Video Signal Decoding With Prioritized Merge Lists for Motion Compensation
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Solution Overview
Problem
Existing video encoding/decoding methods face inefficiencies in inter-prediction and motion compensation, particularly when handling high-resolution and stereographic image content, leading to increased data transmission and storage costs.
Innovation Solution
The method and apparatus enhance inter-prediction efficiency by deriving and rearranging merge candidates from neighboring blocks, prioritizing those with bi-directional information, and using multiple merge candidate lists for motion compensation during encoding/decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image compression techniques are used for high-resolution images, then data transmission and storage costs increase, but image quality and resolution requirements cannot be met
Solution Approach 1:
The patent changes the parameters of motion compensation by introducing multiple merge candidate lists (first merge candidate list and second merge candidate list) with different priorities. The decoder uses a priority flag to select between lists, allowing adaptive parameter selection based on picture type (I/P/B pictures) and block characteristics, thereby improving compression efficiency while maintaining high image quality
Solution Approach 2:
The patent implements dynamic selection of merge candidate lists based on picture type and block characteristics. The decoder dynamically chooses between the first and second merge candidate lists using a priority flag, adapting the motion compensation strategy to different coding scenarios (intra prediction vs. inter prediction, different picture types), which optimizes the balance between image quality and compression ratio
2Productivity
If multiple merge candidate lists are used for motion compensation, then inter-prediction efficiency improves, but decoding complexity increases
Solution Approach 1:
The patent segments the merge candidates into two separate lists: the first merge candidate list containing spatial and temporal candidates, and the second merge candidate list containing additional candidates with different priority characteristics. This segmentation allows the decoder to process candidates in organized groups based on their suitability for different prediction modes, improving efficiency while managing complexity through structured organization
Solution Approach 2:
The patent performs preliminary organization of merge candidates into prioritized lists during the encoding phase. The encoder pre-calculates and stores candidates in the first and second lists based on their expected utility for different picture types and block characteristics, so that the decoder can directly use these pre-organized lists without performing complex real-time calculations, thereby reducing decoding complexity
3Measurement precision
If merge candidates are rearranged based on bi-directional information priority, then motion compensation accuracy improves, but encoding complexity increases
Solution Approach 1:
The patent applies different quality priorities to different merge candidates based on their local characteristics. Bi-directional prediction candidates are assigned higher priority in the first merge candidate list, while uni-directional candidates are placed in the second list. This local quality differentiation allows the decoder to select the most appropriate candidates for each specific coding scenario, improving motion compensation accuracy without uniformly increasing complexity across all candidates
Solution Approach 2:
The patent changes the arrangement parameters of merge candidates by sorting them according to their prediction direction characteristics (bi-directional vs. uni-directional). This parameter-based reorganization allows the encoder to systematically manage candidate priorities using clear classification rules, improving motion compensation accuracy while controlling encoding complexity through structured parameter management
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.