Affine Motion Prediction for Efficient Video Signal Decoding
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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 efficiency.
Innovation Solution
An inter prediction method using an affine model is employed, where affine seed vectors are derived using translational motion vectors of subblocks and transformed into a power series of 2, enhancing encoding/decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If HEVC compression standard is used, then compression performance is improved (about twice as high as H.264/AVC), but device complexity and processing 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 the complex affine transformation to be broken down into smaller, more manageable units, reducing overall processing complexity while maintaining high compression performance.
Solution Approach 2:
Different motion models are applied to different regions: affine motion compensation is used for subblocks where it provides better prediction accuracy, while other methods can be used elsewhere. This local adaptation optimizes the balance between compression efficiency and processing complexity by applying the more computationally intensive method only where necessary.
2Measurement precision
If affine motion compensation is applied to all subblocks, then prediction accuracy is improved, but processing time and computational load increase
Solution Approach 1:
Affine motion compensation is applied selectively to certain subblocks rather than uniformly to all subblocks. The method determines which subblocks benefit most from affine transformation and applies the method only to those, achieving good prediction accuracy while avoiding the excessive processing time that would result from applying it universally.
Solution Approach 2:
The method transforms the distance between neighboring block and current block to a power series of 2, which simplifies the computational parameters and reduces processing time while maintaining prediction accuracy.
3Productivity
If distance transformation to power series of 2 is performed, then computational efficiency is improved, but calculation steps increase
Solution Approach 1:
The method replaces complex distance calculations with a transformation to power series of 2, which can be computed more efficiently using bit-shift operations instead of traditional multiplication or division. This substitution of computational mechanics significantly improves encoding efficiency while the standardized transformation process keeps the added complexity manageable.
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.