Adaptive Loop Filter Spatial Neighboring Samples Video Coding
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently improving the adaptive loop filtering (ALF) process, particularly in terms of filter adaptation, classifier design, and filter shapes, which affect coding efficiency and video quality.
Innovation Solution
The proposed solution involves enhancing the ALF process by using spatial neighboring samples from prediction, residual, or reconstructed signals, combining edge-based and band-based classifiers, and unifying filter shapes for luma and chroma components to improve filter adaptation and coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional adaptive loop filtering is used with limited spatial information, then device complexity is reduced, but video quality and coding efficiency deteriorate
Solution Approach 1:
The patent extends the filtering process from traditional spatial domain to include temporal dimension by incorporating reference frame samples. The filter adapts using both spatial neighboring samples and temporal reference samples, creating a spatio-temporal filtering approach that improves video quality without proportionally increasing complexity
Solution Approach 2:
The patent performs preliminary classification of blocks based on their characteristics (texture complexity, edge presence) before applying filtering. This pre-classification allows the system to select appropriate filter parameters in advance, reducing real-time computational complexity while maintaining high video quality
2Manufacturing precision
If more spatial neighboring samples are used for filtering, then video quality improves, but computational complexity increases
Solution Approach 1:
The patent applies different filtering strengths and methods to different local regions based on their characteristics. Smooth regions receive stronger filtering while textured or edge regions receive minimal or no filtering, optimizing the balance between quality improvement and computational cost for each local area
Solution Approach 2:
The patent dynamically adjusts filter parameters (filter width, strength, type) based on local block characteristics such as variance, gradient, and texture complexity. This adaptive parameter adjustment ensures high filtering precision where needed while reducing computational power consumption in regions where filtering is less beneficial
3Adaptability or versatility
If separate filter shapes are used for luma and chroma components, then filtering adaptability is improved, but device complexity increases
Solution Approach 1:
The patent implements a unified filter framework that can operate on both luma and chroma components using the same core filtering mechanism. The same filter structure adapts its parameters based on the specific component being processed, reducing implementation complexity while maintaining component-specific adaptability through parameter adjustment rather than structural differentiation
Data Source
AI summary
This disclosure is related to video coding and compression. More specifically, this disclosure relates to methods and apparatus for improving the coding efficiency of adaptive loop filter (ALF). In one example, the method includes obtaining, by a decoder, one or more spatial neighboring samples associated with a current sample, wherein the one or more spatial neighboring samples are from at least one of: (i) a prediction sample, (ii) a residual sample, or (iii) a reconstructed sample, wherein the reconstructed sample is sampled prior to a sample adaptive offset (SAO) filtering, and obtaining, by the decoder, a filtered sample, based on the one or more spatial neighboring samples associated with the current sample.