Video Signal Motion Compensation With Prioritized Merge Candidate Lists
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Solution Overview
Problem
Existing video signal processing technologies face challenges in efficiently performing inter-prediction and motion compensation, particularly when dealing with high-resolution and stereographic image content.
Innovation Solution
The method involves deriving merge candidates from neighboring blocks, generating a merge candidate list, rearranging the candidates based on a rearrangement priority, and using these rearranged candidates to derive motion information for the current block.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image compression techniques are used for high-resolution images, then transmission and storage costs increase, but image quality and resolution requirements cannot be met
Solution Approach 1:
The patent segments the merge candidate list into multiple sublists (first merge candidate list and second merge candidate list) based on different priority criteria. This segmentation allows the encoder to selectively process and transmit only the most relevant candidates, reducing the amount of data that needs to be transmitted and stored while maintaining high image quality through efficient motion compensation.
2Productivity
If multiple merge candidate lists are used for motion compensation, then inter-prediction efficiency improves, but processing complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-organizing merge candidates into multiple lists with different priority levels before the actual motion compensation process. The first merge candidate list is constructed based on one priority criterion (e.g., spatial proximity) while the second list is constructed based on another criterion (e.g., temporal similarity). This preliminary organization reduces processing complexity during actual encoding/decoding by avoiding the need to search through all candidates simultaneously, thus improving inter-prediction efficiency without excessive complexity increase.
3Measurement precision
If merge candidates are rearranged based on priority, then motion information derivation accuracy improves, but encoding complexity increases
Solution Approach 1:
The patent applies local quality by treating different merge candidates with different priority levels based on their specific characteristics. Rather than using a uniform processing approach for all candidates, the patent creates specialized lists (first and second merge candidate lists) where candidates are arranged according to their local importance and relevance to the current block. This allows the encoder to focus computational resources on the most promising candidates, improving motion information accuracy while controlling encoding complexity through selective processing.
Data Source
AI summary
An image decoding method according to the present invention can comprise the steps of: deriving merge candidates from neighboring blocks adjacent to a current block; generating a merge candidate list including the merge candidates, wherein the arrangement order of the merge candidates in the merge candidate list is determined on the basis of initial priorities; re-arranging the merge candidates included in the merge candidate list; decoding information for specifying at least one of the merge candidates included in the merge candidate list; and deriving motion information of the current block from a merge candidate corresponding to the information.