Entropy Coding Context Model Selection for Video Partitioning
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Solution Overview
Problem
High-efficiency video coding (HEVC) faces challenges in effectively encoding and decoding partitioning structures of video blocks, particularly in selecting appropriate context models for entropy coding based on the partitioning structures of current and neighboring blocks, which affects the accuracy and efficiency of video processing.
Innovation Solution
A video coder that entropy encodes or decodes partitioning indicators by selecting a context model based on the partitioning structure of the current block and its neighboring blocks, considering split depths, shapes, and orientations, using context-based adaptive binary arithmetic coding (CABAC) to determine context indices for improved entropy coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed context model is used for entropy coding partitioning indicators, then the encoding process is simple, but the coding efficiency and accuracy are limited
Solution Approach 1:
The patent implements dynamic context model selection by adapting the context model based on the partitioning structure of the current block and neighboring blocks. The context index is adjusted according to split depth, split shape, and neighboring block characteristics, making the entropy coding process adaptive rather than static. This dynamic approach improves coding efficiency by matching the context model to the actual data characteristics.
Solution Approach 2:
The patent applies different context models to different regions and scenarios within the video data. Specifically, it selects context models based on local partitioning structures, split depths, and neighboring block characteristics. This localized adaptation ensures that each block is encoded with the most appropriate context model for its specific characteristics, improving overall coding accuracy.
2Measurement precision
If context model selection is based on detailed partitioning structures, then coding accuracy improves, but processing complexity increases
Solution Approach 1:
The patent segments the context model selection process into distinct components: determining split depth, determining split shape, identifying neighboring block characteristics, and selecting the appropriate context model based on these segmented factors. This segmentation allows the system to handle complex partitioning structures systematically by breaking down the selection process into manageable steps.
Solution Approach 2:
The patent performs preliminary analysis of the partitioning structure, split depth, and neighboring blocks before selecting the context model. By pre-determining these characteristics and using them to guide context model selection, the system prepares the encoding process in advance, ensuring accurate context model matching without adding excessive processing complexity during the actual encoding phase.
Data Source
AI summary
A video coder that performs entropy coding of a syntax element for partitioning a block of pixels is provided. The video coder receives a current block of pixels of an image. The video coder encodes or decodes the current block of pixels by partitioning the current block of pixels according to a partitioning indicator. The partitioning indicator is entropy coded as a syntax element of a bitstream by using a context model that is selected based on a partitioning structure of the current block of pixels or of a neighboring block of pixels of the current block of pixels.