Virtual-Boundary CCSAO for Chroma Coding Efficiency
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Solution Overview
Problem
Existing video coding standards face challenges in efficiently encoding and decoding high-definition and ultra-high-definition video data while maintaining image quality, particularly in optimizing chroma coding efficiency.
Innovation Solution
Implementing Cross-Component Sample Adaptive Offset (CCSAO) methods that utilize positional relationships and virtual boundaries to enhance chroma coding by determining sample offsets and modifying sample values based on classifiers, improving decoding efficiency and image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional video coding standards are used for high-definition and ultra-high-definition video, then video data transmission and processing can be performed, but chroma coding efficiency is insufficient and image quality degradation occurs
Solution Approach 1:
The patent introduces an intermediary mechanism (virtual boundary and classifier) that mediates between luma and chroma components. The virtual boundary defines a relationship space between corresponding luma and chroma samples, and the classifier determines offset application based on this relationship, thereby improving chroma coding efficiency while maintaining image quality through cross-component correlation exploitation
2Productivity
If chroma coding is enhanced using cross-component correlations, then decoding efficiency improves, but computational complexity increases
Solution Approach 1:
The patent applies local quality by introducing a virtual boundary that selectively defines relationships between luma and chroma samples based on their positional correspondence. The classifier operates locally at sample levels rather than globally, determining offset application on a per-sample basis. This localized approach improves decoding efficiency for relevant samples while avoiding unnecessary computations for samples where the virtual boundary relationship does not apply, thus managing computational complexity
Data Source
AI summary
An electronic apparatus performs a method of encoding video data. The method includes: in response to a current Coding Tree Unit (CTU) being not located within a bottom row of CTUs of a frame, slice, tile, subpicture or patch, determining a set of one or more samples of a first component of a picture frame according to a positional relationship between a respective sample of a second component of the picture frame and a virtual boundary; determining a class index for the second component from the set of one or more samples of the first component associated with the respective sample of the second component; selecting a sample offset from a plurality of sample offsets for the respective sample of the second component according to the class index; and obtaining a cross-component offsetted sample value of the second component based on the selected sample offset.