CCLM Prediction for Chroma Blocks in Image Decoding
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Solution Overview
Problem
Current image coding technologies face inefficiencies in compressing and transmitting high-resolution, high-quality images, leading to increased transmission and storage costs due to the high amount of information required.
Innovation Solution
The implementation of a Cross Component Linear Model (CCLM) prediction method in image coding systems, which involves deriving intra prediction modes for chroma blocks based on specific values and block sizes, and using down-sampled luma samples to generate prediction samples for chroma blocks, thereby improving encoding and decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image coding techniques are used for high-resolution images, then image quality is maintained, but transmission cost and storage cost increase due to high bit requirements
Solution Approach 1:
The patent applies parameter changes by modifying the prediction process to use cross-component linear model (CCLM) parameters that relate luma and chroma components. By changing how prediction parameters are derived and applied, the system achieves better compression efficiency while maintaining image quality, thus reducing the bit amount required for high-resolution images.
2Productivity
If conventional intra-prediction methods are used for chroma blocks, then encoding is simple, but coding efficiency is insufficient for high-resolution images
Solution Approach 1:
The patent segments the prediction process by separately handling luma and chroma components and deriving prediction modes based on their relationships. The chroma block prediction is segmented into different modes (planar, DC, gradient) based on the availability and characteristics of neighboring samples, allowing efficient coding while adapting to different block sizes and configurations.
Solution Approach 2:
The patent uses down-sampled luma samples as an intermediary to derive CCLM prediction parameters for chroma blocks. This intermediary approach allows the system to exploit the correlation between luma and chroma components without requiring direct access to full-resolution chroma neighboring samples, thereby improving coding efficiency while managing complexity.
3Measurement precision
If all neighboring chroma samples are used for large chroma blocks, then prediction accuracy improves, but computational complexity and memory requirements increase
Solution Approach 1:
The patent applies partial action by using only a subset of neighboring chroma samples for prediction, specifically limiting the number of samples to a manageable set (e.g., using down-sampled versions or selecting specific positions). This partial sampling approach maintains sufficient prediction accuracy for large chroma blocks while significantly reducing computational complexity and memory requirements compared to using all available neighboring samples.
Data Source
AI summary
An image decoding method performed by a decoding device according to the present document comprises: a step for deriving the number of samples of upper peripheral chroma samples and left peripheral chroma samples of a current chroma block on the basis of a specific value and the width and height of the current chroma block; a step for deriving said number of upper peripheral chroma samples and said number of left peripheral chroma samples; and a step for deriving CCLM parameters on the basis of the upper peripheral chroma samples, the left peripheral chroma samples, and down-sampled peripheral luma samples, wherein, when the specific value is derived as 2 and the width and the height of the current chroma block are larger than the specific value, the number of samples is derived as the specific value.