Chroma Block Prediction with Reconstructed Luma Filtering
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Solution Overview
Problem
Existing image encoding/decoding methods struggle with efficient prediction of chroma blocks, particularly in high-resolution and high-quality images, leading to increased data volume and transmission/storage costs.
Innovation Solution
The method involves deriving filter coefficients for chroma block prediction using reconstructed luma blocks, applying filters based on luma and chroma component linearity, and performing prediction in sub-block units, with filter parameters determined by similarity between sub-blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality image data is transmitted or stored using existing methods, then image quality is improved, but transmission and storage expenses increase due to increased data volume
Solution Approach 1:
The image data is divided into luma (Y) and chroma (CbCr) components, allowing independent processing and prediction. The chroma prediction is further segmented into sub-blocks for more precise local prediction, reducing the overall data volume while maintaining high image quality
Solution Approach 2:
The luma block serves as an intermediary to predict chroma blocks. By using the already-encoded luma information as a reference for chroma prediction, the system reduces chroma data volume without requiring separate high-resolution chroma data, thus lowering transmission and storage expenses while maintaining image quality
2Productivity
If chroma block prediction is performed using existing methods, then encoding speed is maintained, but prediction accuracy is insufficient leading to larger data volume
Solution Approach 1:
The chroma block prediction is divided into sub-blocks, allowing independent filter coefficient derivation for each sub-block. This segmentation enables more accurate local prediction while maintaining encoding efficiency through template matching and gradient-based methods that can be computed rapidly
Solution Approach 2:
Filter coefficients are dynamically derived based on local image characteristics such as gradients and template matching results. By adapting the prediction parameters to local variations in the image content, the system achieves higher prediction accuracy without significantly increasing encoding complexity or reducing encoding speed
Data Source
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AI summary
An image encoding/decoding method according to the present invention comprises the steps of: deriving filter coefficients for prediction of a chroma block; and on the basis of the filter coefficients, deriving a prediction sample for the chroma block by filtering a plurality of luma reconstructed samples within a luma block corresponding to the chroma block.