Cross-Component Linear Model Prediction Down-Sampling
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Solution Overview
Problem
Current video coding methods face challenges in efficiently compressing high-resolution videos due to increased bandwidth demands, with existing cross-component linear model (CCLM) implementations requiring excessive neighboring luma samples and suffering from coding performance losses when using fewer samples.
Innovation Solution
The proposed method involves using a cross-component linear model for video coding that down-samples luma samples inside and outside a block differently, depending on their position, and applies various filtering methods to derive prediction models, reducing the number of required neighboring samples while maintaining coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CCLM methods use all available neighboring luma samples for linear model derivation, then prediction accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the neighboring luma samples into two distinct groups: inside samples (within the current block boundaries) and outside samples (beyond the current block boundaries). This segmentation allows different processing strategies to be applied to each group, reducing overall computational complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent applies partial action by selectively using only the necessary subset of neighboring samples for linear model derivation. Instead of processing all available samples, it uses a controlled combination of inside and outside samples, reducing computational load while achieving sufficient prediction accuracy for video coding applications.
2Device complexity
If fewer neighboring luma samples are used for CCLM prediction, then computational complexity is reduced, but coding performance deteriorates
Solution Approach 1:
The patent applies local quality by treating inside and outside luma samples differently based on their spatial relationship to the current block. Inside samples are processed with one down-sampling approach while outside samples use a different approach, optimizing the balance between complexity and performance for each sample group's specific characteristics.
Solution Approach 2:
The patent changes the processing parameters (down-sampling filters) based on the sample group being processed. Different filter configurations are applied to inside versus outside samples, allowing the system to adapt the computational approach to maintain coding performance while reducing overall complexity.
3Ease of operation
If down-sampling filters are applied to all luma samples uniformly, then processing is simplified, but prediction precision for different spatial regions is compromised
Solution Approach 1:
The patent implements local quality by applying different down-sampling filter characteristics to inside and outside luma samples. This allows each spatial region to be processed with filters optimized for its specific characteristics, maintaining prediction precision while still providing a systematic and manageable processing approach.
Data Source
AI summary
Devices, systems, and methods for digital video coding that include cross-component prediction are described. In a representative aspect, a method for video coding includes receiving a bitstream representation of a current block of video data comprising a luma component and a chroma component, determining parameters of a linear model based on a first set of samples that are generated by down-sampling a second set of samples of the luma component, and processing, based on the parameters of the linear model, the bitstream representation to generate the current block.