Angular Intra Prediction Weighting for Uneven Reference Samples
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Solution Overview
Problem
Existing video coding systems do not adequately account for the uneven distribution of available reference samples, leading to suboptimal prediction accuracy in angular mode predictions.
Innovation Solution
Bias the prediction process by deriving a final prediction value using a weighted sum of top and left reference samples, improving prediction accuracy in angular modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional angular prediction modes are used, then the coding process is simple, but prediction accuracy is suboptimal due to uneven distribution of reference samples
Solution Approach 1:
The patent applies local quality by differentiating the treatment of reference samples based on their availability and reliability. Top and left reference samples are given higher weights in the weighted sum calculation, while other reference samples are treated differently or excluded. This localized differentiation of sample importance resolves the contradiction by improving prediction accuracy through selective weighting without requiring a complete redesign of the prediction framework.
Solution Approach 2:
The patent changes the parameters of the prediction process by introducing a weighted sum mechanism with configurable weights for different reference samples. The weights can be adjusted based on the availability and quality of reference samples, allowing the system to adapt to different scenarios. This parameter change enables improved prediction accuracy while maintaining flexibility in the prediction process.
2Measurement precision
If more reference samples are utilized in the prediction process, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies partial action by selectively using only the most reliable and available reference samples (top and left samples) with higher weights, rather than uniformly processing all available reference samples. This selective approach improves prediction accuracy by focusing computational resources on the most valuable samples, thereby reducing overall computational complexity while maintaining or improving prediction quality.
3Measurement precision
If angular prediction modes account for uneven reference sample distribution, then prediction accuracy improves, but the encoding/decoding process becomes more complex
Solution Approach 1:
The patent applies asymmetry by treating top and left reference samples differently from other reference samples through the weighted sum mechanism. This asymmetric treatment reflects the actual uneven distribution and reliability of reference samples in angular prediction scenarios. By introducing this asymmetric weighting, the patent improves prediction accuracy while maintaining a relatively simple encoding/decoding process that builds upon existing angular prediction frameworks.
Data Source
AI summary
The various implementations described herein include methods and systems for encoding and decoding video. In one aspect, a method of video decoding includes receiving video data that includes a first block from a video bitstream, where the first block is predicted in an angular prediction mode. The method also includes identifying a set of reference samples for a portion of the first block using a prediction angle for the angular prediction mode and deriving a first angular prediction value for the portion using at least a first subset of the set of reference samples. The method further includes deriving a second angular prediction value for the portion using a weighted sum of at least a second subset of the set of reference samples and the first angular prediction value and decoding the portion using the second angular prediction value.