Adaptive Control Point Selection for Affine Video Coding
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Solution Overview
Problem
Current video coding technologies face challenges in efficiently compressing higher resolution videos due to increased bandwidth demands, with existing standards like HEVC struggling to optimize coding efficiency and motion vector prediction methods.
Innovation Solution
The implementation of adaptive control point selection for affine coding in video processing, which includes selecting non-corner control points for motion estimation and using control point motion vectors from neighboring blocks to enhance motion vector prediction, thereby improving compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional motion vector prediction methods are used, then coding complexity is reduced, but coding efficiency deteriorates for higher resolution videos
Solution Approach 1:
The current block is divided into multiple sub-blocks, and each sub-block independently selects control points from its own candidate list. This segmentation allows parallel processing and reduces the overall computational complexity while improving coding efficiency through localized motion modeling.
Solution Approach 2:
Different control point candidate lists are constructed for different sub-blocks based on their specific positions and characteristics. Each sub-block uses control points selected from candidates that are locally relevant, improving the accuracy of motion representation without uniformly increasing complexity across the entire block.
2Measurement precision
If more control points are selected for affine coding, then motion representation accuracy is improved, but computational complexity increases
Solution Approach 1:
The control point selection is made dynamically and adaptively for each sub-block based on available candidate lists and local motion characteristics. This dynamic approach allows the system to use more control points where needed for high accuracy while using fewer control points in regions with simpler motion, balancing accuracy and complexity.
Solution Approach 2:
Instead of uniformly applying full affine modeling with multiple control points to the entire block, the method applies control point selection selectively to individual sub-blocks. This partial application of the complex modeling technique reduces overall computational complexity while maintaining accuracy where it matters most.
3Productivity
If sub-block based merge candidate lists are used, then coding efficiency is improved, but entropy coding complexity increases
Solution Approach 1:
The entropy coding approach is changed based on the characteristics of the merge index. Less significant bits or positions are coded using bypass coding to reduce complexity, while more significant information uses context-based adaptive binary arithmetic coding. This parameter-based differentiation resolves the contradiction by adapting the coding complexity to the information importance.
Data Source
AI summary
Devices, systems and methods for digital video coding, which include adaptive control point selection for affine coding, are described. An exemplary method for video processing includes selecting, for a conversion between a current block of a video and a bitstream representation of the video, a plurality of control points for the current block, the plurality of control points comprising at least one non-corner point of the current block, and each of the plurality of control points being representative of an affine motion of the current block; and performing, based on the plurality of control points, the conversion between the current block and the bitstream representation.