Video Processing Cross-Component Prediction Filter Selection
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Solution Overview
Problem
Existing video coding technologies face challenges in improving coding efficiency, particularly in cross-component prediction methods where fixed down-sampling filters may not be efficient.
Innovation Solution
The proposed method involves generating a sample value of a first color component corresponding to a sample of a second color component by applying a plurality of filters to at least one sample of the first color component, and using this generated sample value for video processing conversions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed down-sampling filters are used in cross-component prediction, then device complexity is reduced, but coding efficiency deteriorates
Solution Approach 1:
The patent implements dynamic filter selection by deriving multiple candidate filters (first filter, second filter, third filter) with different tap configurations (e.g., 6-tap, 4-tap, 2-tap) and selecting the most appropriate filter based on content characteristics. This dynamic adaptation allows the system to optimize between complexity and efficiency for each video block, rather than using a fixed filter for all cases.
Solution Approach 2:
The patent changes filter parameters (number of taps, filter coefficients, filter type) based on video content characteristics. Different filter configurations are applied to different regions or blocks depending on their specific properties, allowing optimal balance between computational complexity and coding efficiency for each region.
2Measurement precision
If multiple filters are applied to generate sample values, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the filtering process into multiple candidate filters with different complexities. Instead of applying all filters uniformly, it divides the processing into stages where simpler filters may be used for some blocks and more complex filters for others, based on content requirements. This segmentation allows selective application of computational resources.
Solution Approach 2:
The patent applies partial filtering by selecting only the necessary number of filters (e.g., 1-3 candidate filters) based on content characteristics rather than always applying all possible filters. This partial action approach achieves sufficient prediction accuracy while avoiding unnecessary computational complexity.
3Ease of manufacture
If conventional video coding techniques are used, then implementation is simple, but coding efficiency is insufficient
Solution Approach 1:
The patent creates a universal filter selection mechanism that can adapt to different video content types, color formats, and block characteristics. The same framework supports multiple filter types and selection strategies, making the system universally applicable while maintaining implementation simplicity through standardized procedures.
Solution Approach 2:
The system performs self-service by automatically deriving and selecting appropriate filters based on video content characteristics without requiring manual configuration. The filter selection process is self-adaptive, using locally available information to determine the optimal filtering approach for each video block.
Data Source
AI summary
Embodiments of the disclosure provide a solution for video processing. A method for video processing is proposed. The method includes: generating, for a conversion between a video unit of a video and a bitstream of the video unit, a sample value of a first color component of the video unit that is corresponding to a sample of a second color component by applying a plurality of filters to at least one sample of the first color component; and performing the conversion based on the generated sample value.