CCLM Chroma Prediction Using Limited Samples for Image Decoding
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality images has led to an increase in transmission and storage costs due to the higher amount of information required, necessitating a more efficient image compression technique.
Innovation Solution
The method and device improve image coding efficiency by utilizing Cross Component Linear Model (CCLM) for intra-prediction, specifically by deriving CCLM parameters based on limited neighboring samples for chroma blocks, reducing complexity and enhancing prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image compression techniques are used for high-resolution images, then transmission and storage costs increase, but image quality and resolution requirements cannot be met
Solution Approach 1:
The patent changes the parameter of prediction sample selection by using a specific value (e.g., 2) to limit the number of neighboring samples used for CCLM parameter derivation. This reduces the complexity of intra-prediction while maintaining prediction accuracy, thereby improving compression efficiency and reducing transmission and storage costs for high-resolution images
2Measurement precision
If CCLM prediction uses all neighboring samples, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent extracts only the necessary number of neighboring samples (limited by a specific value) for CCLM parameter derivation, discarding the rest. This selective extraction maintains sufficient prediction accuracy while significantly reducing the computational complexity of deriving CCLM parameters from all available neighboring samples
3Measurement precision
If more neighboring samples are used for CCLM parameter derivation, then prediction precision improves, but processing time increases
Solution Approach 1:
The patent applies partial action by using only a limited number of neighboring samples (not all available samples) for CCLM parameter derivation. This partial sampling maintains adequate prediction precision while reducing the processing time required to compute CCLM parameters, achieving a balance between accuracy and efficiency
Data Source
AI summary
An image decoding method performed by a decoding apparatus according to the present document includes deriving a CCLM mode as an intra prediction mode of a current block, deriving a specific value for the current chroma block, deriving, based on the width and the height of the current chroma block being greater than or equal to the specific value, top neighboring chroma samples whose number is equal to the specific value of the current chroma block, and left neighboring chroma samples whose number is equal to the specific value, deriving CCLM parameters based on the top neighboring chroma samples, the left neighboring chroma samples, and 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 derived specific value is 2.