Chroma Intra Prediction Using Multi-Model Luma-Based Block Fusion
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images leads to higher transmission and storage costs due to increased bit rates, necessitating improved image encoding/decoding efficiency, especially in intra prediction methods.
Innovation Solution
An image encoding/decoding method and apparatus that utilizes various linear models (LM) modes for intra prediction, incorporating multiple reference lines and prediction blocks, and combines chroma prediction blocks using weighted sums based on different modes such as Cross-component Linear Model (CCLM), Multi-Model Linear Mode (MMLM), Convolutional Cross-Component Model (CCCM), and Gradient Linear Model (GLM).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality image data is transmitted, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The current block is divided into multiple prediction blocks (first prediction block, second prediction block, etc.), each processed with different linear model modes. This segmentation allows selective application of complex prediction methods only where needed, improving overall compression efficiency without uniformly increasing processing complexity across the entire block.
Solution Approach 2:
The patent applies different linear model parameters (different prediction modes such as CCLM, MMLM, CCCM, GLM) to different prediction blocks based on local characteristics. By changing parameters adaptively rather than using a fixed mode for the entire block, the method achieves better prediction accuracy and reduces residual energy, thereby lowering bit rate requirements.
2Measurement precision
If multiple linear model modes are used for intra prediction, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The encoding process segments the current block into multiple prediction blocks, each handled with a specific linear model mode. This segmentation strategy allows the system to manage complexity by processing smaller sub-blocks independently, making the overall complex process more tractable and enabling parallel processing optimizations.
Solution Approach 2:
The patent applies multiple linear model modes selectively to different prediction blocks rather than applying all modes uniformly to the entire block. This partial action approach achieves the benefit of improved prediction accuracy in regions where it matters most while avoiding the unnecessary complexity of applying all modes everywhere.
Data Source
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AI summary
An image encoding/decoding method and device are provided. The image decoding method according to the present disclosure may comprise the steps of: determining a first prediction mode and a second prediction mode of a current chroma block, wherein the first prediction mode and the second prediction mode are prediction modes for predicting the current chroma block on the basis of a luma block corresponding to the current chroma block; deriving a first chroma prediction block on the basis of the first prediction mode; deriving a second chroma prediction block on the basis of the second prediction mode; and deriving a final prediction block of the current chroma block on the basis of the first chroma prediction block and the second chroma prediction block, wherein the first chroma prediction block and the second chroma prediction block are derived on the basis of at least two among an upper reference area, a left reference area, and a left upper reference area neighboring the current chroma block.