CCLM Chroma Block Decoding With Down-Sampled Luma Prediction
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images has led to a need for more efficient image compression techniques to reduce transmission and storage costs, as conventional methods struggle with the increased amount of information required for these images.
Innovation Solution
The implementation of a method and device for improving image coding efficiency through intra-prediction based on Cross Component Linear Model (CCLM), which involves deriving CCLM parameters using specific neighboring samples and down-sampled luma samples to generate prediction samples for chroma blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image compression techniques are used for high-resolution images, then image quality can be maintained, but transmission cost and storage cost increase due to the large amount of data
Solution Approach 1:
The patent applies parameter changes by modifying the prediction process through CCLM mode derivation. It changes the parameters used in intra-prediction by deriving CCLM parameters from neighboring samples and using down-sampled luma samples to predict chroma blocks, thereby improving compression efficiency while maintaining image quality
Solution Approach 2:
The patent uses copying by deriving prediction samples for chroma blocks from luma samples through the CCLM model. Instead of encoding chroma data independently, it creates a predicted copy based on the correlation between luma and chroma components, reducing the amount of data that needs to be transmitted or stored
2Measurement precision
If the number of neighboring samples used for CCLM parameter derivation is increased, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies local quality by selectively using different numbers of neighboring samples based on the block size. For small blocks (width or height less than 32), it uses a limited number of neighboring samples (2 or 4), while for large blocks, it uses more samples. This localized approach optimizes the balance between prediction accuracy and computational complexity for different regions
Solution Approach 2:
The patent segments the processing based on block size categories. It divides chroma blocks into small blocks (width or height < 32) and large blocks (width or height >= 32), applying different CCLM parameter derivation methods to each segment. This segmentation allows the system to manage computational complexity while maintaining prediction accuracy for each block size category
Data Source
AI summary
A method for image decoding performed by a decoding device according to the present document comprises the steps of: deriving a specific value for a current chroma block; when a width and a height of the current chroma block have a value equal to or greater than the specific value, deriving upper peripheral chroma samples of a number equal to the specific value of the current chroma block and left peripheral chroma samples of a number equal to the specific value; deriving CCLM parameters on the basis of the upper peripheral chroma samples, the left peripheral chroma samples, and down-sampled peripheral luma samples; and deriving prediction samples for the current chroma block on the basis of the down-sampled luma samples and the CCLM parameters, wherein the derived specific value is 2.