Video Coding CCALF Filtering for Efficient Chroma Processing
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Solution Overview
Problem
Existing video coding technologies face challenges in improving coding efficiency, enhancing image quality, and reducing processing resource utilization in video encoding and decoding processes.
Innovation Solution
Implementing a cross component adaptive loop filtering (CCALF) process that combines luma and chroma component filtering, including adaptive loop filtering (ALF) and clipping operations to enhance image quality and coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If separate filtering processes are applied to luma and chroma components, then filtering flexibility is maintained, but processing complexity and resource utilization increase
Solution Approach 1:
The patent combines separate luma and chroma filtering operations into a unified CCALF process. The chroma filtering coefficients are derived from luma component analysis, and both components are filtered using integrated logic that shares computational resources, thereby reducing overall processing complexity while maintaining effective filtering for both components.
Solution Approach 2:
The filtering mechanism is designed to handle both luma and chroma components through a universal CCALF process. The same filtering infrastructure and coefficient generation logic serve dual purposes: directly filtering luma and indirectly guiding chroma filtering, thus reducing device complexity through multi-functionality.
2Manufacturing precision
If advanced filtering processes like CCALF are implemented, then image quality improves, but processing resource utilization increases
Solution Approach 1:
The patent performs preliminary analysis on the luma component to generate filtering coefficients before applying chroma filtering. By pre-computing coefficients from the luma component (which has higher sampling density), the system avoids redundant computations during chroma filtering, thereby reducing overall processing resource utilization while maintaining advanced filtering quality.
Solution Approach 2:
The luma component serves as an intermediary for chroma filtering. Instead of independently analyzing chroma samples, the system uses luma component characteristics as a mediator to derive filtering coefficients that are then applied to chroma, reducing the computational burden on chroma processing while maintaining filtering effectiveness.
3Measurement precision
If clipping operations are applied to coefficient values, then encoding precision is improved, but information loss increases
Solution Approach 1:
The patent applies clipping operations that adaptively adjust coefficient values based on predefined thresholds and relationships between luma and chroma components. Rather than uniform clipping, the system modifies coefficient parameters dynamically - preserving values within valid ranges and adjusting out-of-range values to maintain encoding precision while minimizing information loss through intelligent threshold selection.
Data Source
AI summary
An encoder includes circuitry and memory coupled to the circuitry. 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, generates a second coefficient value by applying an ALF (adaptive loop filtering) process to a second reconstructed image sample of a chroma component, and clips the second coefficient value. The circuitry generates a third coefficient value by adding the first coefficient value to the clipped second coefficient value, and clips the third coefficient value. The circuitry encodes a third reconstructed image sample of the chroma component using the clipped third coefficient value.