Affine Motion Compensation in Regression GPM Video Encoding
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Solution Overview
Problem
Existing video coding standards face challenges in achieving high compression efficiency, particularly with the development of advanced standards like VVC, where affine motion compensation methods are needed to enhance coding performance.
Innovation Solution
The method involves constructing merge candidate lists that include affine motion vectors for code units, using regression geometric partition mode to improve encoding and decoding processes, allowing for more efficient video compression and decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional geometric partition mode is used for video encoding, then the encoding process is simple, but the coding efficiency is insufficient
Solution Approach 1:
The coding unit is divided into two partitions using geometric partition mode, with each partition independently assigned motion vectors from the merge candidate list. This segmentation allows different regions to be encoded with appropriate motion compensation, improving coding efficiency while maintaining manageable complexity through systematic division
Solution Approach 2:
The patent introduces affine motion vectors that can represent complex motion patterns including rotation, scaling, and shearing transformations. The merge candidate list dynamically adapts to different motion types by providing diverse motion vector options, enabling the encoding system to handle various motion scenarios efficiently without excessive complexity
2Productivity
If affine motion compensation is implemented, then the compression performance improves, but the computational complexity increases
Solution Approach 1:
The patent pre-construction a merge candidate list containing multiple motion vector candidates including affine motion vectors before the actual encoding process. This preliminary preparation allows the encoder to efficiently select appropriate motion vectors during encoding without performing complex calculations in real-time, thus improving compression efficiency while controlling computational complexity
Solution Approach 2:
The patent employs affine transformation parameters (rotation angle, scaling factor, shearing factor) to represent motion patterns. By changing these parameters to match different motion scenarios, the system achieves better compression performance for complex motions while maintaining a standardized computational framework that prevents excessive complexity
Data Source
AI summary
The present disclosure provides a method of encoding a video sequence. The method includes: receiving a video sequence; encoding the video sequence by: constructing a first merge candidate list of pairs of candidates from a regular geometric partition mode (GPM) candidate list for a code unit (CU); constructing one or more additional merge candidate lists of pairs of candidates, wherein each pair of candidates comprises a first motion vector for a first partition and a second motion vector for a second partition, at least one of the pairs of candidates in the one or more additional merge candidate lists comprises an affine motion vector; and encoding the CU using a regression GPM using the first merge candidate list or one of the one or more additional merge candidate lists, wherein the CU comprises a first partition and a second partition.