Multi-Hypothesis Chroma Prediction From Neighboring Luma Samples
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video coding technologies struggle to effectively utilize cross-component intra prediction to reduce bandwidth and storage requirements while maintaining video quality, particularly in applications with varying distortion tolerance.
Innovation Solution
Implementing cross-component intra prediction (CCIP) mode using a multi-hypothesis cross-component prediction (MH-CCP) approach, where chroma samples are predicted based on a linear or nonlinear weighted sum of luma samples, including a co-located luma sample and filtered neighboring luma samples, with weighing factors determined by a least mean square calculation kernel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If cross-component intra prediction is implemented using traditional methods, then bandwidth and storage requirements are reduced, but video quality deteriorates due to insufficient utilization of luma sample information
Solution Approach 1:
The patent segments the luma sample information into multiple hypotheses (first hypothesis using co-located luma samples, second hypothesis using filtered neighboring luma samples). This segmentation allows the system to process and utilize different portions of luma information separately, enabling more effective cross-component prediction while controlling bandwidth usage.
Solution Approach 2:
The patent combines multiple hypotheses (different processed versions of luma samples) into a composite prediction model. By merging the first hypothesis (co-located luma) and second hypothesis (filtered neighboring luma) through weighted averaging, the system creates a more robust prediction that maintains video quality while achieving compression.
2Reliability
If multiple hypotheses are used in cross-component prediction, then video quality is maintained or improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary filtering of neighboring luma samples to generate the second hypothesis before the final prediction stage. By pre-processing the luma information and creating filtered versions in advance, the system reduces the computational burden during the actual prediction and encoding phases, making the multi-hypothesis approach more efficient.
3Measurement precision
If filtering of neighboring luma samples is applied, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies filtering selectively to specific neighboring luma samples based on their relevance and quality. Rather than uniformly filtering all neighboring samples, the system identifies and filters only those samples that contribute most to prediction accuracy, thereby reducing processing time while maintaining prediction quality.
Data Source
AI summary
An example method for decoding video data includes receiving a video bitstream that includes a current block and a syntax element indicating whether each chroma sample of the current block is determined based on one or more luma samples. The method also includes generating a plurality of hypothesis values for predicting a first chroma sample by combining a plurality of neighboring luma samples of a first luma sample that is co-located with the first chroma sample using a plurality of coefficients. The plurality of hypothesis values include a first plurality of hypothesis values calculated based on top and bottom neighboring samples and respective sets of coefficients, and a second plurality of hypothesis values calculated based on left and right neighboring samples and respective sets of coefficients. The method further includes predicting the first chroma sample by combining the first luma sample and the plurality of hypothesis values.