Weighted Unit Intra Prediction for Complex VVC Textures
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Solution Overview
Problem
The accuracy of prediction in the template-based intra mode derivation (TIMD) solution for intra prediction in H.266/VVC needs improvement, particularly in handling blocks with complex textures.
Innovation Solution
Determine different weights for different units of a current block based on sub-templates, using first, second, and fourth weights to accurately calculate the intra prediction value, enhancing the accuracy of intra prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If template-based intra mode derivation (TIMD) is used to determine intra prediction value, then the intra prediction can be performed using correlation between template and current block, but the accuracy of prediction effect needs to be further improved
Solution Approach 1:
The current block is divided into multiple units (e.g., sub-blocks), and different weights are assigned to each unit based on its position and characteristics. This segmentation allows the prediction to adapt to local variations in texture and content, improving overall prediction accuracy without requiring a completely new prediction framework.
Solution Approach 2:
Different weights are applied to different units within the current block based on their specific characteristics. Units with higher reliability (e.g., those closer to the template or with better correlation) receive higher weights, while less reliable units receive lower weights. This local differentiation enhances prediction accuracy for complex textures while maintaining computational efficiency.
2Measurement precision
If different weights are set for different units of current block, then the intra prediction value can be more accurately determined, but the calculation complexity increases
Solution Approach 1:
Weight change rates are pre-calculated based on the positions of units and previously determined weights. These change rates are stored and reused to determine weights for subsequent units, avoiding redundant calculations. This preliminary action significantly reduces the computational complexity while maintaining the ability to adapt weights to local characteristics.
Solution Approach 2:
The weight determination process is simplified by copying and reusing weight change rates across different units. Instead of independently calculating weights for each unit based on complex criteria, the system copies the change rate pattern and applies it systematically, reducing computational overhead while preserving the adaptive weighting benefit.
Data Source
AI summary
An intra prediction method, an encoder, and a decoder are provided. In the method, after determining the weights of at least two prediction modes on at least two units in a current block, the rate of change of a weight in a certain direction can be determined further according to the weights of the at least two units, and according to the rate of change, the weights on other units on the current block can then be determined by means of a smooth transition to determine an intra prediction value of the current block.