Multi-model Chroma Prediction via Region Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video coding standards, such as HEVC and VVC, face challenges in efficiently predicting chroma samples, which affects coding efficiency and quality, especially in diverse video content with varying resolutions and bitrates.
Innovation Solution
A video coding system that employs multiple cross-component linear models (CCLM) to predict chroma samples by constructing models based on neighboring luma and chroma samples, and combining these predictions using weighted sums to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple chroma prediction models are constructed and combined, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The current block is divided into multiple regions (e.g., top region, left region, and other regions), and different prediction models are applied to different regions. This segmentation allows the system to use multiple models only where necessary, improving prediction accuracy for specific regions while limiting the overall computational complexity increase.
Solution Approach 2:
Different prediction models (LM-T, LM-L, LM-LT) are selectively applied to different spatial regions of the current block based on local characteristics. The top region uses LM-T model, the left region uses LM-L model, and other regions use LM-LT model, ensuring that each region receives the most appropriate prediction approach for its local properties.
2Productivity
If multiple prediction models are used for different regions, then coding efficiency is improved, but processing time increases
Solution Approach 1:
The current block is divided into multiple regions (e.g., top region, left region, and other regions), and different prediction models are applied to different regions. This segmentation allows the system to use multiple models only where necessary, improving prediction accuracy for specific regions while limiting the overall computational complexity increase.
Solution Approach 2:
Instead of applying multiple prediction models to the entire current block, the invention applies different models only to specific regions where they are most beneficial. The top region uses LM-T model, the left region uses LM-L model, and other regions use LM-LT model, ensuring that each region receives the most appropriate prediction approach for its local properties.
Data Source
AI summary
A video coding system that uses multiple models to predict chroma samples is provided. The video coding system receives data for a block of pixels to be encoded or decoded as a current block of a current picture of a video. The system constructs two or more chroma prediction models based on luma and chroma samples neighboring the current block. The system applies the two or more chroma prediction models to incoming or reconstructed luma samples of the current block to produce two or more model predictions. The system computes predicted chroma samples by combining the two or more model predictions. The system uses the predicted chroma samples to reconstruct chroma samples of the current block or to encode the current block.