form in which the block of the corresponding node is divided into two rectangular blocks having a size ratio of 1:3, or a form in which the block of the corresponding node is divided in a diagonal direction.

A CU may have various sizes depending on the QTBT or QTBTTT split from the CTU. Hereinafter, a block corresponding to a CU to be encoded or decoded (ie, a leaf node of QTBTTT) is referred to as a 'current block'. According to the adoption of QTBTTT partitioning, the shape of the current block may be not only a square but also a rectangle.

The prediction unit 120 generates a prediction block by predicting the current block. The prediction unit 120 includes an intra prediction unit 122 and an inter prediction unit 124 .

In general, each of the current blocks in a picture may be predictively coded. In general, the prediction of the current block is performed using an intra prediction technique (using data from the picture containing the current block) or inter prediction technique (using data from a picture coded before the picture containing the current block). can be performed. Inter prediction includes both uni-prediction and bi-prediction.

The intra prediction unit 122 predicts pixels in the current block by using pixels (reference pixels) located around the current block in the current picture including the current block. A plurality of intra prediction modes exist according to a prediction direction. For example, as shown in FIG. 3A , the plurality of intra prediction modes may include two non-directional modes including a planar mode and a DC mode and 65 directional modes. According to each prediction mode, the neighboring pixels to be used and the calculation expression are defined differently.

For efficient directional prediction of a rectangular-shaped current block, directional modes (Nos. 67 to 80 and No. -1 to No. -14 intra prediction modes) shown by dotted arrows in FIG. 3B may be additionally used. These may be referred to as “wide angle intra-prediction modes”. Arrows in FIG. 3B indicate corresponding reference samples used for prediction, not prediction directions. The prediction direction is opposite to the direction indicated by the arrow. The wide-angle intra prediction modes are modes in which a specific directional mode is predicted in the opposite direction without additional bit transmission when the current block is rectangular. In this case, among the wide-angle intra prediction modes, some wide-angle intra prediction modes available for the current block may be determined by the ratio of the width to the height of the rectangular current block. For example, the wide-angle intra prediction modes having an angle smaller than 45 degrees (intra prediction modes 67 to 80) are available when the current block has a rectangular shape with a height smaller than the width, and a wide angle having an angle greater than -135 degrees. The intra prediction modes (intra prediction modes -1 to -14) are available when the current block has a rectangular shape with a width greater than a height.

The intra prediction unit 122 may determine an intra prediction mode to be used for encoding the current block. In some examples, the intra prediction unit 122 may encode the current block using several intra prediction modes and select an appropriate intra prediction mode to use from the tested modes. For example, the intra prediction unit 122 calculates bit rate distortion values using rate-distortion analysis for several tested intra prediction modes, and has the best bit rate distortion characteristics among the tested modes. An intra prediction mode may be selected.

The intra prediction unit 122 selects one intra prediction mode from among a plurality of intra prediction modes, and predicts the current block by using a neighboring pixel (reference pixel) determined according to the selected intra prediction mode and an arithmetic expression. Information on the selected intra prediction mode is encoded by the entropy encoder 155 and transmitted to the image decoding apparatus.

The inter prediction unit 124 generates a prediction block for the current block by using a motion compensation process. The inter prediction unit 124 searches for a block most similar to the current block in the coded and decoded reference picture before the current picture, and generates a prediction block for the current block using the searched block. Then, a motion vector (MV) corresponding to displacement between the current block in the current picture and the prediction block in the reference picture is generated. In general, motion estimation is performed for a luma component, and a motion vector calculated based on the luma component is used for both the luma component and the chroma component. Motion information including information on a reference picture and information on a motion vector used to predict the current block is encoded by the entropy encoder 155 and transmitted to the image decoding apparatus.

The inter prediction unit 124 may perform interpolation on a reference picture or reference block in order to increase prediction accuracy. That is, subsamples between two consecutive integer samples are interpolated by applying filter coefficients to a plurality of consecutive integer samples including the two integer samples. When the process of searching for a block most similar to the current block is performed with respect to the interpolated reference picture, the motion vector may be expressed up to the precision of the decimal unit rather than the precision of the integer sample unit. The precision or resolution of the motion vector may be set differently for each unit of a target region to be encoded, for example, a slice, a tile, a CTU, or a CU. When such adaptive motion vector resolution (AMVR) is applied, information on the motion vector resolution to be applied to each target region should be signaled for each target region. For example, when the target region is a CU, information on motion vector resolution applied to each CU is signaled. The information on the motion vector resolution may be information indicating the precision of a differential motion vector, which will be described later.

Meanwhile, the inter prediction unit 124 may perform inter prediction using bi-prediction. In the case of bidirectional prediction, two reference pictures and two motion vectors indicating the position of a block most similar to the current block in each reference picture are used. The inter prediction unit 124 selects a first reference picture and a second reference picture from the reference picture list 0 (RefPicList0) and the reference picture list 1 (RefPicList1), respectively, and searches for a block similar to the current block in each reference picture. A first reference block and a second reference block are generated. Then, the prediction block for the current block is generated by averaging or weighting the first reference block and the second reference block. In addition, motion information including information on two reference pictures and information on two motion vectors used to predict the current block is transmitted to the encoder 150 . Here, the reference picture list 0 is composed of pictures before the current picture in display order among the restored pictures, and the reference picture list 1 is composed of pictures after the current picture in the display order among the restored pictures. have. However, the present invention is not limited thereto, and in display order, the restored pictures after the current picture may be further included in the reference picture list 0, and conversely, the restored pictures before the current picture are additionally added to the reference picture list 1. may be included.

Various methods may be used to minimize the amount of bits required to encode motion information.

For example, when the reference picture and motion vector of the current block are the same as the reference picture and motion vector of the neighboring block, the motion information of the current block may be transmitted to the image decoding apparatus by encoding information for identifying the neighboring block. This method is called 'merge mode'.

In the merge mode, the inter prediction unit 124 selects a predetermined number of merge candidate blocks (hereinafter, referred to as 'merge candidates') from neighboring blocks of the current block.

As the neighboring blocks for inducing the merge candidate, as shown in FIG. 4 , the left block (A0), the lower left block (A1), the upper block (B0), and the upper right block (B1) adjacent to the current block in the current picture. ), and all or part of the upper left block (A2) may be used. Also, a block located in a reference picture (which may be the same as or different from the reference picture used to predict the current block) other than the current picture in which the current block is located may be used as a merge candidate. For example, a block co-located with the current block in the reference picture or blocks adjacent to the co-located block may be further used as merge candidates. If the number of merge candidates selected by the above-described method is smaller than the preset number, a 0 vector is added to the merge candidates.

The inter prediction unit 124 constructs a merge list including a predetermined number of merge candidates by using these neighboring blocks. A merge candidate to be used as motion information of the current block is selected from among the merge candidates included in the merge list, and merge index information for identifying the selected candidate is generated. The generated merge index information is encoded by the encoder 150 and transmitted to the image decoding apparatus.

The merge skip mode is a special case of the merge mode. After performing quantization, when all transform coefficients for entropy encoding are close to zero, only neighboring block selection information is transmitted without transmission of a residual signal. By using the merge skip mode, it is possible to achieve relatively high encoding efficiency in an image with little motion, a still image, or a screen content image.

Hereinafter, the merge mode and the merge skip mode are collectively referred to as a merge/skip mode.

Another method for encoding motion information is AMVP (Advanced Motion Vector Prediction) mode.

In the AMVP mode, the inter prediction unit 124 derives motion vector prediction candidates for the motion vector of the current block using neighboring blocks of the current block. As neighboring blocks used to derive prediction motion vector candidates, the left block (A0), the lower left block (A1), the upper block (B0), and the upper right block (A0) adjacent to the current block in the current picture shown in FIG. B1), and all or part of the upper left block (A2) may be used. In addition, a block located in a reference picture (which may be the same as or different from the reference picture used to predict the current block) other than the current picture in which the current block is located is used as a neighboring block used to derive prediction motion vector candidates. may be For example, a block co-located with the current block in the reference picture or blocks adjacent to the co-located block may be used. If the number of motion vector candidates is smaller than the preset number by the method described above, 0 vectors are added to the motion vector candidates.

The inter prediction unit 124 derives prediction motion vector candidates by using the motion vectors of the neighboring blocks, and determines a predicted motion vector with respect to the motion vector of the current block by using the prediction motion vector candidates. Then, a differential motion vector is calculated by subtracting the predicted motion vector from the motion vector of the current block.

The prediction motion vector may be obtained by applying a predefined function (eg, a median value, an average value operation, etc.) to the prediction motion vector candidates. In this case, the image decoding apparatus also knows the predefined function. In addition, since the neighboring block used to derive the prediction motion vector candidate is a block that has already been encoded and decoded, the video decoding apparatus already knows the motion vector of the neighboring block. Therefore, the image encoding apparatus does not need to encode information for identifying the prediction motion vector candidate. Accordingly, in this case, information on a differential motion vector and information on a reference picture used to predict the current block are encoded.

Meanwhile, the prediction motion vector may be determined by selecting any one of the prediction motion vector candidates. In this case, information for identifying the selected prediction motion vector candidate is additionally encoded together with information on the differential motion vector and information on the reference picture used to predict the current block.

The subtractor 130 generates a residual block by subtracting the prediction block generated by the intra prediction unit 122 or the inter prediction unit 124 from the current block.

The transform unit 140 transforms the residual signal in the residual block having pixel values in the spatial domain into transform coefficients in the frequency domain. The transform unit 140 may transform the residual signals in the residual block by using the entire size of the residual block as a transform unit, or divide the residual block into a plurality of sub-blocks and use the sub-blocks as transform units to perform transformation. You may. Alternatively, the residual signals may be transformed by dividing the sub-block into two sub-blocks, which are a transform region and a non-transform region, and use only the transform region sub-block as a transform unit. Here, the transform region subblock may be one of two rectangular blocks having a size ratio of 1:1 based on the horizontal axis (or vertical axis). In this case, the flag (cu_sbt_flag) indicating that only the subblock is transformed, the vertical/horizontal information (cu_sbt_horizontal_flag), and/or the position information (cu_sbt_pos_flag) are encoded by the entropy encoder 155 and signaled to the video decoding apparatus. do. Also, the size of the transform region subblock may have a size ratio of 1:3 based on the horizontal axis (or vertical axis). Signaled to the decoding device.

Meanwhile, the transform unit 140 may separately transform the residual block in a horizontal direction and a vertical direction. For transformation, various types of transformation functions or transformation matrices may be used. For example, a pair of transform functions for horizontal transformation and vertical transformation may be defined as a multiple transform set (MTS). The transform unit 140 may select one transform function pair having the best transform efficiency among MTSs and transform the residual blocks in horizontal and vertical directions, respectively. Information (mts_idx) on the transform function pair selected from among MTS is encoded by the entropy encoder 155 and signaled to the image decoding apparatus.

The quantization unit 145 quantizes the transform coefficients output from the transform unit 140 using a quantization parameter, and outputs the quantized transform coefficients to the entropy encoding unit 155 . The quantization unit 145 may directly quantize a related residual block for a certain block or frame without transformation. The quantization unit 145 may apply different quantization coefficients (scaling values) according to positions of the transform coefficients in the transform block. A quantization matrix applied to two-dimensionally arranged quantized transform coefficients may be encoded and signaled to an image decoding apparatus.

The rearrangement unit 150 may rearrange the coefficient values on the quantized residual values.

The reordering unit 150 may change a two-dimensional coefficient array into a one-dimensional coefficient sequence by using coefficient scanning. For example, the reordering unit 150 may output a one-dimensional coefficient sequence by scanning from DC coefficients to coefficients in a high frequency region using a zig-zag scan or a diagonal scan. . A vertical scan for scanning a two-dimensional coefficient array in a column direction and a horizontal scan for scanning a two-dimensional block shape coefficient in a row direction may be used instead of the zig-zag scan according to the size of the transform unit and the intra prediction mode. That is, a scanning method to be used among a zig-zag scan, a diagonal scan, a vertical scan, and a horizontal scan may be determined according to the size of the transform unit and the intra prediction mode.

The entropy encoding unit 155 uses various encoding methods such as Context-based Adaptive Binary Arithmetic Code (CABAC) and Exponential Golomb to convert the one-dimensional quantized transform coefficients output from the reordering unit 150 . A bitstream is created by encoding the sequence.

In addition, the entropy encoder 155 encodes information such as a CTU size, a CU split flag, a QT split flag, an MTT split type, an MTT split direction, etc. related to block splitting, so that the video decoding apparatus divides the block in the same way as the video encoding apparatus. to be able to divide. Also, the entropy encoding unit 155 encodes information on a prediction type indicating whether the current block is encoded by intra prediction or inter prediction, and intra prediction information (ie, intra prediction) according to the prediction type. Mode information) or inter prediction information (information on an encoding mode (merge mode or AMVP mode) of motion information, a merge index in the case of a merge mode, and a reference picture index and information on a differential motion vector in the case of an AMVP mode) is encoded. Also, the entropy encoder 155 encodes information related to quantization, that is, information about a quantization parameter and information about a quantization matrix.

The inverse quantization unit 160 inverse quantizes the quantized transform coefficients output from the quantization unit 145 to generate transform coefficients. The inverse transform unit 165 restores the residual block by transforming the transform coefficients output from the inverse quantization unit 160 from the frequency domain to the spatial domain.

The addition unit 170 restores the current block by adding the reconstructed residual block to the prediction block generated by the prediction unit 120 . Pixels in the reconstructed current block are used as reference pixels when intra-predicting the next block.

The loop filter unit 180 reconstructs pixels to reduce blocking artifacts, ringing artifacts, blurring artifacts, etc. generated due to block-based prediction and transformation/quantization. filter on them. The filter unit 180 may include all or a part of a deblocking filter 182, a sample adaptive offset (SAO) filter 184, and an adaptive loop filter (ALF) 186 as an in-loop filter. .

The deblocking filter 182 filters the boundary between the reconstructed blocks in order to remove a blocking artifact caused by block-by-block encoding/decoding, and the SAO filter 184 and alf 186 deblocking filtering Additional filtering is performed on the captured image. The SAO filter 184 and the alf 186 are filters used to compensate for the difference between the reconstructed pixel and the original pixel caused by lossy coding. The SAO filter 184 improves encoding efficiency as well as subjective image quality by applying an offset in units of CTUs. On the other hand, the ALF 186 performs block-by-block filtering, and compensates for distortion by applying different filters by classifying the edge of the corresponding block and the degree of change. Information on filter coefficients to be used for ALF may be encoded and signaled to an image decoding apparatus.

The restored block filtered through the deblocking filter 182 , the SAO filter 184 , and the ALF 186 is stored in the memory 190 . When all blocks in one picture are reconstructed, the reconstructed picture may be used as a reference picture for inter prediction of blocks in a picture to be encoded later.

5 is an exemplary block diagram of an image decoding apparatus capable of implementing the techniques of the present disclosure. Hereinafter, an image decoding apparatus and sub-components of the apparatus will be described with reference to FIG. 5 .

The image decoding apparatus includes an entropy decoding unit 510, a reordering unit 515, an inverse quantization unit 520, an inverse transform unit 530, a prediction unit 540, an adder 550, a loop filter unit 560, and a memory ( 570) may be included.

Like the image encoding apparatus of FIG. 1 , each component of the image decoding apparatus may be implemented as hardware or software, or a combination of hardware and software. In addition, the function of each component may be implemented as software and the microprocessor may be implemented to execute the function of software corresponding to each component.

The entropy decoding unit 510 decodes the bitstream generated by the image encoding apparatus and extracts information related to block division to determine a current block to be decoded, and prediction information and residual signal required to reconstruct the current block. extract information, etc.

The entropy decoder 510 extracts information on the CTU size from a sequence parameter set (SPS) or a picture parameter set (PPS) to determine the size of the CTU, and divides the picture into CTUs of the determined size. Then, the CTU is determined as the uppermost layer of the tree structure, that is, the root node, and the CTU is divided using the tree structure by extracting division information on the CTU.

For example, when a CTU is split using the QTBTTT structure, a first flag (QT_split_flag) related to QT splitting is first extracted and each node is split into four nodes of a lower layer. And, for the node corresponding to the leaf node of QT, the second flag (MTT_split_flag) related to the split of MTT and the split direction (vertical / horizontal) and / or split type (binary / ternary) information are extracted and the corresponding leaf node is set to MTT split into structures. Accordingly, each node below the leaf node of the QT is recursively divided into a BT or TT structure.

As another example, when a CTU is split using the QTBTTT structure, a CU split flag (split_cu_flag) indicating whether a CU is split is extracted first, and when the block is split, a first flag (QT_split_flag) is extracted. may be In the partitioning process, each node may have zero or more repeated MTT splits after zero or more repeated QT splits. For example, in the CTU, MTT division may occur immediately, or conversely, only multiple QT divisions may occur.

As another example, when a CTU is split using a QTBT structure, a first flag (QT_split_flag) related to QT splitting is extracted and each node is split into four nodes of a lower layer. And, for a node corresponding to a leaf node of QT, a split flag (split_flag) indicating whether to further split into BT and split direction information are extracted.

On the other hand, when the entropy decoding unit 510 determines a current block to be decoded by using the tree structure division, information on a prediction type indicating whether the current block is intra-predicted or inter-predicted is extracted. When the prediction type information indicates intra prediction, the entropy decoder 510 extracts a syntax element for intra prediction information (intra prediction mode) of the current block. When the prediction type information indicates inter prediction, the entropy decoding unit 510 extracts a syntax element for the inter prediction information, that is, information indicating a motion vector and a reference picture referenced by the motion vector.

Also, the entropy decoding unit 510 extracts quantization-related information and information on quantized transform coefficients of the current block as information on the residual signal.

The reordering unit 515 re-orders the sequence of one-dimensional quantized transform coefficients entropy-decoded by the entropy decoding unit 510 in a reverse order of the coefficient scanning order performed by the image encoding apparatus into a two-dimensional coefficient array (that is, block) can be changed.

The inverse quantization unit 520 inversely quantizes the quantized transform coefficients and inversely quantizes the quantized transform coefficients using the quantization parameter. The inverse quantizer 520 may apply different quantization coefficients (scaling values) to the two-dimensionally arranged quantized transform coefficients. The inverse quantizer 520 may perform inverse quantization by applying a matrix of quantization coefficients (scaling values) from the image encoding apparatus to a two-dimensional array of quantized transform coefficients.

The inverse transform unit 530 inversely transforms the inverse quantized transform coefficients from the frequency domain to the spatial domain to reconstruct residual signals to generate a residual block for the current block.

In addition, when the inverse transform unit 530 inversely transforms only a partial region (subblock) of the transform block, a flag (cu_sbt_flag) indicating that only the subblock of the transform block has been transformed, and vertical/horizontal information (cu_sbt_horizontal_flag) of the subblock ) and/or subblock position information (cu_sbt_pos_flag), and by inversely transforming the transform coefficients of the corresponding subblock from the frequency domain to the spatial domain, the residual signals are restored. By filling in , the final residual block for the current block is created.

In addition, when MTS is applied, the inverse transform unit 530 determines a transform function or a transform matrix to be applied in the horizontal and vertical directions, respectively, using the MTS information (mts_idx) signaled from the image encoding apparatus, and uses the determined transform function. Inverse transform is performed on transform coefficients in the transform block in the horizontal and vertical directions.

The predictor 540 may include an intra predictor 542 and an inter predictor 544 . The intra prediction unit 542 is activated when the prediction type of the current block is intra prediction, and the inter prediction unit 544 is activated when the prediction type of the current block is inter prediction.

The intra prediction unit 542 determines the intra prediction mode of the current block from among the plurality of intra prediction modes from the syntax elements for the intra prediction mode extracted from the entropy decoding unit 510, and references the vicinity of the current block according to the intra prediction mode. Predict the current block using pixels.

The inter prediction unit 544 determines a motion vector of the current block and a reference picture referenced by the motion vector by using the syntax element for the inter prediction mode extracted from the entropy decoding unit 510, and divides the motion vector and the reference picture. is used to predict the current block.

The adder 550 reconstructs the current block by adding the residual block output from the inverse transform unit and the prediction block output from the inter prediction unit or the intra prediction unit. Pixels in the reconstructed current block are used as reference pixels when intra-predicting a block to be decoded later.

The loop filter unit 560 may include a deblocking filter 562 , an SAO filter 564 , and an ALF 566 as an in-loop filter. The deblocking filter 562 deblocks and filters the boundary between the reconstructed blocks in order to remove a blocking artifact caused by block-by-block decoding. The SAO filter 564 and the ALF 566 perform additional filtering on the reconstructed block after deblocking filtering in order to compensate for the difference between the reconstructed pixel and the original pixel caused by lossy coding. The filter coefficients of the ALF are determined using information about the filter coefficients decoded from the non-stream.

The restored block filtered through the deblocking filter 562 , the SAO filter 564 , and the ALF 566 is stored in the memory 570 . When all blocks in one picture are reconstructed, the reconstructed picture is used as a reference picture for inter prediction of blocks in a picture to be encoded later.

This embodiment relates to encoding and decoding of an image (video) as described above. More specifically, video coding that models a noise model or a subjective image quality model for the current video, pre-processing and post-processing an image based on the model, and signaling information of the model Methods and apparatus are provided.

In the following description, in order to express that the image encoding/decoding apparatus includes pre-processing and post-processing, for convenience, the example of FIG. 1 is shown as an encoder, and the illustration of FIG. 5 is shown as a decoder. . Accordingly, the image encoding apparatus may include components performing the pre-processing process and an encoder, and the image decoding apparatus may include components performing the post-processing process, and a decoder.

<Example 1> Pre-processing/post-processing method using a noise model

6 is an exemplary diagram illustrating an image encoding apparatus and an image decoding apparatus including pre-processing and post-processing according to an embodiment of the present disclosure.

The image encoding apparatus pre-processes the noise of the input image, encodes the noise-preprocessed input image to generate a bitstream, and then transmits the generated bitstream to the image decoding apparatus. The image encoding apparatus includes all or part of an encoder 600 , a denoiser 602 , a noise analyzer 604 , a noise estimator 606 , and a differencer 608 . After generating a reconstructed image from the bitstream, the image decoding apparatus may apply post-processed noise to the reconstructed image. The image decoding apparatus includes all or a part of a decoder 610 , a noise generator 612 , and a summer 614 .

Hereinafter, the operation of the image encoding apparatus as illustrated in FIG. 6 will be described.

The noise remover 602 generates a denoised image by removing noise added to the input image before the image is input to the encoder 600 . The noise remover 602 may use a noise removal method corresponding to the characteristics of noise added to the input image based on the analysis of the input image.

Alternatively, the noise remover 602 may use a method of removing noise using a predefined pixel operation without analyzing the shape of the noise added to the input image. In this case, the predefined pixel operation may refer to various types of filtering methods such as low-pass filtering, bilateral filtering, and bi-linear filtering. The noise canceller 602 may selectively use one of these various types of filtering methods. In particular, in the bidirectional filtering, unlike the conventional FIR filter that uses a weight dependent on the spatial distance of the pixel, the weight may be changed according to a difference in pixel values in addition to the spatial distance of the pixel. Bidirectional filtering is a representative example of filtering methods that preserve a meaningful boundary even if filtering is performed at the boundary of an object in an image. Hereinafter, the present embodiment proposes a method and apparatus for effectively removing noise by performing bidirectional filtering on an input image before being input to the encoder 600 .

The noise canceller 602 provides the noise-removed image as an input of the encoder 600 . Also, the noise remover 602 may provide an image from which noise is removed to the noise analyzer 604 .

The noise analyzer 604 may obtain an original input image and an image from which noise has been removed, and two channels as inputs. Alternatively, the noise analyzer 604 may acquire a difference image between the original input image and the noise-removed image as an input. In this case, the difference image may be generated by the differencer 608 .

The noise analyzer 604 analyzes the characteristics of noise removed from the original input image by analyzing the two-channel or difference image. Here, the noise characteristic may be a Gaussian distribution, a uniform distribution, or the like.

The noise estimator 606 generates noise parameters removed from the original input image by reflecting the characteristics of the noise analyzed by the noise analyzer 604 . Also, the noise parameter may include a noise generating method corresponding to the noise characteristic. Here, the noise generating method may be used later in an image decoding apparatus. As another embodiment, the noise generating method may be agreed in advance between the image encoding apparatus and the decoding apparatus according to noise characteristics.

Meanwhile, the noise parameter is provided to the noise remover 602 , and the noise remover 602 may use a noise removal method corresponding to the characteristics of the noise.

The encoder 600 encodes an image from which noise has been removed to generate a bitstream. In this case, the bitstream may include the encoding result of the noise parameter. The image encoding apparatus may transmit the bitstream to the image decoding apparatus.

Hereinafter, an operation of the image decoding apparatus as illustrated in FIG. 6 will be described.

The decoder 610 generates a reconstructed image from the bitstream. As described above, the bitstream may include a noise parameter generated by the image encoding apparatus.

Meanwhile, the noise parameter may be transmitted from the image encoding apparatus to the image decoding apparatus while being included in an independent bitstream such as supplemental enhancement information (SEI) and video usability information (VUI).

The decoded noise parameter is provided to a noise generator 612 . As described above, the noise parameter may include a noise generation method. Alternatively, a method for generating noise previously agreed between the image encoding apparatus and the decoding apparatus may be used.

The noise generator 612 generates noise based on a noise parameter using a noise generating method.

The image decoding apparatus may generate a final reconstructed image by using the generated noise and the reconstructed image. In this case, as illustrated in FIG. 6 , the noise may be added to the reconstructed image in the form of an offset by the summer 614 , or by applying a predefined filtering to the reconstructed image to generate a final reconstructed image, noise can be added.

7 is an exemplary diagram illustrating a bidirectional filter used for pre-processing and post-processing according to an embodiment of the present disclosure.

As an embodiment, the size of the bidirectional filter may be a 3×3 filter as illustrated in FIG. 7 . Alternatively, filters of 5×5, 7×7, etc. may also be used in addition to the 3×3 filter.

Meanwhile, for a 3×3 size bidirectional filter, filter coefficients may be composed of weights calculated at each position. As an embodiment, the weight of the bidirectional filter may be expressed as Equation (1).

Here, the first term of the exponential function is determined by the spatial distance of the pixels, and the second term is determined by the luminance of each pixel, that is, the difference between the pixel values according to each position.

In Equation 1, the weight w(i, j, k, m) of the bidirectional filter indicates a weight to be applied to the pixel at the (k, m) position in order to apply the filter to the pixel at the (i, j) position. Accordingly, as illustrated in FIG. 7 , the weight is the largest at the center of the bidirectional filter corresponding to i=k and j=m.

Also, the σ _d value in the first term of the exponential function may be determined according to the size of the current block, and may be a different value depending on the encoding method of the current block, that is, intra prediction or inter prediction. In the second term of the exponential function, the σ _r value may be determined by the quantization parameter of the current block.

These sigma values may be directly calculated by the image encoding apparatus and the image decoding apparatus according to Equation (1). Alternatively, it may be signaled from the image encoding apparatus to the image decoding apparatus while being encoded with specific syntax information in the bitstream. In addition, as the size of the filter applied to the pixels in the block according to the weight of the bidirectional filter, one of 3×3, 5×5, and 7×7 as described above may be selected and then used.

<Embodiment 2> Perceptual pre-processing/post-processing method

8 is an exemplary diagram illustrating an image encoding apparatus and an image decoding apparatus including pre-processing and post-processing according to another embodiment of the present disclosure.

The image encoding apparatus analyzes the input image, removes elements that are perceptually removable from the image, encodes the input image from which these elements are removed, generates a bitstream, and transmits it to the image decoding apparatus . The image encoding apparatus includes an encoder 600, a perceptual pre-processor 802, a perceptual model analyzer 804, a perceptual model estimator 806, and a differencer 608. includes all or part of it. After generating a reconstructed image from a bitstream, the image decoding apparatus performs post-processing capable of improving subjective image quality on the reconstructed image. The image decoding apparatus includes all or a part of a decoder 610 , a perceptual enhancer 812 , and a summer 614 .

Hereinafter, the operation of the image encoding apparatus as illustrated in FIG. 8 will be described.

The perceptual quality preprocessor 802 generates an image in which the removable elements are removed by removing elements that can be removed from the viewpoint of perceptual quality added to the input image before the image is input to the encoder 600 . The cognitive quality preprocessor 802 removes the identified removable elements by analyzing the input image based on various subjective image quality measurement models. Also, the cognitive quality preprocessor 802 may remove elements determined to improve encoding efficiency when the corresponding elements are removed.

In this case, elements that can be removed from the viewpoint of cognitive quality mean change elements that are not recognizable in the human visual system when considering human visual characteristics. Examples of such cognitive visual characteristics include a Contrast Sensitivity Function (CSF) effect, a Contrast Masking (CM) or Texture Masking (TM) effect, and a Luminance Adaptation (LA) effect.

The CSF effect means that the characteristics of the human cognitive visual system exhibit the same characteristics as the band-pass filter on the frequency axis. The CSF effect may be classified into a spatial-contrast sensitivity function effect and a temporal-contrast sensitivity function effect according to the type of the frequency axis.

The CM or TM effect indicates a characteristic in which a visual characteristic of an image is changed according to a mask. For example, the human visual system exhibits characteristics such that visibility for distortion is low in a high texture region, and distortion can be easily recognized in a flat or near boundary.

The LA effect refers to a characteristic that distortion in contrast to a luminance region of medium brightness in a dark or bright luminance region is not easily recognized.

On the other hand, there are various cognitive models that reflect the cognitive visual characteristics in terms of cognitive quality and reflect these characteristics in the image. The Just Noticeable Distortion (JND) model is a representative cognitive model. Here, JND represents the minimum error that humans start to visually perceive. The present embodiment proposes a method and apparatus for removing an unrecognizable error element from a subjective image quality point of view by using the perceptual quality preprocessor 802 . As an example, the perceptual quality preprocessor 802 may remove an unrecognizable error element based on the above-described JND model.

The perceptual quality preprocessor 802 provides an image from which an unrecognizable error element has been removed as an input of the encoder 600 . Also, the cognitive quality preprocessor 802 may provide an image from which an unrecognizable error element is removed to the cognitive model analyzer 804 .

The cognitive model analyzer 804 may acquire two channels as an input: an original input image and an image from which unrecognizable error elements are removed. Alternatively, the cognitive model analyzer 804 may acquire, as an input, a difference image between the original input image and an image from which unrecognizable error elements are removed. In this case, the difference image may be generated by the differencer 608 .

The cognitive model analyzer 804 predicts whether there are removable elements in the original input image from the viewpoint of cognitive quality and an improvement in encoding efficiency when the removable elements are removed. In addition, the cognitive model analyzer 804 analyzes the two-channel or difference image to determine the characteristics of the unrecognizable error element removed from the original input image.

According to the characteristic of the unrecognizable error element, the cognitive model analyzer 804 may determine a dominant characteristic among the aforementioned cognitive visual characteristics, and select a model suitable for responding to the dominant characteristic as the cognitive model. As described above, the JND model may be selected as the cognitive model.

The cognitive model estimator 806 generates parameters of the cognitive model selected by the cognitive model analyzer 804 . For example, when the cognitive model is a JND model, the cognitive model estimator 806 may obtain a threshold value of a pixel value of the input image from the viewpoint of cognitive quality. Here, the threshold represents the maximum value that can change the pixel value of the input image from the viewpoint of perceived quality. Cognitive model parameters may include these thresholds.

Also, the cognitive model estimator 806 may select a picture quality compensation method corresponding to the cognitive model and include it in the cognitive model parameter. Here, the image quality compensation method may be used later in an image decoding apparatus. As another embodiment, the image quality compensation method may be agreed in advance between the image encoding apparatus and the decoding apparatus according to perceptual visual characteristics.

Meanwhile, the cognitive model parameters may be provided to the cognitive quality preprocessor 802 . The cognitive quality preprocessor 802 may generate an image in which removable elements are removed from the input image in terms of cognitive quality by using a method corresponding to the cognitive model. For example, when the cognitive model is a JND model, the cognitive quality preprocessor 802 may apply an operation to the input image within the threshold limit described above. Here, the operation may be filtering, a convolutional operation, or changing a pixel value using an offset.

The encoder 600 encodes the preprocessed input image to generate a bitstream. In this case, the bitstream may include parameters of the cognitive model. The image encoding apparatus may transmit the bitstream to the image decoding apparatus.

Hereinafter, an operation of the image decoding apparatus as illustrated in FIG. 8 will be described.

The decoder 610 generates a reconstructed image from the bitstream. As described above, the bitstream may include parameters of the cognitive model generated by the image encoding apparatus.

Meanwhile, the cognitive model parameter may be transmitted from the image encoding apparatus to the image decoding apparatus while being included in an independent bitstream such as SEI and VUI.

The parameters of the decoded cognitive model are provided to the cognitive quality improver 812 . As described above, the parameter of the cognitive model may include a method for improving image quality. Alternatively, an image quality improvement method previously agreed between the image encoding apparatus and the decoding apparatus may be used. When the cognitive model is the JND model, the cognitive model parameter may include a threshold value for changing the pixel value of the reconstructed image from the viewpoint of cognitive quality.

The cognitive quality improver 812 generates an improved image by post-processing the reconstructed image based on the cognitive model parameter in order to improve the subjective quality of the reconstructed image. When the cognitive model is the JND model, the cognitive quality improver 812 may apply an operation to the reconstructed image according to the image quality compensation method within the threshold limit described above. Here, the operation may be a filtering, a convolution operation, or a change of a pixel value using an offset.

The cognitive quality improver 812 may generate an improved image by improving image quality after evaluating the portions in which deterioration occurs in terms of perceived quality. In this case, the cognitive quality improver 812 may divide the reconstructed image into N×N square blocks and post-process the reconstructed image in units of N×N square blocks. That is, the cognitive quality improver 812 applies a single cognitive model to the entire reconstructed image to evaluate the deterioration of the perceptual quality point of view and improve the image quality, or divide the reconstructed image into a plurality of blocks and post-process the reconstructed image in block units. can do.

The image decoding apparatus may generate a final restored image by using the generated improved image and the restored image. In this case, as illustrated in FIG. 8 , the improved image may be added to the reconstructed image in the form of an offset by the summer 614 , or the improved image generated by the cognitive quality improver 812 is the final reconstructed image. it might be

<Example 3> Downsampling and upsampling Pre-processing/post-processing method used

9 is an exemplary diagram illustrating an image encoding apparatus and an image decoding apparatus including pre-processing and post-processing according to another embodiment of the present disclosure.

The image encoding apparatus reduces the resolution of the input image, encodes the reduced resolution input image, generates a bitstream, and transmits the generated bitstream to the image decoding apparatus. The video encoding apparatus includes all or part of an encoder 600 , a down-sampler 902 , and a side information encoder 904 . After generating a reconstructed image from the bitstream, the image decoding apparatus may upsample the reconstructed image. The image decoding apparatus includes all or part of a decoder 610 , a side information decoder 912 , and an upsampler 914 .

Hereinafter, the operation of the image encoding apparatus as illustrated in FIG. 9 will be described.

The downsampler 902 reduces the resolution of the input image before the image is input to the encoder 600 . That is, the downsampler 902 performs an operation of reducing the resolution by using a downsampling filter in order to convert the input image into an image having a width and height of 1/2, 1/4, or the like. For example, unlike the existing method of encoding a 4K image having a resolution of 3840 × 2160, the present embodiment performs downsampling to downsample a 3840 × 2160 image to a 1920 × 1080 image, and encode the downsampled image. Use as input. When the encoder 600 is operated by performing such downsampling, encoding efficiency may be improved.

The peripheral information encoder 904 generates a bitstream by encoding the downsampling operation type and downsampling information, ie, downsampling parameters, used by the downsampling unit 902 for downsampling the original image. Here, the downsampling operation indicates a downsampling filter used when performing downsampling, and the downsampling information indicates an image ratio between an original image and a downsampled image. The peripheral information encoder 904 encodes the downsampling parameter into syntax elements. Also, when cropping is applied to the original image, the peripheral information encoder 904 may additionally encode information on a cropping operation performed before downsampling as a syntax element.

Hereinafter, the operation of the image decoding apparatus as illustrated in FIG. 9 will be described.

The decoder 610 generates a reconstructed image from the bitstream. As described above, the bitstream may include a downsampling parameter generated by the image encoding apparatus.

Meanwhile, the downsampling parameter may be transmitted from the image encoding apparatus to the image decoding apparatus while being included in an independent bitstream such as SEI and VUI. The bitstream including the downsampling parameter is provided to the peripheral information decoder 912 .

The peripheral information decoder 912 decodes the downsampling parameter from the bitstream. Also, the peripheral information decoder 912 obtains upsampling information for upsampling the reconstructed image to the resolution of the original image based on the downsampling parameter. Here, the upsampling information may include an upsampling filter used when performing upsampling, and an image ratio between the downsampled reconstructed image and the original image.

The upsampler 914 generates a final reconstructed image having the resolution of the original image from the reconstructed image based on the upsampling information.

As another embodiment, the upsampler 914 may perform super resolution (SR) to upsample the reconstructed image to the resolution of the original image. Accordingly, the upsampler 914 may use a deep learning-based neural network for performing SR. In this case, the neural network for SR may be composed of a combination of a plurality of convolutional layers, pooling layers, activation function layers, and the like.

Hereinafter, a video encoding method and a video decoding method according to the first realization example will be described with reference to FIGS. 10 and 11 .

10 is an exemplary diagram illustrating an image encoding method including pre-processing according to an embodiment of the present disclosure.

The image encoding apparatus analyzes the input image and analyzes noise characteristics. (S1000). Here, the noise characteristic may be a Gaussian distribution, a uniform distribution, or the like.

The image encoding apparatus estimates the noise parameter (S1002). The image encoding apparatus generates a noise parameter removed from the original input image by reflecting the analyzed characteristics of the noise. Also, the noise parameter may include a noise generating method corresponding to the noise characteristic. Here, the noise generating method may be used later in an image decoding apparatus. As another embodiment, the noise generating method may be agreed in advance between the image encoding apparatus and the decoding apparatus according to noise characteristics.

The image encoding apparatus preprocesses the input image by removing noise from the input image using the noise parameter ( S1004 ). The image encoding apparatus may use a noise removal method corresponding to the characteristics of noise added to the input image based on the analysis of the input image.

Alternatively, a method of removing noise using a predefined pixel operation without analyzing the form of noise added to the input image may be used. In this case, the predefined pixel operation may refer to various types of filtering methods such as low-pass filtering, bi-directional filtering, and bilinear filtering. The image encoding apparatus may selectively use one of these various types of filtering methods.

The image encoding apparatus generates a bitstream by encoding the preprocessed input image (S1006).

The image encoding apparatus encodes a noise parameter and combines it with a bitstream (S1008). The image encoding apparatus may transmit the bitstream to the image decoding apparatus.

11 is an exemplary diagram illustrating an image decoding method including post-processing according to an embodiment of the present disclosure.

The image decoding apparatus generates a restored image by decoding the bitstream (S1100)

The image decoding apparatus decodes the noise parameter from the bitstream (S1102). As described above, the noise parameter may include a noise generation method. Alternatively, a method for generating noise previously agreed between the image encoding apparatus and the decoding apparatus may be used.

Meanwhile, the noise parameter may be transmitted from the image encoding apparatus to the image decoding apparatus while being included in an independent bitstream such as SEI and VUI.

The image decoding apparatus generates noise by post-processing the reconstructed image using the noise parameter (S1104).

The image decoding apparatus generates a final reconstructed image by using the noise and the reconstructed image (S1106). For example, noise may be added to the reconstructed image in the form of an offset, or noise may be added by applying predefined filtering to the reconstructed image to generate a final reconstructed image.

Hereinafter, a video encoding method and a video decoding method according to the second embodiment will be described using the illustrations of FIGS. 12 and 13 .

12 is an exemplary diagram illustrating an image encoding method including pre-processing according to another embodiment of the present disclosure.

The image encoding apparatus analyzes the input image from the viewpoint of cognitive quality and determines removable elements according to the cognitive model (S1200). Here, elements that can be removed from the viewpoint of cognitive quality mean change elements that are not recognizable in the human visual system when considering human visual characteristics. Examples of such cognitive visual characteristics include the CSF effect, the CM or TM effect, and the LA effect.

On the other hand, the cognitive model is a model that reflects the cognitive visual characteristics from the viewpoint of cognitive quality. The JND model is a representative cognitive model. Here, JND represents the minimum error that humans start to visually perceive.

The image encoding apparatus estimates the parameters of the cognitive model (S1202). For example, when the cognitive model is the JND model, the image encoding apparatus may obtain a threshold value of the pixel value of the input image from the viewpoint of perceived quality. Here, the threshold represents the maximum value that can change the pixel value of the input image from the viewpoint of perceived quality. Cognitive model parameters may include these thresholds.

Also, the image encoding apparatus may select a picture quality compensation method corresponding to the cognitive model and include it in the cognitive model parameter. Here, the image quality compensation method may be used later in an image decoding apparatus. As another embodiment, the image quality compensation method may be agreed in advance between the image encoding apparatus and the video decoding apparatus according to perceptual visual characteristics.

The image encoding apparatus pre-processes the input image by removing removable elements from the input image using the parameters of the cognitive model ( S1204 ).

The image encoding apparatus may generate an image in which removable elements are removed from the input image in terms of perceived quality by using a method corresponding to the cognitive model. For example, when the cognitive model is the JND model, the image encoding apparatus may apply the operation to the input image within the above-described threshold limit. Here, the operation may be a filtering, a convolution operation, or a change of a pixel value using an offset.

The image encoding apparatus generates a bitstream by encoding the preprocessed input image (S1206).

The image encoding apparatus encodes the parameters of the cognitive model and combines them with the bitstream (S1208). The image encoding apparatus may transmit the bitstream to the image decoding apparatus.

13 is an exemplary diagram illustrating an image decoding method including post-processing according to another embodiment of the present disclosure.

The image decoding apparatus generates a restored image by decoding the bitstream (S1300)

The image decoding apparatus decodes the parameters of the cognitive model from the bitstream (S1302). Here, the cognitive model is a model that reflects the cognitive visual characteristics in terms of cognitive quality.

Meanwhile, the cognitive model parameter may be transmitted from the image encoding apparatus to the image decoding apparatus while being included in an independent bitstream such as SEI and VUI.

Meanwhile, the parameter of the cognitive model may include a method for improving image quality. Alternatively, an image quality improvement method previously agreed between the image encoding apparatus and the decoding apparatus may be used. When the cognitive model is the JND model, the cognitive model parameter may include a threshold value for changing the pixel value of the reconstructed image from the viewpoint of cognitive quality.

The image decoding apparatus generates an improved image by post-processing the reconstructed image using the parameters of the cognitive model (S1304).

When the cognitive model is the JND model, the image decoding apparatus may apply an operation to the reconstructed image according to the image quality compensation method within the threshold limit described above. Here, the operation may be a filtering, a convolution operation, or a change of a pixel value using an offset.

Alternatively, the image decoding apparatus may generate an improved image by improving image quality after evaluating the portions in which deterioration occurs in terms of perceived quality. In this case, the image decoding apparatus may divide the reconstructed image into N×N square blocks and post-process the reconstructed image in units of N×N square blocks. That is, the image decoding apparatus may apply a single cognitive model to the entire reconstructed image to evaluate deterioration in terms of perceptual quality and improve image quality, or may divide the reconstructed image into a plurality of blocks and post-process the reconstructed image in block units.

The image decoding apparatus generates a final restored image by using the improved image and the restored image (S1306). The image decoding apparatus may output the improved image as a final reconstructed image or may output the improved image and the reconstructed image as the final reconstructed image by adding the reconstructed image.

Although it is described that each process is sequentially executed in the flowchart/timing diagram of the present specification, this is merely illustrative of the technical idea of an embodiment of the present disclosure. In other words, one of ordinary skill in the art to which an embodiment of the present disclosure pertains changes the order described in the flowchart/timing diagram within a range that does not deviate from the essential characteristics of an embodiment of the present disclosure, or performs one of each process Since it will be possible to apply various modifications and variations by executing the above process in parallel, the flowchart/timing diagram is not limited to a time-series order.

It should be understood that the exemplary embodiments in the above description may be implemented in many different ways. The functions or methods described in one or more examples may be implemented in hardware, software, firmware, or any combination thereof. It should be understood that the functional components described herein have been labeled "...unit" to particularly further emphasize their implementation independence.

Meanwhile, various functions or methods described in this embodiment may be implemented as instructions stored in a non-transitory recording medium that can be read and executed by one or more processors. The non-transitory recording medium includes, for example, any type of recording device in which data is stored in a form readable by a computer system. For example, the non-transitory recording medium includes a storage medium such as an erasable programmable read only memory (EPROM), a flash drive, an optical drive, a magnetic hard drive, and a solid state drive (SSD).

The above description is merely illustrative of the technical idea of this embodiment, and various modifications and variations will be possible without departing from the essential characteristics of the present embodiment by those of ordinary skill in the art to which this embodiment belongs. Accordingly, the present embodiments are intended to explain rather than limit the technical spirit of the present embodiment, and the scope of the technical spirit of the present embodiment is not limited by these embodiments. The protection scope of this embodiment should be interpreted by the claims below, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present embodiment.

600: encoder
610: decoder
802: cognitive quality preprocessor
804: Cognitive model analyzer
806: Cognitive model estimator
812: Cognitive quality improver

Claims (17)

In the image decoding method performed by the image decoding apparatus,
generating a restored image by decoding the bitstream;
decoding a parameter of a perceptual model from the bitstream, wherein the perceptual model is a model reflecting perceptual visual characteristics in terms of perceptual quality;
generating an improved image by post-processing the reconstructed image using the parameters of the cognitive model; and
generating a final reconstructed image using the improved image and the reconstructed image
A video decoding method comprising a. According to claim 1,
The cognitive visual characteristics are
An image decoding method comprising all or a part of a Contrast Sensitivity Function (CSF) effect, a Contrast Masking (CM) effect, a Texture Masking (TM) effect, and a Luminance Adaptation (LA) effect. The method of claim 1,
The cognitive model is
The image decoding method, characterized in that the model is selected to be suitable to correspond to a dominant characteristic among the cognitive and visual characteristics. The method of claim 1,
The parameters of the cognitive model are,
and a method for compensating for image quality corresponding to the cognitive model. 5. The method of claim 4,
The cognitive model is
A Just Noticeable Distortion (JND) model, wherein the JND is a minimum error at which a human starts to visually perceive, an image decoding method. 6. The method of claim 5,
The parameters of the cognitive model are,
When the cognitive model is the JND model, further comprising a threshold value of the pixel value of the reconstructed image from the viewpoint of the perceived quality, wherein the threshold represents a maximum value that can change the pixel value of the restored image from the viewpoint of the perceived quality Characterized in , video decoding method. 7. The method of claim 6,
The step of generating the improved image includes:
Within the threshold limit, an operation is applied to the reconstructed image according to the image quality compensation method, wherein the operation is a pixel value change using filtering, a convolution operation, or an offset. According to claim 1,
The step of generating the improved image includes:
The image decoding method, characterized in that the reconstructed image is divided into N×N square blocks, and the reconstructed image is post-processed in units of N×N square blocks. According to claim 1,
The step of generating the final reconstructed image comprises:
and outputting the improved image as the final reconstructed image or outputting the reconstructed image as the final reconstructed image by adding the improved image and the reconstructed image. A decoder that decodes a bitstream to generate a reconstructed image, and decodes a cognitive model-based image quality improvement method and a parameter of the cognitive model from the bitstream, wherein the cognitive model is a model that reflects cognitive visual characteristics in terms of cognitive quality lim;
a cognitive quality improver for generating an improved image by post-processing the reconstructed image using the image quality improvement method and parameters of the cognitive model; and
A summer for generating a final reconstructed image by using the improved image and the reconstructed image
A video decoding apparatus comprising a. In the video encoding method performed by the video encoding apparatus,
determining removable elements according to a perceptual model by analyzing the input image from the viewpoint of cognitive quality, wherein the cognitive model is a model reflecting the cognitive visual characteristics from the viewpoint of cognitive quality;
estimating parameters of the cognitive model;
pre-processing the input image by removing the removable elements from the input image using the parameters of the cognitive model;
generating a bitstream by encoding the preprocessed input image; and
encoding the parameters of the cognitive model and combining them with the bitstream;
A video encoding method comprising a. 12. The method of claim 11,
The cognitive visual characteristics are
An image encoding method comprising a Contrast Sensitivity Function (CSF) effect, a Contrast Masking (CM) effect, a Texture Masking (TM) effect, and a Luminance Adaptation (LA) effect. 12. The method of claim 11,
The determining step is
The method of claim 1, wherein a dominant characteristic among the cognitive and visual characteristics is determined by analyzing the input image, and a model suitable to correspond to the dominant characteristic is selected as the cognitive model. 12. The method of claim 11,
The cognitive model is
A Just Noticeable Distortion (JND) model, wherein the JND is a minimum error at which a human starts to visually perceive, an image encoding method. 15. The method of claim 14,
The estimating step is
a picture quality compensation method corresponding to the cognitive model is selected, and when the cognitive model is the JND model, a threshold value of the pixel value of the input image is derived from the viewpoint of the perception quality, wherein the threshold value is the input value from the viewpoint of the perception quality An image encoding method , characterized in that it represents a maximum value that can change a pixel value of an image. 15. The method of claim 14,
The pre-processing step is
Within the threshold limit, an operation is applied to the input image, wherein the operation is a change of a pixel value using a filtering, a convolution operation, or an offset. 16. The method of claim 15,
The parameters of the cognitive model are,
An image encoding method comprising the threshold value and the image quality compensation method.

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