Abstract: A video coding method that uses a neural network to perform in-loop filtering is provided. The video coder provides a neural network filter by spatially shifting a feature map of the neural network. The video coder reconstructs a current block of pixels of a current picture of a video based on prediction residuals generated or received by the video coder. The video coder filters samples of the reconstructed current block by using the neural network filter. The video coder encodes or decodes a subsequent block of the video by inter- or intra-prediction based on the filtered samples of the current block.

Abstract: A method and apparatus of signal processing using a grouped neural network (NN) process are disclosed. A plurality of input signals for a current layer of NN process are grouped into multiple input groups comprising a first input group and a second input group. The neural network process for the current layer is partitioned into multiple NN processes comprising a first NN process and a second NN process. The first NN process and the second NN process are applied to the first input group and the second input group to generate a first output group and a second output group for the current layer of NN process respectively. In another method, the parameter set associated with a layer of NN process is coded using different code types.

Abstract: A method for video decoding includes receiving a video frame reconstructed based on data received from a bitstream. The method further includes extracting, from the bitstream, a first syntax element indicating whether a spatial partition for partitioning the video frame is active. The method also includes, responsive to the first syntax element indicating that the spatial partition for partitioning the video frame is active, determining a configuration of the spatial partition for partitioning the video frame, determining a plurality of parameter sets of a neural network, and applying the neural network to the video frame. The video frame is spatially divided based on the determined configuration of the spatial partition for partitioning the video frame into a plurality of portions, and the neural network is applied to the plurality of portions in accordance with the determined plurality of parameter sets.

Abstract: A method and apparatus of video encoding video coding for a video encoder or decoder using Neural Network (NN) are disclosed. According to this method, the multiple frames in a video sequence comprises multiple segments, where each of the multiple segments comprises a set of frames. The NN (Neural Network) processing is applied to a target signal in one or more encoded frames of a target segment in the encoder side or to the target signal in one or more decoded frames of the target segment in the decoder side using one NN parameter set for the target segment. The target signal may correspond to reconstructed residual, reconstructed output, de-blocked output, SAO (sample adaptive offset) output, ALF (adaptive loop filter) output, or a combination thereof. In another embodiment, the NN processing is applied to a target signal only in one or more specific encoded or decoded frames.

Abstract: Method and apparatus of video encoding video coding for a video encoder or decoder using Neural Network (NN) are disclosed. According to one method, input data or a video bitstream are received for blocks in one or more pictures, which comprise one or more colour components. The residual data, prediction data, reconstructed data, filtered-reconstructed data or a combination thereof is derived for one or more blocks of said one or more pictures. A target signal corresponding to one or more of the about signal types is processed using a NN (Neural Network) and the input of the NN or an output of the NN comprises two or more colour components. According to another method, A target signal corresponding to one or more of the about signal types is processed using a NN and the input of the NN or an output of the NN comprises two or more colour components.

Abstract: A method and apparatus of video encoding video coding for a video encoder or decoder using Neural Network (NN) are disclosed. According to this method, the multiple frames in a video sequence comprises multiple segments, where each of the multiple segments comprises a set of frames. The NN (Neural Network) processing is applied to a target signal in one or more encoded frames of a target segment in the encoder side or to the target signal in one or more decoded frames of the target segment in the decoder side using one NN parameter set for the target segment. The target signal may correspond to reconstructed residual, reconstructed output, de-blocked output, SAO (sample adaptive offset) output, ALF (adaptive loop filter) output, or a combination thereof. In another embodiment, the NN processing is applied to a target signal only in one or more specific encoded or decoded frames.

Abstract: A method and apparatus of signal processing using a grouped neural network (NN) process are disclosed. A plurality of input signals for a current layer of NN process are grouped into multiple input groups comprising a first input group and a second input group. The neural network process for the current layer is partitioned into multiple NN processes comprising a first NN process and a second NN process. The first NN process and the second NN process are applied to the first input group and the second input group to generate a first output group and a second output group for the current layer of NN process respectively. In another method, the parameter set associated with a layer of NN process is coded using different code types.

Abstract: Method and apparatus of video encoding video coding for a video encoder or decoder using Neural Network (NN) are disclosed. According to one method, input data or a video bitstream are received for blocks in one or more pictures, which comprise one or more colour components. The residual data, prediction data, reconstructed data, filtered-reconstructed data or a combination thereof is derived for one or more blocks of said one or more pictures. A target signal corresponding to one or more of the about signal types is processed using a NN (Neural Network) and the input of the NN or an output of the NN comprises two or more colour components. According to another method, A target signal corresponding to one or more of the about signal types is processed using a NN and the input of the NN or an output of the NN comprises two or more colour components.