Cross-Component Chroma Prediction With Adaptive Multi-Hypothesis Taps
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Solution Overview
Problem
Existing video coding technologies struggle to efficiently compress video data while maintaining high quality, particularly in applications with varying distortion tolerance levels, such as consumer streaming and cinematic broadcasting.
Innovation Solution
Implement cross-component intra prediction (CCIP) using a multi-hypothesis cross-component prediction (MH-CCP) mode, where chroma samples are predicted based on weighted sums of luma samples, with hypothesis tap indices and weighing factors determined for each coding block, to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video coding compression is increased to reduce bandwidth and storage requirements, then data transmission efficiency is improved, but video quality deteriorates
Solution Approach 1:
The patent applies parameter changes by using multiple hypothesis tap combinations (different filter configurations) to predict chroma samples from luma samples. By adjusting the hypothesis parameters (tap indices and weighting factors) based on local image characteristics, the system achieves better compression efficiency while maintaining video quality across different distortion tolerance scenarios
Solution Approach 2:
The patent implements dynamics by adaptively selecting different hypothesis tap combinations for different coding blocks based on their local characteristics. The system dynamically adjusts the prediction parameters (hypothesis indices and weighting factors) to match the local image content, allowing optimal compression quality trade-offs for varying distortion tolerances in different regions
2Productivity
If multi-hypothesis cross-component prediction with multiple tap combinations is implemented, then compression efficiency is improved, but decoding complexity increases
Solution Approach 1:
The patent applies partial action by signaling only the necessary hypothesis tap index information in the bitstream rather than transmitting all possible prediction parameters. The decoder uses the signaled index to select from predefined hypothesis combinations, reducing the complexity overhead while still achieving adaptive prediction for different local characteristics
Data Source
AI summary
The various implementations described herein include methods and systems for coding video. In one aspect, a video bitstream includes a current coding block of an image frame and signals a cross-component intra prediction mode and a hypothesis tap index. An computing system identifies a first luma sample and a first chroma sample co-located with the first luma sample in the current coding block, and selects one of a plurality of hypothesis tap combinations based on the hypothesis tap index. The computing system identifies neighboring luma samples of the first luma sample based on the selected hypothesis tap combinations, and generates hypothesis values based on the identified neighboring luma samples of the first luma sample. The computing system further generates the first chroma sample based on at least the first luma sample and the hypothesis values, and reconstructs the current coding block including the first chroma sample.