Multi-Hypothesis Prediction for Accurate Image Block Encoding
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Solution Overview
Problem
Existing image compression technologies face challenges in achieving high accuracy and efficiency, particularly in inter prediction methods for high-resolution and high-quality images, such as HD and UHD images, due to inefficiencies in handling multi-hypothesis prediction modes.
Innovation Solution
The method involves reordering and refining candidate lists for motion, weight, and interpolation filters in multi-hypothesis prediction modes, using bidirectional prediction to generate and combine prediction blocks, and calculating costs based on differences to improve accuracy and compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-hypothesis prediction mode is used to improve prediction accuracy, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The candidate list is divided into multiple segments or groups, and reordering is applied to specific segments rather than the entire list. This segmentation allows the system to manage complexity by focusing refinement efforts on specific portions of the candidate list while maintaining overall prediction accuracy.
Solution Approach 2:
The candidate list is reordered and refined in advance before the actual prediction process. By performing reordering operations preliminarily, the system reduces the computational burden during real-time prediction while maintaining high accuracy through pre-optimized candidate arrangements.
2Measurement precision
If comprehensive candidate list reordering is performed to improve prediction accuracy, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
Instead of reordering the entire candidate list, the invention applies reordering only to specific portions or segments of the list that have the greatest impact on prediction accuracy. This partial action approach achieves sufficient prediction improvement while significantly reducing the processing time required for reordering operations.
3Measurement precision
If multiple candidate lists are maintained for multi-hypothesis prediction, then prediction accuracy is improved, but signaling bits increase
Solution Approach 1:
Multiple candidate lists are merged or combined into a unified structure with reordering applied. This merging approach allows the system to maintain the benefits of multiple hypotheses for improved prediction accuracy while reducing the signaling overhead by consolidating the representation of multiple candidate lists into a more compact form.
Data Source
AI summary
An image decoding/encoding method and device according to the present disclosure perform bidirectional prediction so as to generate a basic prediction block of the current block, configure a candidate list for multi-hypothesis prediction, derive motion information about the current block on the basis of the candidate list, generate an additional prediction block of the current block on the basis of the motion information, and obtain the weighted sum of the basic prediction block and the additional prediction block so as to generate a final prediction block of the current block.