CCLM Image Decoding with Threshold-Based Neighbor Sampling
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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 the increased amount of information, necessitating a more efficient image compression technique.
Innovation Solution
The method and apparatus enhance image coding efficiency by using a cross-component linear model (CCLM) for intra prediction, deriving downsampled luma and chroma samples based on predetermined threshold values derived from the chroma block's width and height, reducing the complexity of selecting neighboring samples for linear model parameter derivation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution and high-quality images are transmitted or stored, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies parameter changes by using a 2:1 downsampled luma sample configuration instead of traditional 4:2:0 or 4:4:4 chroma subsampling. This changes the sampling parameters to achieve better compression efficiency while maintaining perceived image quality, as human vision is more sensitive to luma variations than chroma variations.
Solution Approach 2:
The patent segments the image data processing into separate luma and chroma components with different sampling rates. By separating the processing of luminance and chrominance information and applying different downscaling factors, the patent optimizes the balance between image quality and data reduction.
2Measurement precision
If the number of neighboring samples is increased for CCLM parameter derivation, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by using only a subset of available neighboring samples for CCLM parameter derivation. Instead of using all neighboring samples, the patent selectively uses a limited number (e.g., 4 samples) to derive the linear model parameters, achieving sufficient prediction accuracy while significantly reducing computational complexity.
Solution Approach 2:
The patent extracts only the necessary neighboring samples required for effective CCLM prediction. By identifying and using only the most relevant samples (taking out the essential subset), the patent avoids the computational burden of processing all available neighboring samples while maintaining prediction effectiveness.
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 down-sampled 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.