Cross-Component Prediction Model Parameter Adjustment
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Solution Overview
Problem
Conventional video coding techniques face challenges in effectively utilizing neighbor samples for cross-component prediction, particularly in determining model parameters and scaling factors for chroma blocks, which affects the accuracy and efficiency of video encoding and decoding processes.
Innovation Solution
The proposed solution involves determining offset and scaling parameters for cross-component prediction models based on derived sample values from neighbor samples, where the number and positions of these samples are dependent on the block width and height, and applying resampling and bit-depth shift operations to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video coding techniques are used for cross-component prediction, then the encoding process is simple, but the prediction accuracy is insufficient
Solution Approach 1:
The patent dynamically determines the number and positions of neighbor samples based on block width and height parameters. It also dynamically selects prediction models (first model, second model, or third model) based on block characteristics, thereby adapting prediction parameters to specific coding scenarios to improve accuracy without excessive complexity
Solution Approach 2:
The patent divides the chroma block into different regions and applies different prediction models to different regions. Specifically, it uses a first prediction model for some blocks, a second prediction model for others, and a third prediction model for remaining blocks, allowing targeted optimization for different block types while managing overall complexity
2Adaptability or versatility
If fixed neighbor samples are used for prediction, then the encoding process is simple, but the adaptability to different block sizes is poor
Solution Approach 1:
The patent makes the number and positions of neighbor samples dynamic rather than fixed. The samples are determined based on the actual block width and height, allowing the prediction process to adapt to different block sizes and shapes while maintaining a systematic approach to sample selection
Solution Approach 2:
The patent applies different prediction strategies to different local regions based on block characteristics. By determining neighbor samples and selecting prediction models based on specific block dimensions and positions, it optimizes prediction quality for each local region rather than using a uniform approach
Data Source
AI summary
Example implementations include a method, apparatus and computer-readable medium of video coding, comprising receiving the code block and one or more neighbor samples and determining the value of beta based on at least one of an average chroma value, a midrange chroma value, a median chroma value, an average luma value, a midrange luma value, or a median luma value of two or more neighbor samples.