CCLM Image Decoding with Thresholded Neighbor Sampling
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images leads to increased transmission and storage costs due to the higher amount of information required, necessitating a more efficient image compression technique.
Innovation Solution
An image decoding method and apparatus that enhances efficiency through intra prediction using a cross-component linear model (CCLM), selecting peripheral samples for deriving linear model parameters, and optimizing the number of downsampled neighboring samples based on the chroma block's dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of neighboring samples used for CCLM parameter derivation is increased, then the prediction accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by using only a predetermined threshold number of neighboring samples (e.g., 4 or 8 samples) instead of all available neighboring samples for CCLM parameter derivation. This selective sampling approach achieves satisfactory prediction accuracy while significantly reducing the computational complexity of deriving linear model parameters.
Solution Approach 2:
The patent changes the parameter of sample count from a variable depending on block size to a fixed predetermined threshold value. This parameter change simplifies the computation by establishing a constant upper limit on the number of samples used for CCLM derivation, regardless of the actual chroma block dimensions.
2Measurement precision
If high-resolution images are transmitted or stored, then the image quality is improved, but the transmission cost and storage cost increase
Solution Approach 1:
The patent extracts only the essential information needed for CCLM prediction by using a limited set of peripheral neighboring samples rather than processing all available luma samples. This extraction approach maintains prediction quality while reducing the amount of data that needs to be transmitted and stored.
Solution Approach 2:
The patent uses partial action by deriving CCLM parameters from only a predetermined threshold number of neighboring samples rather than all available samples. This reduces the computational burden and data processing requirements, thereby lowering transmission and storage costs while maintaining acceptable image quality.
3Productivity
If all available neighboring samples are used for CCLM derivation, then the coding efficiency is improved, but the processing time increases
Solution Approach 1:
The patent applies partial action by using only a predetermined threshold number of neighboring samples for CCLM parameter derivation instead of all available samples. This reduces the number of computations required for calculating linear model parameters, thereby decreasing processing time while maintaining reasonable coding efficiency.
Solution Approach 2:
The patent changes the parameter of sample count to a fixed predetermined threshold value, which reduces the variable computation time associated with processing different numbers of samples based on block size. This parameter change makes the processing time more predictable and generally shorter.
Data Source
AI summary
An image decoding method executed by a decoding apparatus according to the present document comprises the steps of: deriving an intra prediction mode of the current chroma block by means of cross-component linear model (CCLM) mode; deriving downsampled luma samples on the basis of the current luma block; deriving downsampled neighboring luma samples on the basis of neighboring luma samples of the current luma block; and deriving the CCLM parameter on the basis of the downsampled neighboring luma samples and neighboring chroma samples of the current neighboring chroma block, wherein the number of downsampled neighboring luma samples and number of neighboring chroma samples are equal to the previously set thresh-hold value, and the thresh-hold value is derived on the basis of the width and height of the current chroma block