Multi-Hypothesis Cross-Component Prediction for Chroma Bandwidth Reduction
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Solution Overview
Problem
Existing video coding methods struggle to efficiently compress video data while maintaining high quality, particularly in scenarios where bandwidth and storage limitations are constraints, especially in applications with varying tolerance for distortion levels.
Innovation Solution
Implementing multi-hypothesis cross-component prediction (MH-CCP) using a multi-tap model that combines luma samples with weighted sums and model parameters to predict chroma samples, allowing for subsets of model parameters to be determined successively, thereby reducing the need to transmit chroma samples in the video bitstream and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If chroma samples are transmitted in the video bitstream, then video quality is maintained, but communication bandwidth is consumed
Solution Approach 1:
The patent creates a predictive copy of chroma samples by deriving them from luma samples through a multi-tap prediction model. Instead of transmitting actual chroma samples, the encoder transmits model parameters that enable the decoder to reconstruct chroma samples locally, significantly reducing bitstream overhead while maintaining visual quality
Solution Approach 2:
The patent introduces luma samples as an intermediary between the transmitted data and the reconstructed chroma samples. By using luma samples (which are always transmitted) as predictors for chroma samples through the multi-tap model, the system indirectly transmits chroma information without directly encoding it, thus saving bandwidth
2Measurement precision
If multi-tap model with multiple model parameters is used, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the chroma prediction process into multiple independent multi-tap models, each handling different chroma components or regions. By dividing the prediction task into separate models with their own parameter sets, the system achieves high prediction accuracy for each segment while keeping individual model complexities manageable
Solution Approach 2:
The patent dynamically adjusts model parameters based on local image characteristics and prediction accuracy requirements. By changing parameters adaptively rather than using fixed values, the system optimizes the balance between prediction accuracy and computational complexity for different video content regions
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 by combining a set of luma samples including a first luma sample based on a model having a plurality of model parameters. When the MH-CCP mode is enabled, a computing system determines a first subset of model parameters and a second subset of model parameters used in the MH-CCP mode for the current coding block successively. The computing system combines the first luma sample and a set of neighboring luma samples using the plurality of model parameters to generate the first chroma sample, and reconstructs the current coding block including the first chroma sample.