Cross-Component Chroma Prediction Corrections for Video Coding
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Solution Overview
Problem
Existing video coding schemes face challenges in accurately predicting chroma samples from luma samples due to variations in image characteristics, leading to inefficiencies in compression and reconstruction processes.
Innovation Solution
Implementing a correction mechanism for cross-component models, such as CCLM and CCCM, by signaling correction values to improve the prediction accuracy of chroma samples based on corresponding luma samples, using methods like least-squares optimization and autocorrelation matrix calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a cross-component model is used to predict chroma samples from luma samples, then compression efficiency is improved, but prediction accuracy deteriorates due to variations in image characteristics
Solution Approach 1:
The patent applies dynamics by making the cross-component model parameters adaptive rather than fixed. The encoder determines correction values based on actual image characteristics and signals them to the decoder, allowing the prediction model to dynamically adjust to different image types and conditions. This resolves the contradiction by maintaining high compression efficiency while improving prediction accuracy through adaptability.
Solution Approach 2:
The patent changes the parameters of the cross-component model by introducing correction values that modify the original model's prediction results. These correction values are derived through least-squares optimization and autocorrelation matrix calculations, allowing the model parameters to be adjusted according to specific image characteristics, thereby improving prediction accuracy without sacrificing compression efficiency.
2Measurement precision
If correction values are signaled to improve prediction accuracy, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements feedback by having the encoder calculate correction values based on actual image data and signal them back to the decoder. This feedback mechanism allows the system to continuously improve prediction accuracy by adjusting model parameters based on real image characteristics, while the feedback loop is integrated into the existing encoding/decoding framework to minimize added complexity.
Solution Approach 2:
The patent applies preliminary action by pre-calculating correction values during the encoding process before the actual prediction is performed. The encoder determines these correction values in advance using least-squares optimization, and the decoder simply applies them during decoding. This preliminary computation reduces the complexity during the actual prediction operation.
3Measurement precision
If correction values are calculated using least-squares optimization, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent replaces complex mechanical computation systems with more efficient mathematical approaches. Instead of using computationally intensive methods to determine model parameters, the patent employs least-squares optimization and autocorrelation matrix calculations that are more suitable for digital signal processing. This substitution maintains high prediction accuracy while reducing computational complexity through mathematically optimized algorithms.
Data Source
AI summary
Apparatuses and methods are disclosed including techniques for encoding video data. Techniques disclosed include obtaining video data, including data representing a video data region, and obtaining correction value(s) that represent a correction to a cross-component model for predicting a chroma sample from the video data region based on a corresponding luma sample. Then, encoding the correction value(s) and the video data into coded video data. Apparatuses and methods are also disclosed that include techniques for decoding coded video data. Techniques disclosed include receiving coded video data, coding video data including data representing a video data region and correction value(s). Then, decoding the correction value(s) from the coded video data. The decoded correction value(s) are used to adjust the cross-component model, applied for intra-prediction of a chroma sample from the video data region.