CCALF Video Coding With Reduced Chroma Processing Complexity
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Solution Overview
Problem
There is a need to improve coding efficiency, enhance image quality, and reduce circuit scale in video coding technologies, particularly in cross component adaptive loop filtering processes.
Innovation Solution
Implementing a cross component adaptive loop filtering (CCALF) process that applies adaptive loop filtering to both luma and chroma components, clipping the coefficient values, and combining them to encode chroma components, along with other encoding and decoding methods to optimize video coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If adaptive loop filtering is applied to both luma and chroma components separately, then image quality is improved, but processing complexity and circuit scale increase
Solution Approach 1:
The patent combines separate filtering processes for luma and chroma components into a unified CCALF process. The chroma filtering coefficients are derived from luma filtering operations, merging two independent processing paths into one integrated system that achieves joint optimization while reducing circuit complexity.
Solution Approach 2:
The luma adaptive loop filter is designed to serve dual purposes: filtering luma components directly and generating filtering coefficients for chroma components through the cross-component adaptation mechanism. This multi-functionality eliminates the need for separate chroma filtering circuits.
2Measurement precision
If separate filtering processes are implemented for luma and chroma components, then filtering precision is improved, but processing time and resource utilization increase
Solution Approach 1:
The luma filtering process is performed first and its results are preprocessed to generate chroma filtering coefficients. This preliminary action allows chroma filtering to reuse computed values and operational patterns, significantly reducing the time required for separate chroma processing.
Solution Approach 2:
The patent changes the parameter derivation method for chroma filtering by obtaining coefficients from luma filtering results rather than computing them independently. This parameter transformation reduces processing operations while maintaining filtering precision through cross-component statistical relationships.
3Manufacturing precision
If independent filtering coefficients are used for chroma components, then filtering accuracy is maintained, but coding efficiency decreases
Solution Approach 1:
The patent applies local quality by using luma filtering characteristics (which vary by region and content) to determine appropriate chroma filtering coefficients for each block. This localized coefficient derivation maintains filtering accuracy adapted to local image features while improving overall coding efficiency through unified processing.
Data Source
AI summary
An encoder includes circuitry and memory. The circuitry, in operation, generates a first coefficient value by applying a CCALF (cross component adaptive loop filtering) process to a first reconstructed image sample of a luma component. The circuitry generates a second coefficient value by applying an ALF (adaptive loop filtering) process to a second reconstructed image sample of a chroma component. The circuitry generates a third coefficient value by adding the first coefficient value to the second coefficient value, and encodes a third reconstructed image sample of the chroma component using the third coefficient value. The circuitry determines a first parameter having the same value for Cb component and Cr component of the chroma component. The circuitry determines, using the first parameter, a model of entropy coding from a plurality of models. The circuitry performs, using the model, the entropy coding of a second parameter of the CCALF process.