A block of video data is split and coded using existing transform sizes through one of several embodiments. In one embodiment, the block is split in alternate dimensions, depending on the block size. In another embodiment, the video block can be coded after splitting the block into at least two rectangular sub-blocks using horizontal or vertical divisions. Successive divisions using asymmetric splitting are forbidden if an equivalent split can be attained using only symmetrical splitting, and only one succession of divisions is permitted when there are other successions of asymmetric splitting that result in the identical sub-blocks. In another embodiment, a video block is split using successive splits, but the second type of split is dependent on the first type of split. Methods, apparatus, and signal embodiments are provided for encoding and decoding.

A block of video data is split and coded using existing transform sizes through one of several embodiments. In one embodiment, the block is split in alternate dimensions, depending on the block size. In another embodiment, the video block can be coded after splitting the block into at least two rectangular sub-blocks using horizontal or vertical divisions. Successive divisions using asymmetric splitting are forbidden if an equivalent split can be attained using only symmetrical splitting, and only one succession of divisions is permitted when there are other successions of asymmetric splitting that result in the identical sub-blocks. In another embodiment, a video block is split using successive splits, but the second type of split is dependent on the first type of split. Methods, apparatus, and signal embodiments are provided for encoding and decoding.

A method includes performing a conversion, according to a rule, between a current slice of a current picture of a video and a bitstream of the video, wherein the rule specifies that a first syntax element of a picture parameter set (PPS) and a second syntax element of the PPS control whether a third syntax element is included in the bitstream, and wherein the first syntax element indicates whether a weighted prediction is enabled for bi-directional slices of coded pictures referring to the PPS, the second syntax element indicates whether information related to the weighted prediction is present in picture headers or slice headers of coded pictures referring to the PPS, and the third syntax element indicates a number of weights associated with a reference picture list 1 of the current slice.

Systems and methods for deep neural network (DNN)-based cross component prediction are provided. A method includes inputting a reconstructed luma block of an image or video into a DNN; and predicting, by the DNN, a reconstructed chroma block of the image or video based on the reconstructed luma block that is input. Luma and chroma reference information and side information may also be input into the DNN to predict the reconstructed chroma block. The various inputs may also be generated using processes such as downsampling and transformation.

A method and apparatus for neural network based cross component prediction with scaling factors during encoding or decoding of an image frame or a video sequence, which may include training a deep neural network (DNN) cross component prediction (CCP) model with at least one or more scaling factors, wherein the at least one or more scaling factors are learned by optimizing a rate-distortion loss based on an input video sequence comprising a luma component, and reconstructing a chroma component based on the luma component using the trained DNN CCP model with the at least one or more scaling factors for chroma prediction. The trained DNN CCP may be updated for chroma prediction of the input video sequence using the one or more scaling factors, and performing chroma prediction of the input video sequence using the updated DNN CCP model with the one or more scaling factors.

A method and apparatus for neural network based cross component prediction with scaling factors during encoding or decoding of an image frame or a video sequence, which may include training a deep neural network (DNN) cross component prediction (CCP) model with at least one or more scaling factors, wherein the at least one or more scaling factors are learned by optimizing a rate-distortion loss based on an input video sequence comprising a luma component, and reconstructing a chroma component based on the luma component using the trained DNN CCP model with the at least one or more scaling factors for chroma prediction. The trained DNN CCP may be updated for chroma prediction of the input video sequence using the one or more scaling factors, and performing chroma prediction of the input video sequence using the updated DNN CCP model with the one or more scaling factors.

A method and apparatus for neural network based cross component prediction with low-bit precision during encoding or decoding of an image frame or a video sequence, which may include reconstructing a chroma component based on a received luma component using a pre-trained deep neural network (DNN) cross component prediction (CCP) model for chroma prediction, and updating a set of parameters of the pre-trained DNN CCP model with low-bit precision. The method may also include generating an updated DNN CCP model for chroma prediction with low-bit precision based on at least one video sequence, and using the updated DNN CCP model for cross component prediction of the at least one video sequence at reduced processing time.

An object of the embodiments is to achieve an improved reference picture handling. That is achieved by taking into account whether the reference pictures in the decoded picture buffer are long-term reference pictures or short-term reference pictures when determining how they should be marked when the information of the reference picture set is received. The reference pictures are marked as “used for short-term reference” or “used for long-term reference” in the Decoded Picture Buffer (DPB) depending on whether they are included as short-term pictures or long-term pictures in the RPS of a current picture.

An object of the embodiments is to achieve an improved reference picture handling. That is achieved by taking into account whether the reference pictures in the decoded picture buffer are long-term reference pictures or short-term reference pictures when determining how they should be marked when the information of the reference picture set is received. The reference pictures are marked as “used for short-term reference” or “used for long-term reference” in the Decoded Picture Buffer (DPB) depending on whether they are included as short-term pictures or long-term pictures in the RPS of a current picture.

A macro block size determining unit 1 determines the size of each macro block on a frame-by-frame basis. A macro block dividing unit 2 divides an inputted image into macro blocks each having the size determined by the macro block size determining unit 1. A macro block coding unit 3 determines a coding mode for each of the macro blocks divided by the macro block dividing unit 2, and codes pixel values in each of the macro blocks in the determined coding mode.