Chroma Intra Prediction Using CCLM for Lower Image Bitrate
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Solution Overview
Problem
Existing image encoding/decoding technologies face inefficiencies in handling high-resolution and high-quality images, leading to increased transmission and storage costs, and there is a need for improved methods to efficiently perform intra prediction of chroma signals.
Innovation Solution
The method involves deriving representative values from neighboring luma and chroma samples using a Cross-Component Linear Model (CCLM) to generate prediction blocks for chroma components, optimizing the intra-prediction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high resolution and quality images are used, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies parameter changes by using Cross-Component Linear Model (CCLM) to establish linear relationships between luma and chroma components. By deriving CCLM parameters (weights and offsets) from neighboring samples and applying them to generate chroma prediction blocks, the method achieves efficient compression of high-resolution images while maintaining quality, thus resolving the contradiction between image quality and data amount.
2Ease of manufacture
If conventional intra prediction methods are used, then encoding process is simple, but chroma signal prediction efficiency is insufficient
Solution Approach 1:
The patent introduces CCLM parameters (weight and offset) as intermediaries to bridge luma and chroma components. These parameters are derived from neighboring samples and used to generate accurate chroma prediction blocks. This intermediary approach maintains relative encoding simplicity while significantly improving chroma prediction efficiency compared to conventional methods.
3Measurement precision
If more neighboring samples are used for CCLM parameter derivation, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by selectively using neighboring samples based on their availability and relevance. The method derives CCLM parameters from neighboring luma and chroma samples within the current block, using local statistical properties to achieve accurate prediction without requiring global information, thus balancing accuracy and complexity.
Solution Approach 2:
The patent uses partial action by deriving CCLM parameters from a subset of neighboring samples rather than all possible samples. The method selectively processes available neighboring samples to compute weights and offsets, achieving sufficient prediction accuracy with reduced computational effort compared to using all possible samples.
Data Source
AI summary
An image encoding/decoding method is provided. An image decoding method of the present invention may comprise restoring an intra-prediction mode of a current chroma block, deriving at least one representative value based on a neighboring sample of the current chroma block and a neighboring sample of a corresponding luma block corresponding to the current chroma block in case the intra-prediction mode is a CCLM (Cross-Component Linear Model) mode, deriving a parameter of CCLM by using the at least one representative value, and generating a prediction block of the current chroma block by using the parameter of CCLM.