Abstract:
Improved neural-network-based image and video coding techniques are presented, including hybrid techniques that include both tools of a host codec and neural-network-based tools. In these improved techniques, the host coding tools may include conventional video coding standards such H.266 (VVC). In an aspects, source frames may be partitioned and either host or neural-network-based tools may be selected per partition. Coding parameter decisions for a partition may be constrained based on the partitioning and coding tool selection. Rate control for host and neural network tools may be combined. Multi-stage processing of neural network output may use a checkerboard prediction pattern.
Abstract:
Techniques are disclosed for selecting deblocking filter parameters in a video decoding system. According to these techniques, a boundary strength parameter may be determined based, at least in part, on a bit depth of decoded video data. Activity of a pair of decoded pixel blocks may be classified based, at least in part, on the determined boundary strength parameter, and when a level of activity indicates that deblocking filtering is to be applied to the pair of pixel blocks, pixel block content at a boundary between the pair of pixel blocks may be filtered using filtering parameters derived at least in part based on the bit depth of the decoded video data. The filtering parameters may decrease strength with increasing bit depth of the decoded video data, which improves quality of the decoded video data.
Abstract:
A filtering system for video coders and decoders is disclosed that includes a feature detector having an input for samples reconstructed from coded video data representing a color component of source video, and having an output for data identifying a feature recognized therefrom, an offset calculator having an input for the feature identification data from the feature detector and having an output for a filter offset, and a filter having an input for the filter offset from the offset calculator and an input for the reconstructed samples, and having an output for filtered samples. The filtering system is expected to improve operations of video coder/decoder filtering systems by selecting filtering offsets from analysis of recovered video data in a common color plane as the samples that will be filtered.
Abstract:
An encoder or decoder can perform enhanced motion vector prediction by receiving an input block of data for encoding or decoding and accessing stored motion information for at least one other block of data. Based on the stored motion information, the encoder or decoder can generate a list of one or more motion vector predictor candidates for the input block in accordance with an adaptive list construction order. The encoder or decoder can predict a motion vector for the input block based on at least one of the one or more motion vector predictor candidates.
Abstract:
Systems and methods disclosed for video compression, utilizing neural networks for predictive video coding. Processes employed combine multiple banks of neural networks with codec system components to carry out the coding and decoding of video data.
Abstract:
A cross-component based filtering system is disclosed for video coders and decoders. The filtering system may include a filter having an input for a filter offset and an input for samples reconstructed from coded video data representing a native component of source video on which the filter operates. The offset may be generated at least in part from a sample classifier that classifies samples reconstructed from coded video data representing a color component of the source video orthogonal to the native component according to sample intensity.