CCLM Parameter Derivation for Video Coding Efficiency
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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 video codecs struggling to balance complexity and compression efficiency.
Innovation Solution
The implementation of a cross-component linear model (CCLM) prediction mode in video coding, which predicts chroma samples based on reconstructed luma samples using a linear model, and includes methods for deriving model parameters from neighboring samples, such as the two-point method and multi-directional LM modes, to enhance coding efficiency and reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional video coding methods are used for high-resolution videos, then video quality can be maintained, but bandwidth consumption and computational complexity increase significantly
Solution Approach 1:
The patent changes the parameter selection strategy by using only two specific chroma samples (at predetermined positions) instead of multiple samples for deriving CCLM parameters. This parameter reduction directly lowers computational complexity while maintaining coding efficiency through the use of position-based selection rules that ensure representative sample choices.
Solution Approach 2:
The patent extracts only the essential elements needed for CCLM parameter derivation by selecting two specific chroma samples from the available neighboring samples. This extraction approach removes unnecessary computational steps involving other samples while preserving the core functionality of the cross-component linear model.
2Measurement precision
If more chroma samples are used to derive CCLM parameters, then prediction accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent uses simple, easily accessible chroma samples at predetermined positions rather than complex or distant samples. These samples are 'cheap' in terms of processing effort and immediately available in the neighboring block, enabling fast parameter derivation without sacrificing prediction accuracy.
Solution Approach 2:
The patent changes from using multiple chroma samples to using exactly two samples at specific positions. This parameter change reduces processing time while maintaining accuracy by focusing on the most informative samples based on their positional relationships to the current block.
3Productivity
If existing CCLM derivation methods are used, then coding efficiency can be maintained, but implementation complexity increases
Solution Approach 1:
The patent segments the chroma sample selection process by defining specific positions (e.g., top-left and bottom-left corners of the neighboring chroma block) from which samples are taken. This segmentation simplifies implementation by providing clear, discrete selection rules rather than requiring complex optimization algorithms.
Solution Approach 2:
The patent changes the derivation approach from complex multi-sample optimization to simple two-sample arithmetic based on predetermined positions. This parameter change makes implementation easier while maintaining coding efficiency through straightforward calculations.
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 and a coded representation of the video, a context that is used to code a flag using arithmetic coding in the coded representation of the current video block, wherein the context is based on whether a top-left neighboring block of the current video block is coded using a cross-component linear model (CCLM) prediction mode; and performing the conversion based on the determining.