Intra Prediction Weighting for Video Coding Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video coding techniques face challenges in achieving improved compression ratios with minimal sacrifice in picture quality, particularly in the context of intra prediction, where post-filtering processes can increase complexity and duration without significant performance benefits.
Innovation Solution
The proposed method involves determining a weighted prediction value for a current block based on its dimensions and intra prediction mode, using specific formulas and conditions to derive the weighted prediction value only when necessary, thereby reducing processor load and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If post-filtering is applied to all current blocks, then prediction accuracy is improved, but processing complexity and duration increase significantly
Solution Approach 1:
The patent applies position-dependent prediction combination (PDPC) selectively based on the spatial position of samples within the current block. Specifically, PDPC is applied to boundary samples (e.g., top and left edges) where prediction accuracy is most critical, while interior samples use standard intra prediction. This local differentiation improves overall prediction accuracy without incurring the full processing cost of applying PDPC uniformly across all samples.
Solution Approach 2:
The patent implements partial post-filtering by applying PDPC only to specific samples that benefit most from it, rather than to all samples. The decision to apply PDPC is based on position-dependent criteria, making the filtering action partial rather than excessive. This approach achieves sufficient prediction accuracy for boundary-critical regions while avoiding unnecessary processing overhead in regions where standard prediction is adequate.
2Measurement precision
If post-filtering is applied to all current blocks, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies position-dependent prediction combination (PDPC) selectively based on the spatial position of samples within the current block. Specifically, PDPC is applied to boundary samples (e.g., top and left edges) where prediction accuracy is most critical, while interior samples use standard intra prediction. This local differentiation improves overall prediction accuracy without incurring the full processing cost of applying PDPC uniformly across all samples.
Solution Approach 2:
The patent implements partial post-filtering by applying PDPC only to specific samples that benefit most from it, rather than to all samples. The decision to apply PDPC is based on position-dependent criteria, making the filtering action partial rather than excessive. This approach achieves sufficient prediction accuracy for boundary-critical regions while avoiding unnecessary processing overhead in regions where standard prediction is adequate.
3Measurement precision
If weighted prediction value is derived for all samples, then prediction accuracy is improved, but processor load increases
Solution Approach 1:
The patent applies position-dependent prediction combination (PDPC) selectively based on the spatial position of samples within the current block. Specifically, PDPC is applied to boundary samples (e.g., top and left edges) where prediction accuracy is most critical, while interior samples use standard intra prediction. This local differentiation improves overall prediction accuracy without incurring the full processing cost of applying PDPC uniformly across all samples.
Solution Approach 2:
The patent implements partial post-filtering by applying PDPC only to specific samples that benefit most from it, rather than to all samples. The decision to apply PDPC is based on position-dependent criteria, making the filtering action partial rather than excessive. This approach achieves sufficient prediction accuracy for boundary-critical regions while avoiding unnecessary processing overhead in regions where standard prediction is adequate.
Data Source
AI summary
An intra prediction method is described. The method can include determining a prediction value for a sample of a current block from one or more reference samples outside the current block by using an intra predication mode. The method can also include deriving a weighted prediction value, when one or more predefined conditions are not satisfied, wherein the one or more predefined conditions relate to at least one of a width and/or a height of the current block and the intra prediction mode. Furthermore, the method can include coding the current block using the weighted prediction value, when the one or more predefined conditions are not satisfied.