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Invertibility-Driven Interpolation Filter for Video Coding

delete2019-10-01
delete18
PRE
AI
N
Ning Yan
刘东 (Dong Liu) *
李厚强 (Houqiang Li)
B
Bin Li
李莉 (Li Li)
吴枫 (Feng Wu)
DOI:10.1109/TIP.2019.2913092delete
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摘要

摘要

En 中文
Motion compensation with fractional motion vector has been widely utilized in the video coding standards. The fractional samples are usually generated by fractional interpolation filters. Traditional interpolation filters are usually designed based on the signal processing theory with the assumption of band-limited signal, which cannot effectively capture the non-stationary property of video content and cannot adapt to the variety of video quality. In this paper, we reveal an intuitive property of the fractional interpolation problem, named invertibility. That is, the fractional interpolation filters should not only generate fractional samples from integer samples but also recover the integer samples from the fractional samples in an invertible manner. We prove in theory that the invertibility in the spatial domain is equivalent to the constant magnitude in the Fourier transform domain. Driven by the invertibility, we then develop a learning-based method to solve the fractional interpolation problem. Inspired by the advances of convolutional neural network (CNN), we propose to establish an end-to-end scheme using CNN to train invertibility-driven interpolation filter (InvIF). Different from the previous learning-based methods, the proposed training scheme does not need hand-crafted ground truth of fractional samples. The proposed InvIF is integrated into high efficiency video coding (HEVC), and extensive experiments are conducted to verify its effectiveness. The experimental results show that the proposed method can achieve on average 4.7% and 3.6% BD-rate reduction compared with the HEVC anchor, under low-delay-B and random-access configurations, respectively.
Keyword:
Convolutional neural network
fractional-pixel interpolation
high efficiency video coding
inter prediction
invertibility
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期刊

IEEE Transactions on Image Processing 封面图
IEEE Transactions on Image Processing
IF:
13.7
论文数:
1.0W
被引数:
8.4W

机构

U
university of science & technology of china, cas
学者数:
3.2W
论文数: 2.7W
被引数: 74
M
Microsoft Research Asia
学者数:
421
论文数: 407
被引数: 2
C
chinese academy of sciences
学者数:
56.6W
论文数: 44.9W
被引数: 704
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