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Three Gaps for Quantisation in Learned Image Compression

Pan, Shi; Finlay, Chris; Besenbruch, Chri; Knottenbelt, William; (2021) Three Gaps for Quantisation in Learned Image Compression. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). (pp. pp. 720-726). IEEE: Nashville, TN, USA. Green open access

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Abstract

Learned lossy image compression has demonstrated impressive progress via end-to-end neural network training. However, this end-to-end training belies the fact that lossy compression is inherently not differentiable, due to the necessity of quantisation. To overcome this difficulty in training, researchers have used various approximations to the quantisation step. However, little work has studied the mechanism of quantisation approximation itself. We address this issue, identifying three gaps arising in the quantisation approximation problem. These gaps are visualised, and show the effect of applying different quantisation approximation methods. Following this analysis, we propose a Soft-STE quantisation approximation method, which closes these gaps and demonstrates better performance than other quantisation approaches on the Kodak dataset.

Type: Proceedings paper
Title: Three Gaps for Quantisation in Learned Image Compression
Event: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Dates: 19 Jun 2021 - 25 Jun 2021
ISBN-13: 978-1-6654-4899-4
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/cvprw53098.2021.00081
Publisher version: http://dx.doi.org/10.1109/cvprw53098.2021.00081
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Training; Visualization; Computer vision; Quantization (signal); Image coding; Conferences; Neural networks
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment
URI: https://discovery.ucl.ac.uk/id/eprint/10198022
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