Video Encoding Motion Refinement Candidate Derivation
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Solution Overview
Problem
Conventional image encoding/decoding methods face limitations in improving coding efficiency due to their reliance on motion compensation based solely on spatial/temporal neighboring blocks, which is insufficient for high-resolution and high-quality images, leading to increased data transmission and storage costs.
Innovation Solution
A method that refines motion information by deriving candidates from spatial and temporal neighboring blocks, predefined motion information, and the most frequent motion information in a reference picture, and applies bilateral template matching to refine the initial motion vector, thereby generating a prediction block with improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If motion compensation is performed based solely on spatial/temporal neighboring blocks, then device complexity is reduced, but manufacturing precision (coding efficiency) deteriorates
Solution Approach 1:
The motion information candidates are segmented into multiple sources: spatial neighboring blocks, temporal neighboring blocks, predefined motion information, and most frequent motion information from reference pictures. This segmentation allows the system to select from diverse candidate types without requiring complex processing of all possibilities simultaneously.
Solution Approach 2:
Motion information candidates are derived and prepared in advance from multiple sources before the actual motion compensation process. This preliminary derivation of candidates from spatial blocks, temporal blocks, predefined information, and frequent patterns reduces the complexity during the main encoding/decoding operation while maintaining high precision.
2Productivity
If conventional motion compensation is used, then device complexity is low, but productivity (compression efficiency) deteriorates
Solution Approach 1:
The patent merges motion information from multiple sources including spatial neighboring blocks, temporal neighboring blocks, predefined motion information, and most frequent motion information from reference pictures into a unified candidate set. This combination improves compression efficiency by selecting the best candidate without proportionally increasing device complexity.
Solution Approach 2:
The system changes the parameter of motion information selection by considering multiple sources (spatial, temporal, predefined, frequent patterns) rather than relying on a single source. This parameter change enables better compression efficiency while managing complexity through structured candidate derivation.
3Measurement precision
If motion information from multiple sources is derived, then measurement precision (motion vector accuracy) is improved, but device complexity increases
Solution Approach 1:
Different types of motion information candidates are derived from different local sources: spatial neighboring blocks provide local spatial context, temporal neighboring blocks provide local temporal context, predefined information provides baseline motion, and frequent patterns provide statistical local quality. This local quality approach improves motion vector accuracy without requiring globally complex processing.
Data Source
AI summary
Disclosed is an image encoding method. The method includes deriving a motion refinement candidate from among motion information of spatial neighboring blocks, motion information of a temporal neighboring blocks, predefined motion information, and motion information that most frequently occurs in a reference picture, performing a motion information refinement on the derived motion refinement candidate, and generating a prediction block of a current block by using the motion refinement candidate having undergone the motion information refinement.