Image encoding / decoding method and device

Abstract

The present invention relates to an image encoding / decoding method and device. The image decoding method according to an embodiment of the present invention may comprise the steps of: acquiring a bitstream including an encoded syntax element; determining a context model for the encoded syntax element; determining a probability model on the basis of the context model; decoding the encoded syntax element on the basis of the probability model; and determining whether to update the probability model.

Description

Video encoding / decoding method and device

[0001] The present invention relates to a video encoding / decoding method and device, and more particularly, to a video encoding / decoding method and device for improving the processing rate of CABAC (Context Adaptive Binary Arithmetic Coding).

[0002] Recently, the demand for multimedia data, such as video, has been rapidly increasing. In particular, the demand for high-resolution, high-quality video, such as HD (High Definition) and UHD (Ultra High Definition) video, is growing across a wide range of applications. High-resolution, high-quality video data typically requires significantly more data volume than conventional video data. Consequently, the transmission and storage costs for storing and / or transmitting high-resolution, high-quality video data increase compared to conventional video data.

[0003] To solve these problems, high-efficiency image encoding / decoding technology for images with higher resolution and quality is required.

[0004] In order to encode an image, various techniques are used, such as an intra prediction technique that predicts the pixel values ​​included in the current picture using pixel information in the current picture, an intra prediction technique that predicts the pixel values ​​included in the current picture from the pictures before or after the current picture, a transform and quantization technique for compressing the energy of the residual signal, which is the difference between the predicted signal and the original signal, and an entropy coding technique that assigns short codes to values ​​with high appearance frequencies and long codes to values ​​with low appearance frequencies. In addition, various tools are being developed to implement each technique in order to improve the efficiency of image coding. In addition, in order to decode an encoded image, the image can be restored and reproduced through an image decoding technique that uses a technique and tools corresponding to the image coding technique.

[0005] Using these video encoding and decoding technologies, video data can be effectively compressed, transmitted, stored, and played back.

[0006] The present disclosure aims to provide a video encoding / decoding method and device that improves the inefficiency of CABAC (Context Adaptive Binary Arithmetic Coding) and enhances the compression efficiency of video data.

[0007] In addition, the present disclosure aims to provide a video encoding / decoding method and device that improves the compression efficiency of video data by improving the algorithm of the encoding / decoding process.

[0008] The technical challenges to be achieved through this disclosure are not limited to the technical challenges mentioned above. Furthermore, other technical challenges not mentioned in this disclosure will be readily apparent to those skilled in the art from this disclosure.

[0009] An image decoding method according to one embodiment of the present invention may include a step of obtaining a bitstream including an encoded syntax element, a step of determining a context model for the encoded syntax element, a step of determining a probability model based on the context model, a step of decoding the encoded syntax element based on the probability model, and a step of determining whether to update the probability model.

[0010] In the above image decoding method, whether or not to update the probability model can be determined based on the syntax element.

[0011] In the above image decoding method, whether or not to update the probability model can be determined based on the number of bins of the syntactic elements to be decoded.

[0012] In the above image decoding method, whether or not the probability model is updated can be determined based on the continuity of the bins of the syntactic elements to be decoded.

[0013] In the above image decoding method, whether or not to update the probability model can be determined according to a preset prediction unit.

[0014] In the above image decoding method, if a predetermined number or more of syntactic elements of a preset prediction unit are decoded, the probability model may not be updated.

[0015] In the above image decoding method, the context model for the encoded syntax element is determined from among a predetermined number of context models, and the predetermined number may be less than the number of context models used in regular coding of the syntax element.

[0016] In the above image decoding method, the predetermined number can be determined based on the syntax elements.

[0017] In the above image decoding method, the predetermined number can be determined based on the number of bins of the syntactic elements to be decoded.

[0018] In the above image decoding method, the predetermined number can be determined based on the continuity of the bins of the syntactic elements to be decoded.

[0019] In the above image decoding method, the predetermined number can be determined according to a preset prediction unit.

[0020] In the above image decoding method, when a predetermined number or more of syntactic elements of a preset prediction unit are decoded, a context model for the encoded syntactic element can be determined from among a predetermined number of context models.

[0021] An image encoding method according to one embodiment of the present invention may include a step of binarizing a syntax element, a step of determining a context model for the binarized syntax element, a step of determining a probability model based on the context model, a step of encoding the binarized syntax element based on the probability model, and a step of determining whether to update the probability model.

[0022] A non-transitory computer-readable recording medium storing a bitstream generated by an image encoding method according to one embodiment of the present invention can store a bitstream generated by an image encoding method including the steps of binarizing a syntax element, determining a context model for the binarized syntax element, determining a probability model based on the context model, encoding the binarized syntax element based on the probability model, and determining whether to update the probability model.

[0023] A method for transmitting a bitstream generated by an image encoding method according to one embodiment of the present invention comprises the steps of transmitting the bitstream, and can transmit a bitstream generated by the image encoding method, including the steps of binarizing a syntax element, determining a context model for the binarized syntax element, determining a probability model based on the context model, encoding the binarized syntax element based on the probability model, and determining whether to update the probability model.

[0024] According to the present disclosure, a video encoding / decoding method and device can be provided that improve the inefficiency of CABAC (Context Adaptive Binary Arithmetic Coding) and enhance the compression efficiency of video data.

[0025] In addition, according to the present disclosure, a method and device for encoding / decoding an image can be provided that improves the compression efficiency of image data by improving an information reference process in an encoding / decoding process.

[0026] In addition, according to the present disclosure, a video encoding / decoding method and device can be provided that improves the compression efficiency of video data by improving the algorithm of the encoding / decoding process.

[0027] In addition, according to the present disclosure, a recording medium storing a bitstream generated by the image encoding method or device of the present invention can be provided.

[0028] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art to which the present invention pertains from the description below.

[0029] FIG. 1 is a block diagram showing an image encoding device according to one embodiment of the present invention.

[0030] FIG. 2 is a block diagram showing an image decoding device according to one embodiment of the present invention.

[0031] FIG. 3 is a diagram schematically showing a video coding system to which the present invention can be applied.

[0032] FIG. 4 is a drawing exemplarily showing a content streaming system to which an embodiment according to the present invention can be applied.

[0033] Figure 5 is a diagram illustrating a CABAC encoding structure for encoding one syntax element.

[0034] Figure 6 is a diagram illustrating a CABAC decoding structure for decoding one syntax element.

[0035] FIG. 7 is a diagram illustrating a low-complexity CABAC encoding structure for encoding one syntax element according to one embodiment of the present disclosure.

[0036] FIG. 8 is a diagram illustrating a low-complexity CABAC decoding structure for decoding one syntax element according to one embodiment of the present disclosure.

[0037] FIG. 9 is a flowchart illustrating an image decoding method for a low-complexity CABAC method according to an embodiment of the present disclosure.

[0038] FIG. 10 is a flowchart illustrating an image encoding method for a low-complexity CABAC method according to an embodiment of the present disclosure.

[0039] The present invention is susceptible to various modifications and embodiments. Specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, but rather to encompass all modifications, equivalents, and alternatives falling within the spirit and technical scope of the present invention. Throughout the description of each drawing, similar reference numerals have been used to designate similar components.

[0040] While terms such as "first" and "second" may be used to describe various components, these components should not be limited by these terms. These terms are used solely to distinguish one component from another. For example, without departing from the scope of the present invention, a first component may be referred to as a "second component," and similarly, a second component may also be referred to as a "first component." The term "and / or" includes a combination of multiple related items described herein or any of multiple related items described herein.

[0041] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components intervening. Conversely, when a component is referred to as being "directly connected" or "connected" to another component, it should be understood that there are no other components intervening.

[0042] The terminology used in this application is only used to describe specific embodiments and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, it should be understood that the terms "comprise" or "have" indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0043] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. Hereinafter, identical components in the drawings will be designated by the same reference numerals, and redundant descriptions of identical components will be omitted.

[0044] FIG. 1 is a block diagram showing an image encoding device according to one embodiment of the present invention.

[0045] Referring to FIG. 1, an image encoding device (100) may include an image segmentation unit (101), an intra prediction unit (102), an inter prediction unit (103), a subtraction unit (104), a transformation unit (105), a quantization unit (106), an entropy encoding unit (107), an inverse quantization unit (108), an inverse transformation unit (109), an addition unit (110), a filter unit (111), and a memory (112).

[0046] Each component shown in Fig. 1 is independently depicted to indicate different characteristic functions in the video encoding device, and does not mean that each component is composed of separate hardware or a single software component. That is, each component is listed and included as a separate component for convenience of explanation, and at least two components among each component may be combined to form a single component, or one component may be divided into multiple components to perform a function, and such integrated and separate embodiments of each component are also included in the scope of the present invention as long as they do not deviate from the essence of the present invention.

[0047] Additionally, some components may not be essential components that perform essential functions of the present invention, but may be optional components merely used to enhance performance. The present invention may be implemented by including only components essential to implementing the essence of the present invention, excluding components used solely for performance enhancement. A structure that includes only essential components, excluding optional components used solely for performance enhancement, is also within the scope of the present invention.

[0048] The image segmentation unit (101) can segment an input image into at least one block. At this time, the input image can have various shapes and sizes such as a sequence, a picture, a slice, a tile, a segment, a tile group, a coding tree unit, etc. According to another embodiment, the image segmentation unit (101) can segment one input picture into a plurality of sub-pictures defined as a group of rectangular slices, segment each sub-picture into the tiles / slices, and segment the tiles / slices into coding tree units.

[0049] In addition, the image segmentation unit (101) can recursively segment the segmented coding tree unit. The terminal node segmented from the coding tree unit may be referred to as a coding unit (CU). The block may mean a coding unit (CU), or a prediction unit (PU) or a transformation unit (TU) segmented from the coding unit (CU). The segmentation may be performed based on at least one of a quadtree, a binary tree, and a ternary tree. The quadtree is a method of segmenting an upper block into lower blocks whose width and height are half of those of the upper block. The binary tree is a method of segmenting an upper block into lower blocks whose width or height is half of that of the upper block. The ternary tree is a method of segmenting an upper block into three lower blocks. For example, the three lower blocks may be obtained by segmenting the width or height of the upper block at a ratio of 1:2:1. Through the binary tree-based partitioning described above, blocks can have not only square but also non-square shapes. Blocks can first be partitioned into a quad tree. Blocks corresponding to leaf nodes of the quad tree can be left unpartitioned, or can be partitioned into a binary tree or a ternary tree. Leaf nodes of the binary tree or ternary tree can be units of encoding, prediction, and / or transformation.

[0050] The image segmentation unit (101) can recursively segment the CTU into not only a quad tree (QT) but also a multi-type tree (MTT). Here, the MTT can be composed of a binary tree (BT) and a triple tree (TT). For example, the MTT structure can be divided into a vertical binary tree segmentation mode (SPLIT_BT_VER), a horizontal binary tree segmentation mode (SPLIT_BT_HOR), a vertical ternary tree segmentation mode (SPLIT_TT_VER), and a horizontal ternary tree segmentation mode (SPLIT_TT_HOR).

[0051] In addition, the image segmentation unit (101) can segment a CTU by applying a dual tree that uses different CTU segmentation structures for luminance and chrominance components, or by applying a single tree in which luminance and chrominance CTBs (Coding Tree Blocks) within a CTU share a coding tree structure.

[0052] The prediction unit (102, 103) may include an intra-prediction unit (102) that performs intra-prediction and an inter-prediction unit (103) that performs inter-prediction. The prediction unit (102, 103) may determine whether to use intra-prediction or inter-prediction for a prediction unit. In addition, the prediction unit (102, 103) may determine specific information (e.g., intra-prediction mode, inter-prediction mode, motion vector, reference picture, etc.) according to the determined prediction method. At this time, the processing unit where the prediction is performed and the processing unit where the prediction method and specific contents are determined may be different. For example, the prediction unit (102, 103) may determine the prediction method and prediction mode for each prediction unit, and perform prediction according to the transformation unit.

[0053] In another embodiment, the prediction unit may encode the input image using a third mode (e.g., IBC mode, Palette mode, etc.) other than the intra mode and the inter mode. However, if the third mode has functional characteristics similar to the intra mode or the inter mode, the third mode may be classified as the intra mode or the inter mode. In this disclosure, the third mode will be described only when a specific description thereof is required.

[0054] The intra prediction unit (102) can generate a prediction block of the current block based on the intra prediction mode of the current block and reference pixel information around the current block, which is pixel information within the current picture. If a neighboring block of the current block is predicted by inter prediction, the reference pixels included in the inter-predicted neighboring block can be replaced with reference pixels within another neighboring block that has been intra-predicted. That is, if a reference pixel is not available, the intra prediction unit (102) can perform intra prediction of the current block by replacing the unavailable reference pixel with at least one reference pixel among the available reference pixels.

[0055] The intra prediction unit (102) can use multiple reference pixel lines for intra prediction of the current block. If multiple reference pixel lines are available, information indicating a reference pixel line used for intra prediction among the multiple reference pixel lines can be signaled.

[0056] Intra prediction modes used for intra prediction may include a directional prediction mode that uses reference pixel information according to the prediction direction, and a non-directional mode that does not use directional information. Additionally, the mode for predicting luminance information and the mode for predicting chrominance information may be different, and the intra prediction mode information of the luminance component block or the predicted luminance signal information may be utilized to predict chrominance information.

[0057] Alternatively, the intra prediction unit (102) may perform intra prediction on the current block by applying at least one mode among decoder-side intra mode derivation (DIMD), Occurrence-based intra coding (OBIC), extrapolation filter based intra prediction mode (EIP), template based intra mode derivation (TIMD), spatial geometric partitioning mode (SGPM) mode, intra template matching prediction (IntraTMP), and intra block copy. When the intra prediction mode of the block is a predetermined mode, the intra prediction unit (102) may perform intra prediction on the current block by using a block vector indicating a block other than the current block.

[0058] The intra prediction unit (102) may include a reference sample filter, an interpolation filter, and a DC filter. The reference sample filter is a filter that performs filtering on the reference pixels of the current block and may be adaptively applied depending on the prediction mode, size, shape, and / or whether the reference pixel of the current prediction unit is included in a reference pixel line immediately adjacent to the current block. If the prediction mode of the current block is a mode that does not perform reference pixel filtering, the reference pixel filter may not be applied.

[0059] An interpolation filter is a filter that interpolates and filters prediction samples of the current block, and can be adaptively applied depending on the prediction mode, size, shape, and / or whether the reference pixel of the current prediction unit is included in the reference pixel line immediately adjacent to the current block.

[0060] If the prediction mode of the current block is DC mode, a prediction block can be generated by applying a DC filter.

[0061] In one embodiment, the intra prediction unit (102) may perform intra prediction using a pre-trained neural network (NN) model. For example, the intra prediction unit (102) may derive an intra prediction mode of a block to which the DIMD mode is applied and use a pre-trained neural network model to perform intra prediction.

[0062] The inter prediction unit (103) generates a prediction block using the previously restored reference image stored in the memory (112) and the inter prediction mode and motion information. Here, inter prediction may mean motion prediction or motion compensation.

[0063] The inter prediction unit (103) can set the inter mode of a prediction unit included in an encoding unit to one of the Skip Mode, Merge Mode, and Advanced Motion Vector Prediction (AMVP) mode in order to perform motion prediction and / or motion compensation. Then, the inter prediction unit (103) can perform motion prediction and / or motion compensation for the prediction unit according to the set mode.

[0064] In addition, the inter prediction unit (103) can perform motion prediction and / or motion compensation for a prediction unit by applying the AFFINE mode of sub-PU based prediction, the SbTMVP (Subblock-based Temporal Motion Vector Prediction) mode, and the MMVD (Merge with MVD) mode and the GPM (Geometric Partitioning Mode) mode of PU based prediction based on the inter prediction mode. In addition, the inter prediction unit (103) can perform motion prediction and / or motion compensation for the prediction unit by applying HMVP (History based MVP), PAMVP (Pairwise Average MVP), CIIP (Combined Intra / Inter Prediction), AMVR (Adaptive Motion Vector Resolution), DMVR (Decoder side Motion Vector Refinement), BDOF (Bi-Directional Optical-Flow), PROF (Prediction Refinement With Optical Flow), BCW (Bi-predictive with CU Weights), LIC (Local Illumination Compensation), TM (Template Matching), OBMC (Overlapped Block Motion Compensation), etc. to improve the performance of each mode.

[0065] Here, AFFINE mode can be used in both AMVP and MERGE modes. It is a technique with high encoding efficiency. AFFINE mode can be a prediction mode that uses a 4-parameter affine motion model using two control point motion vectors (CPMV) or a 6-parameter affine motion model using three control point motion vectors. Here, CPMV can be a vector representing an affine motion model of any one of the top left, top right, and bottom left of the current block.

[0066] The motion information may include, for example, a motion vector, a reference picture index, a list 1 prediction flag, a list 0 prediction flag, a half-sample interpolation filter index, a bidirectional prediction weight index, etc.

[0067] According to one embodiment, the inter prediction unit (103) may perform inter prediction using a pre-trained neural network model. For example, the inter prediction unit (103) may synthesize a reference frame using a pre-trained neural network model and perform inter prediction based on the synthesized reference frame.

[0068] A residual block containing residual value information, which is the difference value between the prediction unit generated in the prediction unit (102, 103) and the original block of the prediction unit, can be generated. The generated residual block can be input to the transformation unit (130) and transformed.

[0069] The subtraction unit (104) subtracts the block to be encoded from the prediction block generated by the intra prediction unit (102) or inter prediction unit (103) to generate a residual block of the current block. The residual value (residual block) between the generated prediction block and the original block can be input to the transformation unit (105).

[0070] Additionally, the prediction mode information, motion vector information, etc. used for prediction can be encoded together with the residual value in the entropy encoding unit (107) and transmitted to the decoder. When using a specific encoding mode, it is also possible to encode the original block as is and transmit it to the decoding unit without generating a prediction block through the prediction unit (102, 103).

[0071] The transformation unit (105) can perform a transformation on a residual block including residual data to generate and output a transform coefficient. Here, the transform coefficient may be a coefficient value generated by performing a transformation on the residual block. When the transform skip mode is applied, the transformation unit (105) may also skip the transformation on the residual block.

[0072] The conversion unit (105) can determine a conversion type and a conversion kernel based on at least one of encoding parameters such as the size, color component, and prediction mode of the conversion block, and perform conversion on the conversion block using the determined conversion type and conversion kernel.

[0073] According to one embodiment, the transformation unit (105) may perform transformation on a 4x4 luminance residual block generated as an intra prediction result using a transformation type and transformation kernel according to DST (Discrete Sine Transform), and may perform transformation on the remaining residual blocks using a transformation type and transformation kernel according to DCT (Discrete Cosine Transform).

[0074] According to another embodiment, the transform unit (105) may apply the Multiple Transform Selection (MTS) technology that performs the transform by selectively using several transform types and transform kernels. That is, the transform unit (105) may perform the transform in units of sub-blocks using the Sub-block Transform (SBT) technology. Specifically, the SBT may be applied only to inter-prediction blocks, and the current block may be divided into ½ or ¼ sizes in the vertical or horizontal direction, and the transform may be performed on only one of the blocks. For example, the transform unit (105) may perform the transform on the leftmost or rightmost block among the vertically divided current blocks, and may perform the transform on the topmost or bottommost block among the horizontally divided current blocks.

[0075] According to another embodiment, the transform unit (105) may apply a non-separable primary transform (NSPT) technique that performs the transform by selectively using multiple transform kernels based on the intra prediction mode or the size and / or shape of the block.

[0076] According to another embodiment, the transform unit (105) may apply LFNST (Low Frequency Non-Separable Transform), which is a technology that applies a secondary transform to a residual signal that has been transformed into a frequency domain through DCT or DST. LFNST additionally performs a transform on a 4x4 or 8x8 low-frequency region in the upper left, thereby concentrating the residual coefficients in the upper left.

[0077] The quantization unit (106) can quantize the transform coefficients or residual signals converted to the frequency domain by the transform unit (105) according to a quantization parameter (QP). The quantization parameter can vary depending on the block or the importance of the image. The value produced by the quantization unit (106) can be provided to the dequantization unit (108) and the entropy encoding unit (107).

[0078] The above transformation unit (105) and / or quantization unit (106) may be optionally included in the image encoding device (100). That is, the image encoding device (100) may encode the residual block by performing at least one of transformation or quantization on the residual data of the residual block, or by skipping both transformation and quantization. Even if neither transformation nor quantization is performed in the image encoding device (100), or neither transformation nor quantization is performed, a block that is input to the entropy encoding unit (107) is typically referred to as a transformation block.

[0079] The entropy encoding unit (107) can generate and output a bitstream by performing entropy encoding according to a probability distribution on values ​​output by the quantization unit (106), coding parameter values ​​output during the encoding process, information for decoding an image, etc. Here, the information for decoding an image may include syntax elements, etc.

[0080] Coding parameters may include not only information (flags, indexes, etc.) encoded in an encoding device (100) and signaled to a decoding device (200), such as syntax elements, but also information derived during an encoding process or a decoding process, and may mean information necessary when encoding or decoding an image.

[0081] The entropy encoding unit (107) can encode various information such as coefficient information of a transform block, block type information, prediction mode information, division unit information, prediction unit information, transmission unit information, motion vector information, reference frame information, block interpolation information, and filtering information. The coefficients of a transform block can be encoded in units of sub-blocks within the transform block.

[0082] For encoding the coefficients of a transform block, various syntax elements can be encoded, such as Last_sig, a syntax element indicating the position of the first non-zero coefficient in reverse scan order, Coded_sub_blk_flag, a flag indicating whether there is at least one non-zero coefficient in the subblock, Sig_coeff_flag, a flag indicating whether the coefficient is non-zero, Abs_greater1_flag, a flag indicating whether the absolute value of the coefficient is greater than 1, Abs_greater2_flag, a flag indicating whether the absolute value of the coefficient is greater than 2, and Sign_flag, a flag indicating the sign of the coefficient. The residual value of the coefficient that is not encoded by the above syntax elements alone can be encoded through the syntax element remaining_coeff.

[0083] When entropy coding is applied, a small number of bits are allocated to symbols with a high occurrence probability, and a large number of bits are allocated to symbols with a low occurrence probability, thereby representing the symbols, thereby reducing the size of the bit string for the symbols to be encoded. The input data is entropy encoded. For example, entropy coding can use various coding methods such as Exponential Golomb and CABAC (Context-Adaptive Binary Arithmetic Coding).

[0084] The inverse quantization unit (108) and the inverse transformation unit (109) can inverse quantize the values ​​quantized in the quantization unit (106) and inversely transform the values ​​transformed in the transformation unit (105). The residual values ​​generated in the inverse quantization unit (108) and the inverse transformation unit (109) can be combined with the prediction units predicted through the motion estimation unit, motion compensation unit, and intra prediction unit (102) included in the prediction unit (102, 103) to generate a reconstructed block. The addition unit (110) adds the prediction blocks generated in the prediction units (102, 103) and the residual blocks generated through the inverse transformation unit (109) to generate a reconstructed block.

[0085] The filter unit (111) can apply a deblocking filter, a sample adaptive offset (SAO), an adaptive loop filter (ALF), a bilateral filter (BIF), and a LMCS (Luma Mapping with Chroma Scaling) as a filtering technique, in whole or in part, to a restored sample, restored block, or restored image.

[0086] A deblocking filter can remove block distortion caused by boundaries between blocks in a reconstructed picture. To determine whether to perform deblocking, a deblocking filter can be applied to the current block based on the pixels contained in several columns or rows within the block. When applying a deblocking filter to a block, a strong filter or a weak filter can be applied depending on the required deblocking filtering strength. Furthermore, when applying a deblocking filter, horizontal and vertical filtering can be processed in parallel when performing vertical and horizontal filtering.

[0087] Sample adaptive offset may be a method of correcting the offset from the original image on a sample basis for an image on which deblocking has been performed. The filter unit (111) may use a method of dividing the samples included in the image into a certain number of regions, determining the regions on which the offset is to be performed, and applying the offset to the regions, or a method of applying the offset by considering edge information of each sample. Here, the sample adaptive offset may be at least one of a general sample adaptive offset, a bilateral filter, and a cross-component sample adaptive offset (CCSAO).

[0088] Adaptive Loop Filtering (ALF) can be performed based on the comparison of the filtered restored image with the original image. After dividing the pixels included in the image into predetermined groups, a filter to be applied to each group can be determined, and filtering can be performed differentially for each group. Information regarding whether to apply ALF can be transmitted by luminance signal for each coding unit (CU), and the shape and filter coefficients of the ALF filter to be applied can vary depending on each block. Furthermore, an ALF filter of the same form (fixed form) can be applied regardless of the characteristics of the target block.

[0089] An adaptive loop filter can perform filtering based on a comparison between a reconstructed image and the original image. By dividing the samples contained in the image into predetermined groups and determining the filter to be applied to each group, filtering can be performed differentially for each group. Information regarding whether to apply an adaptive loop filter can be signaled for each coding unit (CU), and the shape and filter coefficients of the adaptive loop filter applied to each block can vary.

[0090] According to one embodiment, the filter unit (111) may filter all or part of a restored sample, restored block, or restored image using a pre-trained neural network model. Specifically, the filter unit (111) may apply an adaptive loop filter to all or part of a restored sample, restored block, or restored image using a pre-trained neural network model.

[0091] The memory (112) can store a restored block or picture produced through the filter unit (111). The memory (112) can include a reference picture buffer. In addition, the restored block or picture stored in the memory (112) can be provided to the prediction unit (102, 103) when performing inter prediction.

[0092] Next, an image decoding device according to one embodiment of the present invention will be described with reference to the drawings.

[0093] FIG. 2 is a block diagram showing an image decoding device (200) according to one embodiment of the present invention.

[0094] Referring to FIG. 2, the image decoding device (200) may include an entropy decoding unit (201), an inverse quantization unit (202), an inverse transformation unit (203), a prediction unit (204, 205), an addition unit (206), a filter unit (207), and a memory (208).

[0095] The image decoding device (200) can receive a bitstream output by the image encoding device (100). The image decoding device (200) can receive a bitstream stored in a computer-readable recording medium, or can receive a bitstream streamed through a wired / wireless transmission medium. The image decoding device (200) can decode the bitstream to generate a restored image or a decoded image, and can output the restored image or the decoded image.

[0096] The entropy decoding unit (201) can generate symbols by performing entropy decoding according to a probability distribution for the bitstream. The generated symbols may include symbols in the form of quantized levels. Here, the entropy decoding method may be the reverse process of the entropy encoding method described above.

[0097] The entropy decoding unit (201) can change a one-dimensional vector-shaped coefficient into a two-dimensional block-shaped coefficient through a transform coefficient scanning method to decode a transform coefficient level (quantized level).

[0098] The entropy decoding unit (201) can perform entropy decoding in a procedure opposite to that of the entropy encoding unit (107) of the video encoding device (100). For example, various methods such as Exponential Golomb and CABAC (Context-Adaptive Binary Arithmetic Coding) can be applied in response to the method performed in the video encoder.

[0099] The entropy decoding unit (201) can decode and obtain various information such as coefficient information of the transform block as described above, block type information, prediction mode information, division unit information, prediction unit information, transmission unit information, motion vector information, reference frame information, block interpolation information, and filtering information.

[0100] The inverse quantization unit (202) performs inverse quantization on a quantized transform block to generate a transform block. It operates substantially the same as the inverse quantization unit (108) of Fig. 1.

[0101] The inverse transform unit (203) performs an inverse transform on the transform block to generate a residual block. At this time, the transform method can be determined based on information regarding the prediction method (inter or intra prediction), the size and / or shape of the block, the intra prediction mode, etc. It operates substantially the same as the inverse transform unit (109) of FIG. 1.

[0102] According to one embodiment, the inverse transform unit (203) may perform inverse transform using a transform type and transform kernel according to DST (Discrete Sine Transform) on the transform coefficient levels of the 4x4 luminance component generated as an intra prediction result, and may perform inverse transform using a transform type and transform kernel according to DCT (Discrete Cosine Transform) on the remaining transform coefficient levels.

[0103] According to another embodiment, the inverse transform unit (203) may apply MTS (Multiple Transform Selection) technology to perform transformation by selectively using multiple transformation kernels.

[0104] According to another embodiment, the inverse transform unit (203) may apply LFNST (Low Frequency Non-Separable Transform), which is a technology that applies a secondary inverse transform to a transform coefficient level inversely transformed through a DCT or DST-based transform type and transform kernel.

[0105] According to another embodiment, the inverse transform unit (203) may apply a non separable primary transform (NSPT) technique that performs inverse transform by selectively using a transform kernel based on the intra prediction mode or the size and / or shape of the block.

[0106] The prediction unit (204, 205) can generate a prediction block based on the prediction block generation related information provided by the entropy decoding unit (201) and the previously decoded block or picture information provided by the memory (208).

[0107] The prediction unit (204, 205) may include an intra prediction unit (204) and an inter prediction unit (205). The prediction unit (204, 205) may receive various information such as prediction unit information input from the entropy decoding unit (201), prediction mode information of the intra prediction method, and motion prediction-related information of the inter prediction method, and may distinguish a prediction unit from a current encoding unit and determine a prediction mode of the prediction unit.

[0108] The intra prediction unit (204) can generate a prediction block of the current block based on the intra prediction mode of the current block and reference pixel information around the current block, which is pixel information within the current picture.

[0109] The intra prediction unit (204) can generate a prediction block based on reference pixel information surrounding the current block, which is pixel information within the current picture. The intra prediction unit (204) can use multiple reference pixel lines for intra prediction. When multiple reference pixel lines are available, the intra prediction unit (204) can obtain information indicating a reference pixel line used for intra prediction from among the multiple reference pixel lines.

[0110] The intra prediction mode used for intra prediction may be a directional prediction mode or a non-directional mode. Furthermore, the mode for predicting luminance information may be different from the mode for predicting chrominance information, and the intra prediction mode information of the luminance component block or the predicted luminance signal information may be utilized to predict chrominance information.

[0111] According to one embodiment, the intra prediction unit (204) can perform intra prediction using a pre-trained neural network model. The intra prediction unit (204) can perform intra prediction using the same neural network model as the intra prediction unit (102) of FIG. 1.

[0112] The intra prediction unit (204) operates substantially the same as the intra prediction unit (102) of FIG. 1.

[0113] The inter prediction unit (205) may perform inter prediction on the current prediction unit based on information included in at least one picture among the previous picture or the subsequent picture of the current picture including the current prediction unit, using information necessary for inter prediction of the current prediction unit provided by the image encoding device (100). Alternatively, inter prediction may be performed based on information of some pre-restored area within the current picture including the current prediction unit.

[0114] The inter prediction unit (205) can set the inter mode of a prediction unit included in an encoding unit to one of the skip mode, merge mode, and advanced motion vector prediction (AMVP) mode in order to perform motion prediction and / or motion compensation. In addition, the inter prediction unit (205) can perform motion compensation on the prediction unit according to the set mode.

[0115] In addition, the inter prediction unit (205) can perform motion compensation for the prediction unit by applying the AFFINE mode of sub-PU based prediction, the SbTMVP (Subblock-based Temporal Motion Vector Prediction) mode, and the MMVD (Merge with MVD) mode and the GPM (Geometric Partitioning Mode) mode of PU based prediction based on the inter prediction mode. In addition, the inter prediction unit (205) can perform motion compensation for the prediction unit by applying the HMVP (History based MVP), the PAMVP (Pairwise Average MVP), the CIIP (Combined Intra / Inter Prediction), the AMVR (Adaptive Motion Vector Resolution), the BDOF (Bi-Directional Optical-Flow), the BCW (Bi-predictive with CU Weights), the LIC (Local Illumination Compensation), the TM (Template Matching), the OBMC (Overlapped Block Motion Compensation), etc. to improve the performance of each mode.

[0116] The motion information may include, for example, a motion vector, a reference picture index, a list 1 prediction flag, a list 0 prediction flag, a half-sample interpolation filter index, a bidirectional prediction weight index, etc.

[0117] According to one embodiment, the inter prediction unit (205) can perform inter prediction using a pre-trained neural network model. The inter prediction unit (205) can perform inter prediction using the same neural network model as the inter prediction unit (103) of FIG. 1.

[0118] The inter prediction unit (205) can operate substantially the same as the inter prediction unit (103) of FIG. 1.

[0119] The addition unit (206) adds the prediction block generated by the intra prediction unit (204) or inter prediction unit (205) and the residual block generated by the inverse transformation unit (203) to generate a restored block. It operates substantially the same as the addition unit (110) of Fig. 1.

[0120] The filter unit (207) can reduce various types of noise occurring in restored blocks. The filter unit (207) can include a deblocking filter, a sample adaptive offset, an adaptive loop filter, a bilateral filter, and an LMCS.

[0121] The filter unit (207) can receive information on whether each filter is applied, information on the strength of the filter, etc. from the image encoding device (100). The filter unit (207) of the image decoding device (200) can receive filter-related information provided from the image encoding device (100) and perform filtering on the corresponding block in the image decoding device (200).

[0122] According to one embodiment, the filter unit (207) may filter all or part of a restored sample, restored block, or restored image using a pre-trained neural network model. Specifically, the filter unit (207) may apply an adaptive loop filter to all or part of a restored sample, restored block, or restored image using a pre-trained neural network model.

[0123] The filter unit (207) can operate substantially the same as the filter unit (111) of FIG. 1.

[0124] The memory (208) can store the restoration block generated by the addition unit (206). For example, the memory (208) can include a reference picture buffer. The memory (208) can operate substantially the same as the memory (112) of FIG. 1.

[0125]

[0126] FIG. 3 is a diagram schematically showing a video coding system to which the present invention can be applied.

[0127] A video coding system according to one embodiment may include an encoding device (10) and a decoding device (20). The encoding device (10) may transmit encoded video and / or image information or data to the decoding device (20) in the form of a file or streaming through a digital storage medium or a network.

[0128] An encoding device (10) according to one embodiment may include an image generating unit (11), an encoding unit (12), and a transmission unit (13). A decoding device (20) according to one embodiment may include a receiving unit (21), a decoding unit (22), and an image reproducing unit (23). The encoding unit (12) may be referred to as a video / image encoding unit, and the decoding unit (22) may be referred to as a video / image decoding unit. The transmission unit (13) may be included in the encoding unit (12). The receiving unit (21) may be included in the decoding unit (22). The image reproducing unit (23) may include a display unit, and the display unit may be configured as a separate device or an external component.

[0129] The image generation unit (11) can obtain a video / image through a process of capturing, synthesizing, or generating a video / image. The image generation unit (11) can include a video / image capture device and / or a video / image generation device. The video / image capture device can include, for example, one or more cameras, a video / image archive including previously captured video / images, etc. The video / image generation device can include, for example, a computer, a tablet, a smartphone, etc., and can (electronically) generate a video / image. For example, a virtual video / image can be generated through a computer, etc., in which case the video / image capture process can be replaced with a process of generating related data.

[0130] The encoding unit (12) can encode the input video / image. The encoding unit (12) can perform a series of procedures such as prediction, transformation, and quantization for compression and encoding efficiency. The encoding unit (12) can output encoded data (encoded video / image information) in the form of a bitstream. The detailed configuration of the encoding unit (12) can be configured in the same manner as the encoding device (100) of FIG. 1 described above.

[0131] The transmission unit (13) can transmit encoded video / image information or data output in the form of a bitstream to the reception unit (21) of the decoding device (20) via a digital storage medium or a network in the form of a file or streaming. The digital storage medium can include various storage media such as USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc. The transmission unit (13) can include an element for generating a media file through a predetermined file format and can include an element for transmission via a broadcasting / communication network. The reception unit (21) can extract / receive the bitstream from the storage medium or network and transmit it to the decoding unit (22).

[0132] The decoding unit (22) can decode video / image by performing a series of procedures such as inverse quantization, inverse transformation, and prediction corresponding to the operation of the encoding unit (12). The detailed configuration of the decoding unit (22) can be configured identically to the decoding device (200) of FIG. 2 described above.

[0133] The image playback unit (23) can render decrypted video / images. The rendered video / images can be displayed through the display unit.

[0134]

[0135] FIG. 4 is a drawing exemplarily showing a content streaming system to which an embodiment according to the present invention can be applied.

[0136] As illustrated in FIG. 4, a content streaming system to which an embodiment of the present invention is applied may largely include a multimedia input device, a media storage, an encoding server, a streaming server, a web server, and a user device.

[0137] The encoding server compresses content input from multimedia input devices such as smartphones, cameras, CCTVs, etc. into digital data, creates a bitstream, and transmits it to the streaming server. Alternatively, the encoding server compresses content already stored in a media storage into digital data, creates a bitstream, and transmits it to the streaming server.

[0138] As another example, if multimedia input devices such as smartphones, cameras, CCTVs, etc. directly generate bitstreams, the encoding server may be omitted.

[0139] The above bitstream can be generated by an image encoding method and / or an image encoding device to which an embodiment of the present invention is applied, and the streaming server can temporarily or non-temporarily store the bitstream during the process of transmitting or receiving the bitstream.

[0140] The streaming server transmits multimedia data to a user device based on a user request via a web server, and the web server can act as an intermediary to inform the user of available services. When the user device requests a desired service from the web server, the web server transmits the request to the streaming server, and the streaming server can transmit multimedia data to the user device. At this time, the content streaming system may include a separate control server, and in this case, the control server may play a role in controlling commands / responses between each device within the content streaming system.

[0141] The streaming server can receive content from a media repository and / or encoding server. For example, when receiving content from the encoding server, the content can be received in real time. In this case, to provide a smooth streaming service, the streaming server can store the bitstream for a certain period of time.

[0142] Examples of the user devices may include mobile phones, smart phones, laptop computers, digital broadcasting terminals, personal digital assistants (PDAs), portable multimedia players (PMPs), navigation devices, slate PCs, tablet PCs, ultrabooks, wearable devices (e.g., smartwatches, smart glasses, HMDs), digital TVs, desktop computers, digital signage, etc.

[0143] Each server within the above content streaming system can be operated as a distributed server, in which case data received from each server can be processed in a distributed manner.

[0144]

[0145] Video encoding and decoding methods are implemented through a hybrid compression framework based on prediction methods, transformation methods, and entropy coding methods. In particular, a probability-based arithmetic coding method has been used to remove redundancy of symbols to be coded. Generally, the arithmetic coding method used in codec technology is binary arithmetic coding that uses only 0 and 1. Specifically, the binary arithmetic coding used in codec technology may be CABAC (Context Adaptive Binary Arithmetic Coding), which is an adaptive binary arithmetic coding that adaptively sets a probability model depending on the surrounding situation to improve compression performance. The CABAC encoding structure and CABAC decoding structure may be as described below, respectively.

[0146]

[0147] Figure 5 is a diagram illustrating a CABAC encoding structure for encoding one syntax element.

[0148] Referring to FIG. 5, a CABAC encoder may include a binarizer and a binary arithmetic coder. The binary arithmetic coder may include a regular coding engine and a bypass coding engine. Furthermore, the CABAC encoder may further include a context modeler for updating the context model of the regular coding engine.

[0149] The CABAC encoding procedure may include a binarization procedure and a binary arithmetic encoding procedure. If a signal that is a syntax element rather than a binary value is input, the input signal can be converted into a binary value through binarization. If the input signal is already a binary value, it can be bypassed without going through binarization. Here, each binary digit 0 or 1 that constitutes a binary value is called a bin. In addition, the bin(s) for one syntax element can represent the value of the corresponding syntax element.

[0150] In the binary arithmetic encoding process, the binarized bins can be input to a regular coding engine or a bypass coding engine. In the CABAC encoding process, the CABAC encoder can determine whether to perform encoding through the regular coding engine or the bypass coding engine, and can switch the coding path.

[0151] The regular coding engine can assign a context model that reflects probability values ​​to binarized bins and encode the binarized bins based on the probability model corresponding to the assigned context model. Furthermore, the regular coding engine, having performed coding for each bin, can update the probability model for that bin. Bins coded by the regular coding engine are called context-coded bins.

[0152] A context model can be assigned and updated for each context-coded (regularly coded) bean, and the context model can be indicated based on a context index (ctxidx) or a context index increment (ctxInc). ctxidx can be derived based on ctxInc. Specifically, for example, a context index indicating a context model for each of the regularly coded bins can be derived as the sum of the context index increment (ctxInc) and the context index (context index offset (ctxIdxOffset). Here, ctxInc can be derived differently for each bean. ctxIdxOffset can be represented as the lowest value of ctxIdx. ctxIdxOffset can generally be determined according to a slice type, and a context model for one syntax element in a slice can be distinguished / derived based on ctxinc.

[0153] The bypass coding engine omits the process of estimating probabilities for input bins and updating the probability model applied to those bins after coding. Instead of assigning context to input bins, the bypass coding engine applies a uniform probability distribution (e.g., 50:50) to the input bins, thereby improving coding speed. Bins coded in this way are called bypass bins.

[0154]

[0155] Figure 6 is a diagram illustrating a CABAC decoding structure for decoding one syntax element.

[0156] Referring to FIG. 6, the CABAC decoder may include a binary arithmetic coder and an inverse binarizer. The binary arithmetic coder may include a regular coding engine and a bypass coding engine. In addition, the CABAC decoder may further include a context modeler for updating the context model of the regular coding engine.

[0157] The CABAC decoding procedure may include a binary arithmetic decoding procedure and a debinarization procedure. The CABAC decoding procedure may determine whether to perform decoding through a regular coding engine or a bypass coding engine, and may switch the coding path.

[0158] The input signal to be decoded by CABAC may be a binarized bin of a bitstream. In the binary arithmetic decoding procedure, the binarized bins may be input to a regular coding engine or a bypass coding engine. The regular coding engine may assign a context model reflecting a probability value for the bin, and decode the bin based on the probability model corresponding to the assigned context model. In addition, the regular coding engine, which has performed decoding for each bin, may update the probability model for the bin. The context model may be assigned and updated for each context-coded (regularly coded) bin, and the context model may be indicated based on ctxidx or ctxInc. ctxidx may be derived based on ctxInc.

[0159] The bypass coding engine omits the process of estimating probabilities for input bins and updating the probability model applied to those bins after coding. Instead of assigning context, it applies a uniform probability distribution (e.g., 50:50) to decode input bins, thereby improving coding speed.

[0160]

[0161] Since CABAC was adopted in the AVC / H.264 standard, entropy coding and decoding methods using CABAC have been used in video encoding and decoding standards. In particular, CABAC demonstrates a higher compression ratio than Huffman coding, a conventional entropy coding method. Therefore, CABAC is used exclusively as an entropy coding method in the HEVC / H.265 and VVC / H.266 standards.

[0162] However, CABAC suffers from a lower throughput compared to Huffman coding. In particular, the low throughput of encoding and decoding operations can be problematic when encoding and / or decoding high-resolution images.

[0163] To improve the throughput of encoding and decoding operations, a bypass coding engine can be used instead of a regular coding engine. However, using a bypass coding engine instead of a regular coding engine can result in a reduced compression ratio. Therefore, the present disclosure proposes a low-complexity CABAC method and device that improves throughput while maintaining the advantages of CABAC.

[0164] The low-complexity CABAC encoding structure and the low-complexity CABAC decoding structure can be as described below.

[0165]

[0166] FIG. 7 is a diagram illustrating a low-complexity CABAC encoding structure for encoding one syntax element according to one embodiment of the present disclosure.

[0167] Referring to FIG. 7, a low-complexity CABAC encoder may include a binarizer and a binary arithmetic coder. The binary arithmetic coder may include a regular coding engine, a simplified regular coding engine, and a bypass coding engine. Furthermore, the CABAC encoder may further include a context modeler for updating the context model of the regular coding engine.

[0168] The CABAC encoding procedure may include a binarization procedure and a binary arithmetic encoding procedure. In the binary arithmetic encoding procedure, the binarized bins may be input to a regular coding engine, a simplified regular coding engine, or a bypass coding engine of a binary arithmetic coder.

[0169] The regular coding engine can assign a context model that reflects the probability value for the bin and encode the bin based on the probability model corresponding to the assigned context model. Furthermore, the regular coding engine, having performed coding for each bin, can update the probability model for that bin.

[0170] The simplified canonical coding engine can assign a context model that reflects a probability value for a given bin and encode the bin based on a probability model corresponding to the assigned context model. Here, the simplified canonical coding engine can encode the bin by simplifying the context model selection process or the probability model update process.

[0171] The bypass coding engine omits the process of estimating probabilities for input bins and updating the probability model applied to those bins after coding. Instead of assigning context, it encodes input bins using a uniform probability distribution (e.g., 50:50), thereby improving coding speed.

[0172] In the CABAC encoding process, a low-complexity CABAC encoder can determine which coding engine among the regular coding engine, the simplified regular coding engine, and the bypass coding engine to perform encoding through, and can switch the coding path.

[0173]

[0174] FIG. 8 is a diagram illustrating a low-complexity CABAC decoding structure for decoding one syntax element according to one embodiment of the present disclosure.

[0175] Referring to FIG. 8, a CABAC decoder may include a binary arithmetic decoder and a debinarizer. The binary arithmetic decoder may include a regular coding engine, a simplified regular coding engine, and a bypass coding engine. Furthermore, the CABAC decoder may further include a context modeler for updating the context model of the regular coding engine.

[0176] The CABAC decoding process may include a binary arithmetic decoding process and a debinarization process. The low-complexity CABAC decoder can determine which coding engine among the regular coding engine, the simplified regular coding engine, and the bypass coding engine to perform decoding through, and can switch the coding path.

[0177] In the binary arithmetic decoding procedure, the binarized bins can be input to a regular coding engine, a simplified regular coding engine, or a bypass coding engine.

[0178] The regular coding engine can assign a context model that reflects the probability value for the corresponding bin and decode the corresponding bin based on the probability model corresponding to the assigned context model. Furthermore, the regular coding engine, having performed coding for each bin, can update the probability model for that bin.

[0179] The simplified canonical coding engine can assign a context model that reflects the probability value for a given bin and encode the bin based on the probability model corresponding to the assigned context model. Here, the simplified canonical coding engine can perform coding for a bin by simplifying the context model selection process or the probability model update process.

[0180] The bypass coding engine omits the process of estimating probabilities for input bins and updating the probability model applied to those bins after coding. Instead of assigning context, it applies a uniform probability distribution (e.g., 50:50) to the input bins, thereby improving coding speed. Bins coded in this way are called bypass bins.

[0181] When a signal that is a syntax element other than a binary value is input, the input signal can be converted to a binary value through binarization. If the input signal is already a binary value, binarization can be bypassed.

[0182]

[0183] A low-complexity CABAC encoder can perform encoding by simplifying operations requiring high throughput during the regular coding process. Furthermore, a low-complexity CABAC decoder can perform decoding by simplifying operations requiring high throughput during the regular coding process. Here, the method for simplifying operations requiring high throughput during the regular coding process can be as follows.

[0184]

[0185] One procedure that directly impacts the throughput of CABAC may be the procedure for updating the probability model. In particular, significant delays can occur during the procedure for updating the probability model. Therefore, according to one embodiment of the present disclosure, the probability model can be updated adaptively in the CABAC procedure.

[0186] Specifically, according to one embodiment of the present disclosure, a binary arithmetic decoder of a low-complexity CABAC decoder may include a bypass coding engine, a regular coding engine, and a simplified regular coding engine. Unlike the regular coding engine, the simplified regular coding engine may not update a probability model during the CABAC decoding process. Therefore, unlike the regular coding engine, the simplified regular coding engine may not require a feedback procedure from a context modeler.

[0187] In contrast, according to another embodiment of the present disclosure, a binary arithmetic decoder may include a bypass coding engine and a regular coding engine. The regular coding engine may determine whether to update the probability model based on additional information transmitted with the bin. If it determines not to update the probability model based on the additional information, the regular coding engine may block information fed back from the context modeler or maintain the probability model unchanged, thereby not performing a probability update.

[0188] According to one embodiment of the present disclosure, a probabilistic model used in a CABAC procedure can be adaptively updated according to the syntax.

[0189] When decoding syntax that requires significant processing in the CABAC process, the decoder may not update the probability model. That is, the decoder performs regular coding using the probability model determined by the initialization result, but omits updating the probability model. Consequently, the complexity of CABAC decoding can be reduced, and the throughput of syntax decoding can be increased.

[0190] For example, when decoding a syntax that uses many types of context models in the decoding process, the decoder may not update the probability model. Here, the syntax that uses many types of context models in the decoding process may be a syntax composed of a predetermined number of bins or more. Alternatively, the syntax that uses many types of context models in the decoding process may be an empirically determined syntax. The encoder and decoder may share information about the empirically determined syntax. Therefore, the encoder and decoder may determine whether to update the probability model based on the information about the syntax.

[0191] Alternatively, an encoder encoding a syntax using multiple types of context models can transmit information indicating whether to update the probability model to the decoder for each predefined unit. The decoder can then determine whether to update the probability model based on the information received from the encoder. Here, the predefined units can be sequences, groups of pictures (GOPs), intra-frame periods, pictures, slices, tiles, CTUs, CUs, TUs, and context initialization units.

[0192] According to one embodiment of the present disclosure, the probability model used in the CABAC procedure can be adaptively updated according to bins.

[0193] In the CABAC procedure, the decoding throughput for a bin may vary depending on the transmission characteristics of the bin. Therefore, when decoding a bin expected to have a high throughput, the decoder may not update the probability model. In other words, the decoder performs regular coding using the probability model determined by the initialization result, but omits updating the probability model. Consequently, the complexity of CABAC decoding can be reduced, and the throughput of syntax decoding can be increased.

[0194] For example, when decoding bins that use multiple context models during the decoding process, the decoder may not update the probability model. Alternatively, the probability model for a bin may fluctuate significantly during the decoding process for consecutive bins. Therefore, when decoding bins that are passed consecutively, the decoder may not update the probability model. Alternatively, if the predicted probability for a bin based on context modeling is determined to differ significantly from the actual probability for the bin, the decoder may not update the probability model.

[0195] Alternatively, the number of bins encoded by the regular coding engine may be preset to a specific number. If the decoder decodes more than the preset number of bins, the decoder may not update the probability model. Here, the encoder may transmit information about the preset number of bins to the decoder. The decoder may determine whether to update the probability model based on the information received from the encoder. Here, the information about the preset number of bins may be transmitted in a predetermined unit. The predefined units may include a sequence, a GOP, an intra-frame period, a picture, a slice, a tile, a CTU, a CU, a TU, and a context initialization unit.

[0196] Here, by omitting the probability model update, the decoding operation that reduces the complexity of CABAC can be performed by the simplified regular coding engine of the binary arithmetic decoder. Alternatively, by omitting the probability model update, the decoding operation that reduces the complexity of CABAC can be selectively performed by the regular coding engine of the binary arithmetic decoder.

[0197] Additionally, by omitting the probability model update described above, operations identical to or equivalent to the decoding operation that reduces the complexity of CABAC can be performed by the encoder.

[0198]

[0199] Furthermore, one of the procedures that directly impacts the processing rate of CABAC is the procedure for determining a context model. In particular, significant delays can occur in the process of determining which context model to use among multiple context model candidates. Therefore, according to one embodiment of the present disclosure, context models can be adaptively constructed and used in the CABAC procedure.

[0200] Specifically, the binary arithmetic decoder of the low-complexity decoder may include a bypass coding engine, a regular coding engine, and a simplified regular coding engine. The simplified regular coding engine may be configured independently from the regular coding engine. Here, the simplified regular coding engine may selectively use context model information used in the regular coding procedure or simplified context model information for low complexity. Alternatively, the simplified regular coding engine may not use context model information used in the regular coding procedure, but may use only the simplified context model information.

[0201] In contrast, according to another embodiment of the present disclosure, a binary arithmetic decoder may include a bypass coding engine and a regular coding engine. Here, the regular coding engine may selectively use context model information used in the regular coding procedure or simplified context model information for low complexity. For example, if a certain condition is met, the regular coding engine may use simplified context model information for low complexity instead of context model information used in the conventional regular coding procedure.

[0202] Here, according to the context model information used in the regular coding procedure, the number of available context models used in the coding of the syntactic element can be defined for each predetermined unit. Furthermore, according to the simplified context model information, the number of available context models used in the coding of the syntactic element can be defined for each predetermined unit. Here, the number of context models defined by the simplified context model information may be smaller than the number of context models defined by the context model information used in the regular coding procedure.

[0203] The encoder can transmit information about the number of context models defined for each predetermined unit to the decoder. The decoder can then decode the data using the context models based on the information received from the encoder. Here, the predetermined units can be sequences, groups of operations (GOPs), intra-frame periods, pictures, slices, tiles, CTUs, units of control units (CUs), units of transition (TUs), and context initialization units.

[0204] According to one embodiment of the present disclosure, the context model used in the CABAC procedure can be used adaptively according to the syntax.

[0205] When decoding syntax that requires a significant amount of processing in the CABAC process, the decoder can decode the syntax using a limited number of context models. In this case, the decoder can decode the syntax using a limited number of context models, which is smaller than the number used in regular coding. Therefore, the complexity of CABAC decoding can be reduced, and the throughput of syntax decoding can be increased.

[0206] For example, when decoding a syntax that uses many types of context models in a decoding procedure, the decoder can use a limited number of context models. Here, the syntax that uses many types of context models may be a syntax that consists of a predetermined number or more of bins. Alternatively, the syntax that uses many types of context models may be an empirically determined syntax. The encoder and the decoder can share information about the empirically determined syntax. Therefore, the encoder that encodes the syntax and the decoder that decodes the syntax can determine whether to use a limited number of context models based on the shared information.

[0207] Alternatively, an encoder encoding a syntax using a variety of context models can transmit information to the decoder indicating the use of a limited number of context models for each predefined unit. The decoder can then determine whether to update the probability model based on the information received from the encoder. Here, the predefined units can be sequences, groups of operations (GOPs), intra-frame periods, pictures, slices, tiles, CTUs, units of control units (CUs), units of transition (TUs), and context initialization units.

[0208] According to one embodiment of the present disclosure, the context model used in the CABAC procedure can be used adaptively depending on the bin.

[0209] In the CABAC procedure, the decoding throughput for a bin may vary depending on the transmission characteristics of the bin. Therefore, when decoding a bin expected to require a large amount of processing, the decoder can use a limited number of context models. That is, the decoder can decode the bin using a limited number of context models, which is less than the number used in the regular coding procedure. Consequently, the complexity of CABAC decoding can be reduced, and the throughput of syntax decoding can be increased.

[0210] For example, when decoding a bin that uses many types of context models in the decoding procedure, the decoder may decode the bin using a limited number of context models. Alternatively, the probability model for a bin may vary significantly during the decoding procedure of consecutive bins. Therefore, when decoding consecutively transmitted bins, the decoder may decode the bin using a limited number of context models. Alternatively, when the predicted probability of a bin based on context modeling is judged to differ significantly from the actual probability of the bin, the decoder may encode and decode the bin using a limited number of context models.

[0211] Alternatively, the number of bins encoded by the regular coding engine may be preset to a specific number. When the decoder decodes more than the preset number of bins, the decoder may decode the syntax using a limited number of context models. Here, the encoder may transmit information about the number of bins to the decoder. The decoder may determine whether to use the limited number of context models based on the information received from the encoder. Here, the predefined units may be sequences, GOPs, intra-frame periods, pictures, slices, tiles, CTUs, CUs, TUs, and context initialization units.

[0212] Here, the decoding operation that reduces the complexity of CABAC by using a limited number of context models can be performed by a simplified regular coding engine of a binary arithmetic decoder. Alternatively, the decoding operation that reduces the complexity of CABAC by using a limited number of context models can be selectively performed by a regular coding engine of a binary arithmetic decoder.

[0213] Additionally, by using the limited number of context models described above, operations identical to or equivalent to the decoding operations that reduce the complexity of CABAC can be performed by the encoder.

[0214]

[0215] Additionally, to improve the throughput of CABAC, according to one embodiment of the present disclosure, the decoder can adaptively configure and use a probability model and / or a context model during the initialization procedure of the CABAC procedure. According to one embodiment of the present disclosure, the initialization procedure in the CABAC procedure can be performed as follows.

[0216] Specifically, the initial probability values ​​used in the CABAC procedure may be probability values ​​preset as a result of training. In this case, the decoder may perform the CABAC procedure without updating the initial probability values. For example, the initial probability values ​​may be set to the same probability, such as 0.5(MPS):0.5(LPS). Alternatively, the initial probability values ​​may be set to a(MPS):1-a(LPS), where a may be a value between 0 and 1.

[0217] When decoding syntax or bins using one or more context models, the decoder may use a probability value preset as a training result as an initial probability value. Then, the decoder may update the probability model until it decodes a predetermined number of units of syntax or bins. Then, the decoder that decodes the syntax or bins of subsequent units may not update the probability model. Alternatively, the decoder that decodes the syntax or bins of subsequent units may selectively update the probability model. Here, the predefined units may be sequences, GOPs, intra-frame periods, pictures, slices, tiles, CTUs, CUs, TUs, and context initialization units.

[0218] For example, a decoder may perform a probability model update during the process of decoding syntax or bins for 10 CTUs. Furthermore, the decoder may not update the probability model when decoding subsequent syntaxes or bins.

[0219]

[0220] When decoding a syntax or bin using a limited number of context models, the decoder can use a probability value preset as a training result as an initial probability value. The decoder can update the context model and / or probability model using all kinds of context models defined in the canonical coding until it decodes a predetermined number of units of syntax or bins. Then, in the decoding process of the syntax or bin of the subsequent units, the decoder can use only a specific context model among the context models defined in the canonical coding. Here, the predefined units can be a sequence, a GOP, an intra-frame period, a picture, a slice, a tile, a CTU, a CU, a TU, and a context initialization unit.

[0221] For example, a decoder may use four context models in the encoding and decoding procedures of syntax or bins for 10 CTUs. Furthermore, the decoder may use only one specific context model among the four context models in subsequent syntax or bin encoding and decoding procedures.

[0222] When decoding syntax or bins using a limited number of context models, the decoder can use a probability value preset as a training result as an initial probability value. The decoder can update the context model and / or probability model using all kinds of context models defined in the canonical coding until it decodes a predetermined number of units of syntax or bins. Then, in the process of decoding syntax or bins of subsequent units, the decoder can use only a limited number of context models among the context models defined in the canonical coding. Here, the predefined units can be a sequence, a GOP, an intra-frame period, a picture, a slice, a tile, a CTU, a CU, a TU, and a context initialization unit.

[0223] For example, a decoder can use four context models in the encoding and decoding procedures of syntax or bins for 10 CTUs. Furthermore, the decoder can utilize the four context models to derive a single context model in subsequent syntax or bin encoding and decoding procedures, and perform low-complexity CABAC using the derived single context model. In other words, the decoder may not use any models other than the derived context model.

[0224] Embodiments of low-complexity CABAC that simplify the procedure for updating the probabilistic model of the present disclosure can be performed independently or simultaneously. Embodiments of low-complexity CABAC that adaptively construct the context model of the present disclosure can be performed independently or simultaneously. Furthermore, embodiments of low-complexity CABAC that simplify the CABAC initialization procedure of the present disclosure can be performed independently or simultaneously.

[0225] Additionally, operations identical to or corresponding to the initialization operations that reduce the complexity of CABAC described above can be performed by the encoder.

[0226] An image decoding method and an image encoding method for a low-complexity CABAC method according to an embodiment of the present disclosure may be as described below.

[0227]

[0228] FIG. 9 is a flowchart illustrating an image decoding method for a low-complexity CABAC method according to an embodiment of the present disclosure. The image decoding method of FIG. 9 can be performed by an image decoding device.

[0229] Referring to FIG. 9, the image decoding device can obtain a bitstream including encoded syntax elements (S910).

[0230] The image decoding device can determine a context model for the encoded syntax element (S920).

[0231] Here, the context model for the encoded syntactic element can be determined from a predetermined number of context models. Specifically, the predetermined number may be less than the number of context models used in the regular coding of the syntactic element.

[0232] Here, the predetermined number may be determined based on the syntactic elements. Alternatively, the predetermined number may be determined based on the number of bins in the syntactic elements to be decoded. Alternatively, the predetermined number may be determined based on the continuity of the bins in the syntactic elements to be decoded. Alternatively, the predetermined number may be determined based on a preset prediction unit.

[0233] Here, when a predetermined number or more of syntactic elements of a preset prediction unit are decoded, a context model for the encoded syntactic element can be determined from among the predetermined number of context models.

[0234] The image decoding device can determine a probability model based on the context model (S930). Then, the image decoding device can decode the encoded syntax element based on the probability model (S940).

[0235] And, the image decoding device can determine whether to update the probability model (S950).

[0236] Here, whether to update the probability model can be determined based on the syntactic elements. Alternatively, whether to update the probability model can be determined based on the number of bins in the decoded syntactic elements. Alternatively, whether to update the probability model can be determined based on the continuity of the bins in the decoded syntactic elements.

[0237] Here, whether the probability model is updated can be determined based on the preset prediction units. For example, if a certain number of syntactic elements of a preset prediction unit or more are decoded, the probability model may not be updated.

[0238]

[0239] FIG. 10 is a flowchart illustrating an image encoding method for a low-complexity CABAC method according to an embodiment of the present disclosure. The image encoding method of FIG. 10 can be performed by an image encoding device.

[0240] Referring to FIG. 10, the image encoding device can binarize the syntax element (S1010).

[0241] The image encoding device can determine a context model for the binarized syntax elements (S1020).

[0242] Here, the context model for the binarized syntactic element can be determined from a predetermined number of context models. Specifically, the predetermined number may be less than the number of context models used in the regular coding of the syntactic element.

[0243] Here, the predetermined number may be determined based on the syntactic elements. Alternatively, the predetermined number may be determined based on the number of bins in the binarized syntactic elements. Alternatively, the predetermined number may be determined based on the continuity of the bins in the binarized syntactic elements. Alternatively, the predetermined number may be determined based on a preset prediction unit.

[0244] Here, when a predetermined number or more of syntactic elements of a preset prediction unit are binarized, a context model for the binarized syntactic elements can be determined from among a predetermined number of context models.

[0245] The image encoding device can determine a probability model based on the context model (S1030). In addition, the image encoding device can encode a binarized syntactic element based on the probability model (S1040).

[0246] And, the image encoding device can determine whether to update the probability model (S1050).

[0247] Here, whether to update the probability model can be determined based on the syntactic elements. Alternatively, whether to update the probability model can be determined based on the number of bins in the binarized syntactic elements. Alternatively, whether to update the probability model can be determined based on the continuity of the bins in the binarized syntactic elements.

[0248] Here, whether the probability model is updated can be determined based on the preset prediction units. For example, if a certain number or more of the syntactic elements of the preset prediction units are binarized, the probability model may not be updated.

[0249]

[0250] Additionally, a bitstream may be generated by a video encoding method including the steps described in FIG. 10. The bitstream may be stored on a non-transitory computer-readable recording medium and may also be transmitted (or streamed).

[0251]

[0252] While the exemplary methods of this disclosure are presented as a series of operations for clarity of description, this is not intended to limit the order in which the steps are performed, and individual steps may be performed simultaneously or in different orders, if desired. To implement a method according to this disclosure, additional steps may be included in addition to the steps illustrated, some steps may be excluded and the remaining steps may be included, or some steps may be excluded and additional steps may be included.

[0253] The various embodiments of the present disclosure are not intended to list all possible combinations but rather to illustrate representative aspects of the present disclosure, and the matters described in the various embodiments may be applied independently or in combinations of two or more.

[0254] Various embodiments of the present disclosure may be implemented by hardware, firmware, software, or a combination thereof. In the case of hardware implementation, the embodiments may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general processors, controllers, microcontrollers, microprocessors, etc.

[0255] Alternatively, various embodiments of the present disclosure may be implemented in the form of program commands that can be executed by various computer components and recorded on a computer-readable recording medium. Furthermore, a bitstream generated by the encoding method according to the above embodiment may be stored on a non-transitory computer-readable recording medium.

[0256] The computer-readable recording medium may include program commands, data files, data structures, etc., either singly or in combination. The program commands recorded on the computer-readable recording medium may be those specifically designed and configured for the present disclosure or may be known and available to those skilled in the art of computer software.

[0257] As described above, the present disclosure has been described based on specific details, such as specific components, and limited embodiments and drawings. However, the embodiments of the present disclosure are provided merely to facilitate a general understanding of the present disclosure and are not intended to limit the present disclosure to these embodiments. Accordingly, those skilled in the art will appreciate that various modifications and variations can be made based on the description.

[0258] Therefore, the idea of ​​the present invention should not be limited to the embodiments described above, and all things that are modified equally or equivalently to the following claims as well as the claims are considered to fall within the scope of the idea of ​​the present invention.

[0259] The present invention can be used in a device for encoding an image, a device for decoding an image, and a recording medium for storing a bitstream.

Claims

1. In the video decryption method, A step of obtaining a bitstream including encoded syntax elements; A step of determining a context model for the above encoded syntactic elements; A step of determining a probability model based on the above context model; A step of decoding the encoded syntactic element based on the above probability model; and An image decoding method, characterized by comprising a step of determining whether to update the above probability model.

2. In paragraph 1, Whether the above probability model is updated or not, An image decoding method characterized in that it is determined based on the above syntax elements.

3. In paragraph 1, Whether the above probability model is updated or not, A method for decoding an image, characterized in that the number of bins of a syntactic element to be decoded is determined based on the number of bins.

4. In paragraph 1, Whether the above probability model is updated or not, An image decoding method characterized in that it is determined based on the continuity of the bins of the syntactic elements to be decoded.

5. In paragraph 1, Whether the above probability model is updated or not, A method for decoding an image, characterized in that it is determined according to a preset prediction unit.

6. In paragraph 5, An image decoding method, characterized in that the probability model is not updated when a predetermined number or more of syntactic elements of a preset prediction unit are decoded.

7. In paragraph 1, The context model for the above encoded syntactic elements is: is determined from a given number of context models, The above specified number is, An image decoding method characterized in that the number of context models used in the regular coding of the above syntax elements is smaller than that of the above syntax elements.

8. In paragraph 7, The above specified number is, An image decoding method characterized in that it is determined based on the above syntax elements.

9. In paragraph 7, The above specified number is, A method for decoding an image, characterized in that the number of bins of a syntactic element to be decoded is determined based on the number of bins.

10. In paragraph 7, The above specified number is, An image decoding method characterized in that it is determined based on the continuity of the bins of the syntactic elements to be decoded.

11. In paragraph 7, The above specified number is, A method for decoding an image, characterized in that it is determined according to a preset prediction unit.

12. In paragraph 11, If a syntactic element of a preset prediction unit exceeding a certain number is decoded, An image decoding method, characterized in that the context model for the above-mentioned encoded syntax element is determined from among a predetermined number of context models.

13. In the video encoding method, Step of binarizing the syntactic elements; A step of determining a context model for the above binarized syntactic elements; A step of determining a probability model based on the above context model; A step of encoding the binarized syntactic elements based on the above probability model; and An image encoding method, characterized in that it comprises a step of determining whether to update the above probability model.

14. In a non-transitory computer-readable recording medium storing a bitstream generated by an image encoding method, The above image encoding method is, Step of binarizing the syntactic elements; A step of determining a context model for the above binarized syntactic elements; A step of determining a probability model based on the above context model; A step of encoding the binarized syntactic elements based on the above probability model; and A non-transitory computer-readable recording medium, characterized in that it comprises a step of determining whether to update the above probability model.

15. In a method for transmitting a bitstream generated by a video encoding method, The above transmission method includes a step of transmitting the bitstream, The above image encoding method is, Step of binarizing the syntactic elements; A step of determining a context model for the above binarized syntactic elements; A step of determining a probability model based on the above context model; A step of encoding the binarized syntactic elements based on the above probability model; and A transmission method, characterized in that it comprises a step of determining whether to update the above probability model.

Images