Virtual-Boundary CCSAO for Luma-Guided Chroma Coding
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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 efficiency.
Innovation Solution
The implementation of Cross-Component Sample Adaptive Offset (CCSAO) methods that utilize positional relationships between luma and chroma components to determine sample offsets and modify sample values, enhancing coding efficiency by leveraging cross-component correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional chroma coding methods are used, then coding complexity is low, but chroma coding efficiency and image quality deteriorate
Solution Approach 1:
The patent introduces luma samples as an intermediary to determine chroma sample offsets. By using the positional relationship between luma and chroma components, the method leverages cross-component correlations to improve chroma coding efficiency without requiring complex chroma-specific analysis, thus resolving the contradiction between coding efficiency and complexity.
2Loss of information
If chroma coding efficiency is improved through complex methods, then bit-rate savings are achieved, but processing complexity increases
Solution Approach 1:
The patent changes the parameter used for offset determination from chroma-specific features to luma-based positional relationships. By utilizing the already-available luma samples and their positional relationships with chroma samples, the method achieves better chroma coding efficiency and bit-rate savings while avoiding the need for additional complex processing.
3Productivity
If cross-component correlations are leveraged, then chroma coding efficiency improves, but computational requirements increase
Solution Approach 1:
The patent makes the chroma coding process self-service by using luma samples that are already available from the decoding process. Instead of requiring separate complex chroma analysis, the method utilizes the existing luma-chroma positional relationships to automatically determine chroma offsets, thereby improving coding efficiency without significantly increasing computational requirements.
Data Source
AI summary
An electronic apparatus performs a method of decoding video data. The method includes: receiving, from the video signal, a picture frame that includes a first component and a second component; determining a classifier for the second component from a set of one or more samples of the first component associated with a respective sample of the second component; determining whether to modify a value of the respective sample of the second component of a current block of the picture frame within a virtual boundary according to the classifier; in response to the determination to modify the value of the respective sample of the second component of the current block according to the classifier, determining a sample offset for the respective sample of the second component according to the classifier; and modifying the value of the respective sample of the second component based on the determined sample offset.