P-frame coding proposal by NCTU: Parametric video prediction through backprop-based motion estimation

Yung Han Ho, Chih Chun Chan, David Alexandre, Wen-Hsiao Peng, Chih Peng Chang

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

This paper presents a parametric video prediction scheme with backprop-based motion estimation, in response to the CLIC challenge on P-frame compression. Recognizing that most learning-based video codecs rely on optical flow-based temporal prediction and suffer from having to signal a large amount of motion information, we propose to perform parametric overlapped block motion compensation on a sparse motion field. In forming this sparse motion field, we conduct the steepest descent algorithm on a loss function for identifying critical pixels, of which the motion vectors are communicated to the decoder. Moreover, we introduce a critical pixel dropout mechanism to strike a good balance between motion overhead and prediction quality. Compression results with HEVC-based residual coding on CLIC validation sequences show that our parametric video prediction achieves higher PSNR and MS-SSIM than optical flow-based warping. Moreover, our critical pixel dropout mechanism is found beneficial in terms of rate-distortion performance. Our scheme offers the potential for working with learned residual coding.

原文English
主出版物標題Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
發行者IEEE Computer Society
頁面598-601
頁數4
ISBN(電子)9781728193601
DOIs
出版狀態Published - 14 6月 2020
事件2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 - Virtual, Online, 美國
持續時間: 14 6月 202019 6月 2020

出版系列

名字IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
2020-June
ISSN(列印)2160-7508
ISSN(電子)2160-7516

Conference

Conference2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
國家/地區美國
城市Virtual, Online
期間14/06/2019/06/20

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