Cross-Component Linear Model Parameter Derivation in Video Coding
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Solution Overview
Problem
Current video coding methods face challenges in efficiently compressing and decompressing high-resolution videos due to increased bandwidth demands, with existing implementations introducing complexity and computational overhead in cross-component prediction techniques.
Innovation Solution
The proposed solution involves simplified linear model derivations for cross-component prediction in video coding, specifically using a cross-component linear model (CCLM) that predicts chroma samples based on luma samples, with adaptive selection of neighboring samples and downsampling filters to improve coding efficiency and reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-component prediction techniques are used to improve video coding efficiency, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent divides the current block into multiple regions (e.g., top region, left region, and other regions) and applies different prediction strategies to each region. For example, the top and left regions may use one set of neighboring samples for parameter derivation, while other regions use a different set. This segmentation allows the system to achieve good prediction accuracy in each region without requiring complex global processing, thus reducing overall computational complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent applies different prediction parameters and neighboring sample selections to different spatial regions of the current block. Instead of using a uniform prediction approach across the entire block, the system adapts the prediction parameters locally for each region based on the availability and quality of neighboring samples. This local quality approach improves prediction accuracy in each specific region while avoiding the need for complex global optimization.
2Measurement precision
If more neighboring samples are used to derive CCLM parameters, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts and uses only the necessary neighboring samples for parameter derivation, rather than processing all available neighboring samples. By selectively choosing which neighboring samples to use (e.g., only top neighbors for certain regions, only left neighbors for other regions), the system reduces the number of samples that need to be processed while still deriving accurate CCLM parameters for each region, thus reducing processing time without significantly compromising accuracy.
Solution Approach 2:
The patent uses a partial set of neighboring samples sufficient for accurate parameter derivation in each region, rather than using all available neighboring samples. This partial action approach provides enough information for good prediction accuracy while avoiding the excessive processing time that would result from using all neighboring samples, achieving an optimal balance between accuracy and processing time.
3Quantity of substance
If downscaling operations are performed on luma samples, then memory usage is reduced, but computational overhead increases
Solution Approach 1:
The patent performs downscaling of luma samples in advance, before the actual CCLM parameter derivation and prediction processes. By pre-processing the luma samples to the required resolution, the system reduces the amount of data that needs to be stored and processed in subsequent steps, thereby reducing memory usage. The preliminary downscaling operation is performed once, and the results are reused, which minimizes the overall computational overhead compared to performing downscaling multiple times during processing.
Data Source
AI summary
A method for video processing is provided. The method includes determining, for a conversion between a current video block of a video that is a chroma block and a coded representation of the video, whether to derive maximum values and/or minimum values of a luma component and a chroma component that are used to derive parameters of a cross-component linear model (CCLM) based on availability of a left-neighboring block and an above-neighboring block of the current video block; and performing the conversion based on the determining.