Affine Motion Compensation for Subblock Video Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing demand for high-definition video services has led to a significant increase in data volume, and existing video compression standards like HEVC are showing limitations in performance.
Innovation Solution
An inter prediction method using an affine model is employed, where affine seed vectors are derived using a translational motion vector of a subblock and transformed into a power series of 2, to enhance encoding/decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If HEVC compression standard is used, then compression performance is improved (about twice as high as H.264/AVC), but performance limitations are revealed as video quality requirements increase
Solution Approach 1:
The current block is divided into multiple subblocks, and affine motion compensation is applied independently to each subblock. This segmentation allows for more precise local motion representation, improving prediction accuracy while maintaining compression efficiency. The patent divides the block into four subblocks (TL, TR, BL, BR) and derives motion vectors for each, enabling better handling of complex motion patterns that HEVC's traditional methods struggle with.
Solution Approach 2:
The patent introduces dynamic affine motion models that adapt to local motion characteristics within each subblock. By using different motion compensation strategies for different subblocks based on their specific motion patterns, the system dynamically adjusts to varying motion complexities, overcoming the static limitations of traditional HEVC methods.
2Measurement precision
If affine model with subblock division is used, then prediction efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary classification of subblocks to identify which ones require affine motion compensation. By pre-processing and categorizing subblocks based on their motion characteristics, the system avoids applying computationally intensive affine calculations to all subblocks uniformly, thereby reducing overall computational complexity while maintaining prediction accuracy for regions that need it.
Solution Approach 2:
Different motion compensation methods are applied to different subblocks based on their local motion characteristics. Subblocks with complex motion patterns receive affine compensation, while simpler regions use traditional methods. This localized approach optimizes the balance between prediction accuracy and computational cost by applying complex processing only where necessary.
3Measurement precision
If affine seed vectors are derived from multiple sources, then encoding accuracy is improved, but processing time increases
Solution Approach 1:
The patent enables the decoding device to autonomously derive affine seed vectors from available neighboring block information without requiring extensive signaling from the encoder. By self-generating motion vectors from local context, the system reduces processing overhead and signaling complexity while maintaining accurate motion compensation, thereby reducing processing time without sacrificing encoding accuracy.
Data Source
AI summary
An image decoding method according to the present application includes the steps of: generating a merge candidate list in a current block; specifying one of a plurality of merge candidates included in the merge candidate list; deriving a first affine seed vector and a second affine seed vector of the current block on the basis of a first affine seed vector and a second affine seed vector of the specified merge candidate; deriving an affine vector for a subblock in the current block, using the first affine seed vector and the second affine seed vector of the current block; and performing motion compensation prediction for the subblock on the basis of the affine vector.