CCSAO Chroma Coding With Luma-Based Offset Mapping
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently encoding and decoding high-definition and ultra-high-definition video data while maintaining image quality, particularly in optimizing chroma coding by not leveraging cross-component relationships between luma and chroma components.
Innovation Solution
Implementing Cross-Component Sample Adaptive Offset (CCSAO) by using luma samples to determine chroma sample offsets through a mapping table, which includes a classifier and sample offsets based on luma component characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If chroma coding is performed without leveraging cross-component relationships between luma and chroma components, then device complexity is reduced, but chroma coding efficiency deteriorates
Solution Approach 1:
The patent uses luma component characteristics as an intermediary to determine chroma sample offsets. Instead of directly coding chroma without relationships, the luma component serves as a mediator that provides classification information (through classifiers) to guide chroma offset selection from mapping tables, thereby improving chroma coding efficiency while maintaining manageable complexity through structured intermediary processing
Solution Approach 2:
The patent changes the parameter used for chroma coding by introducing cross-component correlation. Specifically, it uses luma sample values to derive classifiers that then determine chroma sample offsets through mapping tables. This parameter change from independent chroma coding to correlated chroma-luma coding improves chroma coding efficiency by exploiting the statistical relationship between luma and chroma components
2Measurement precision
If cross-component sample adaptive offset is applied to improve chroma coding efficiency, then image quality is improved, but bit-rate requirements increase
Solution Approach 1:
The patent applies partial action by selectively using cross-component relationships only where beneficial. It uses mapping tables that provide a limited set of predefined offset values based on luma classifiers, rather than computing full independent chroma offsets. This partial application of cross-component offset improves image quality while controlling bit-rate increase through the use of pre-defined offset ranges and classification-based selection
Solution Approach 2:
The patent performs preliminary classification using luma samples before determining chroma offsets. The mapping tables are pre-populated with offset values based on luma component characteristics, and the classification process groups chroma samples into categories that share common offset patterns. This preliminary action reduces the need for transmitting detailed chroma offset information, thereby improving image quality while managing bit-rate requirements
Data Source
AI summary
An electronic apparatus performs a method of decoding video data. The method comprises: receiving, from the video signal, a picture frame that includes a first component and a second component; receiving, from the video signal, a first syntax element that indicates whether Cross-component Sample Adaptive Offset (CCSAO) is enabled for the second component at a picture level; when the first syntax element indicates the CCSAO is enabled for the second component at the picture level, receiving, from the video signal, a second syntax element that indicates whether the CCSAO is controlled for the second component at a coding tree block (CTB) level; and when the second syntax element indicates the CCSAO is controlled for the second component at the CTB level, receiving, from the video signal, a third syntax element that indicates whether the CCSAO is enabled for the second component at the CTB level.