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Jianle Chen

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Proceedings Article | 4 October 2023 Presentation + Paper

Sub-block-based motion vector refinement of AVM

Mohammed Golam Sarwer, Jianle Chen, Debargha Mukherjee

Proceedings Volume 12674, 126740O (2023) https://doi.org/10.1117/12.2681766

KEYWORDS: Video, Optical flow, Video compression, Tunable filters, Interpolation, Video coding, Standards development, Displays, Quantization

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This paper improves the inter prediction method of AOMedia Video Model (AVM) by introducing a sub-block based motion vector (MV) refinement method. In the proposed method, if a block is coded as a compound mode with bidirectional reference frame, MVs of the blocks are refined before producing the final prediction. In the proposed method, at first a predicted block is divided into number of non-overlapping sub-blocks. Then, for each sub-block, offset motion vectors are searched by minimizing the sum of absolute differences (SAD) between two predicted signals P0 and P1 (Assume, P0 and P1 are the predicted blocks from reference 0 and 1, respectively ). To simplify the searching process, instead of full search, a two-step integer search is proposed. At the first step, 9 offset MVs are searched ( initial MV and 8 neighbors ). The offset which produces minimum SAD in first step is selected as the center for the second step. In the second step, additional searching is conducted around the best offset found from the first step. A block level flag is conditionally signaled in the bitstream to indicate if the proposed method is used or not. The proposed method is implemented in AVM reference software research-v4.0.0. The simulation results show the proposed method can achieve -0.47% (YUV) and 0.90% (VMAF) BDRate gain as compared to AVM research-v4.0.0.

Proceedings Article | 4 October 2023 Presentation + Paper

Warped motion prediction beyond AV1

Mohammed Golam Sarwer, Rachel Barker, Jianle Chen, Debargha Mukherjee

Proceedings Volume 12674, 126740M (2023) https://doi.org/10.1117/12.2680783

KEYWORDS: Motion models, Video coding, Video, Cameras, Affine motion model, Video compression, Motion estimation

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In AV1 local warped motion mode, the warped motion parameters of the current block are derived by fitting a model to nearby motion vectors using least-squares. This paper extend this mechanism to add two new warped motion modes ( called WARP_EXTEND and WARP_DELTA), which provide different ways to compute a local warped motion model. In the proposed WARP_EXTEND mode, a warped motion model is constructed by smoothly extending the motion of neighboring blocks into the current block, with modification based on the signaled motion vector. In the proposed WARP_DELTA mode, at first, the parameters of the warped motion model of the current coding block is predicted from the previously encoded/decoded blocks. Then the difference between the current block model parameters and the predicted model parameters are signaled. For each block, a warped motion reference list ( WRL) is maintained to store all of the predicted model parameters. The WRL is generated from the corner motion vectors (MVs) and from the warped motion model of the spatial neighboring blocks. A model parameter bank is also maintained to store the model parameters of the previously decoded blocks of the current frame. If there are not enough candidates from the spatial neighborhood to fill the WRL, the warped motion models from the model parameter bank are inserted to the WRL. Besides WARP_EXTEND and WARP_DELTA modes, this paper also proposes a separate single reference prediction mode where motion vector (MV) of the current block is predicted from the WRL. The simulation results show -1.18% (YUV) and -1.53%(YUV) compression gain as compared to the existing AV1 warped motion in random access and low delay configurations, respectively.

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