Block Vector Predictor Diversity for Accurate Low-Overhead Coding
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Solution Overview
Problem
Existing video encoding and decoding technologies face challenges in efficiently reducing signaling overhead and improving prediction accuracy for block vectors, leading to increased data size and resource requirements for video storage and transmission.
Innovation Solution
The use of block vector predictors (BVPs) within a decoded region of a frame to select a group of BVPs with the lowest cost, diversifying the list of BVPs used to indicate block vectors, thereby reducing signaling overhead and enhancing prediction efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple block vector predictors are generated and evaluated, then prediction accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the block vector prediction process into distinct stages: generating multiple candidate BVPs from different reference blocks, evaluating each candidate using a cost function, and selecting the optimal candidate. This segmentation allows systematic exploration of multiple predictions while managing complexity through structured evaluation.
Solution Approach 2:
The patent changes parameters such as the cost function metrics (e.g., sum of absolute differences, sum of squared differences) and evaluation criteria to optimize the selection process. By adjusting these parameters, the system can balance between exploring more candidates for higher accuracy and limiting the search space to reduce computational complexity.
2Quantity of substance
If block vector predictors are selected from multiple candidates, then signaling overhead is reduced, but prediction accuracy may deteriorate
Solution Approach 1:
The patent implements feedback through cost function evaluation, where each candidate BVP is assessed based on its prediction quality. The feedback from these evaluations guides the selection process, ensuring that the final chosen BVP achieves an optimal balance between compact representation (low signaling overhead) and accurate prediction.
Solution Approach 2:
The patent adjusts parameters such as the cost function weights and selection thresholds to optimize the trade-off between signaling overhead and prediction accuracy. By tuning these parameters, the system can prioritize either compression efficiency or prediction quality based on specific application requirements.
3Measurement precision
If a diverse list of block vector predictors is used, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-generating and pre-evaluating multiple candidate BVPs before the actual prediction process. This allows the system to have a ready list of diverse predictors that can be quickly selected from, reducing the time required during actual video decoding while maintaining diverse prediction options for accuracy.
Solution Approach 2:
The patent applies partial action by evaluating only a subset of possible BVP candidates rather than exhaustively checking all possibilities. This selective evaluation approach provides sufficient diverse predictions for accurate reconstruction while significantly reducing the processing time compared to complete exhaustive search.
Data Source
AI summary
Encoding and/or decoding a block of a video frame may be based on a previously decoded reference block in the same frame or in a different frame. The reference block may be indicated by a block vector (BV). The BV may be encoded as difference between a block vector predictor (BVP) and the BV. The BVP may be selected based on a distance between the BVP and another BVP which may improve diversity of selected BVPs and improve prediction accuracy of the BVP.