Journal
2022 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/VCIP56404.2022.10008881
Keywords
VVC; ECM; affine; history-based motion prediction; affine parameter inheritance
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In VVC, affine motion compensation (AMC) and history-based motion vector prediction (HMVP) are powerful coding tools. However, HMVP does not consider non-translational motion. This paper presents a method to efficiently represent control point motion vectors (CPMV) using history information, which has been adopted into VVC.
In VVC, affine motion compensation (AMC) is a powerful coding tool to address non-translational motion, while history-based motion vector prediction (HMVP) is an efficient approach to compress motion vectors. However, HMVP was designed for translational motion vectors, without considering control point motion vectors (CPMV) for AMC. This paper presents a method of history-parameter-based affine model inheritance (HAMI), to utilize history information to represent CPMV more efficiently. With HAMI, affine parameters of previously affine-coded block are stored in a first history-parameter table (HPT). New affine- merge, affine motion vector prediction candidates and regular-merge candidates can be constructed with affine parameters fetched from the first HPT and base MVs fetched from neighbouring blocks in a base-parameter-decoupled way. New affine merge candidates can also be generated in a base-parameter-coupled way from a second HPT, which stores base MV information together with corresponding affine parameters. Besides, pair-wised affine merge candidates are generated by two existing affine merge candidates. Experimental results show that HAMI provides an average BD-rate saving about 0.34% with a negligible change on the running time, compared with ECM-3.1 in random access configurations. HAMI has been adopted into ECM.
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