Multi-Hypothesis Cross-Component Prediction for Chroma Coding
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Solution Overview
Problem
Existing video coding methods struggle to efficiently compress video data while maintaining high quality, particularly in applications with varying distortion tolerance levels, as they often rely on single nonlinear terms that do not fully utilize the redundancy in video data.
Innovation Solution
Implementing multi-hypothesis cross-component prediction (MH-CCP) that uses a weighted sum of multiple luma samples, including nonlinear terms, to predict chroma samples, reducing the need to transmit chroma samples in the video bitstream and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If video data is compressed using conventional single-nonlinear-term methods, then bandwidth and storage requirements are reduced, but compression efficiency and quality are insufficient
Solution Approach 1:
The patent segments the prediction process into multiple hypotheses, each using different nonlinear terms (e.g., squared terms, cubic terms, interaction terms) to model the relationship between luma and chroma components. This segmentation allows the system to explore multiple prediction paths and select the optimal one, thereby improving compression efficiency while maintaining low bandwidth consumption.
Solution Approach 2:
The patent introduces a new dimension to the prediction model by incorporating multiple nonlinear terms beyond the conventional single term. This dimensional expansion enables the model to capture more complex relationships in video data, leading to higher compression ratios and better quality preservation without increasing the transmitted data size.
2Loss of energy
If multiple video coding standards are used to optimize compression, then bandwidth efficiency improves, but system complexity increases
Solution Approach 1:
The patent creates a universal multi-hypothesis prediction framework that can be integrated into various video coding standards (HEVC, VVC, AV1) without requiring separate implementations for each standard. This multi-functional approach allows the same core technology to improve compression efficiency across different coding frameworks while managing system complexity through a unified design.
3Productivity
If lossy compression is applied to reduce bandwidth, then compression ratio increases, but video quality degrades
Solution Approach 1:
The patent changes the parameters of the prediction model by using multiple nonlinear terms with different weights. By optimizing these parameters, the system achieves higher compression ratios while maintaining video quality. The ability to adjust and fine-tune prediction parameters allows for optimal balance between compression efficiency and quality preservation.
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 syntax element for a multi-hypothesis cross-component prediction (MH-CCP) mode. The syntax element indicates whether to reconstruct a first chroma sample of the current coding block based on a first luma sample and associated neighboring luma samples. The first luma sample is collocated with the first chroma sample. A computing system identifies the first luma sample and one or more neighboring luma samples in the current coding block, generates a plurality of nonlinear terms based on at least a subset of the first luma sample and the one or more neighboring luma samples, predicts the first chroma sample based on the plurality of nonlinear terms, and reconstructs the current image frame including the current coding block.