CCLM Prediction Chroma Block Sample Selection
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality images has led to a need for more efficient image compression techniques to reduce transmission and storage costs, as conventional methods struggle with the high bit rates associated with such images.
Innovation Solution
The implementation of Cross-Component Linear Model (CCLM) prediction in video coding systems, which involves deriving CCLM parameters using a limited number of neighboring samples for chroma blocks, reducing intra-prediction complexity and improving encoding and decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CCLM prediction is applied to chroma blocks using all neighboring samples, then prediction accuracy is improved, but computational complexity and hardware cost increase
Solution Approach 1:
The patent extracts only the necessary neighboring samples (specifically, samples at predetermined positions such as top and left neighbors) from the full set of available neighboring chroma samples. By selectively using only these critical samples for CCLM parameter derivation, the patent maintains prediction accuracy while significantly reducing the number of operations required, thus lowering computational complexity and hardware cost.
Solution Approach 2:
The patent applies different treatment to different neighboring samples based on their local importance. Instead of uniformly processing all neighboring samples, the patent identifies and processes only the locally significant samples (e.g., top and left neighbors) that have the most impact on prediction accuracy. This localized approach optimizes the balance between prediction quality and computational effort.
2Device complexity
If the number of neighboring samples used for CCLM parameter derivation is reduced, then hardware complexity is reduced, but prediction efficiency may deteriorate
Solution Approach 1:
The patent extracts and uses only the most critical neighboring samples (top and left neighbors) for CCLM parameter derivation. This selective extraction maintains prediction efficiency by focusing computational resources on the samples that contribute most significantly to accurate chroma prediction, while avoiding the overhead of processing all available neighboring samples.
Solution Approach 2:
The patent changes the parameter of sample quantity from using all neighboring samples to using a limited set of predetermined samples. This parameter change optimizes the trade-off between hardware complexity and prediction efficiency, achieving acceptable prediction performance with reduced computational burden.
3Productivity
If conventional image compression is used for high-resolution images, then transmission and storage costs increase, but compression efficiency is insufficient
Solution Approach 1:
The patent replaces conventional mechanical compression methods with a sophisticated prediction-based approach using CCLM. By substituting traditional compression techniques with predictive modeling that leverages chroma-luma correlations and neighboring sample relationships, the patent achieves superior compression efficiency for high-resolution images, thereby reducing transmission and storage costs.
Data Source
AI summary
A video decoding method performed by a decoding apparatus according to the present disclosure includes deriving one of a plurality of cross-component linear model (CCLM) prediction mode as a CCLM prediction mode of the current chroma block, deriving a sample number of neighboring chroma samples of the current chroma block based on the CCLM prediction mode of the current chroma block, a size of the current chroma block, and a specific value; deriving the neighboring chroma samples of the sample number, calculating CCLM parameters based on the neighboring chroma samples and the down sampled neighboring luma samples, deriving prediction samples for the current chroma block based on the CCLM parameters and the down sampled luma samples and generating reconstructed samples for the current chroma block based on the prediction samples, wherein the specific value is derived as 2.