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Perceptually-inspired super-resolution of compressed videos
Author(s): Di Ma; Mariana Fernandez Afonso; Fan Zhang; David R. Bull
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Paper Abstract

Spatial resolution adaptation is a technique which has often been employed in video compression to enhance coding efficiency. This approach encodes a lower resolution version of the input video and reconstructs the original resolution during decoding. Instead of using conventional up-sampling filters, recent work has employed advanced super-resolution methods based on convolutional neural networks (CNNs) to further improve reconstruction quality. These approaches are usually trained to minimise pixel-based losses such as Mean-Squared Error (MSE), despite the fact that this type of loss metric does not correlate well with subjective opinions. In this paper, a perceptually-inspired super-resolution approach (M-SRGAN) is proposed for spatial up-sampling of compressed video using a modified CNN model, which has been trained using a generative adversarial network (GAN) on compressed content with perceptual loss functions. The proposed method was integrated with HEVC HM 16.20, and has been evaluated on the JVET Common Test Conditions (UHD test sequences) using the Random Access configuration. The results show evident perceptual quality improvement over the original HM 16.20, with an average bitrate saving of 35.6% (Bjøntegaard Delta measurement) based on a perceptual quality metric, VMAF.

Paper Details

Date Published: 6 September 2019
PDF: 9 pages
Proc. SPIE 11137, Applications of Digital Image Processing XLII, 1113717 (6 September 2019); doi: 10.1117/12.2530688
Show Author Affiliations
Di Ma, Univ. of Bristol (United Kingdom)
Mariana Fernandez Afonso, Univ. of Bristol (United Kingdom)
Fan Zhang, Univ. of Bristol (United Kingdom)
David R. Bull, Univ. of Bristol (United Kingdom)


Published in SPIE Proceedings Vol. 11137:
Applications of Digital Image Processing XLII
Andrew G. Tescher; Touradj Ebrahimi, Editor(s)

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