Feng, T. and Abhayaratne, C. orcid.org/0000-0002-2799-7395 (2018) Visual saliency guided high dynamic range image compression. In: 2018 26th European Signal Processing Conference (EUSIPCO). 2018 26th European Signal Processing Conference (EUSIPCO), 03-07 Sep 2018, Rome, Italy. IEEE , pp. 166-170. ISBN 978-9-0827-9701-5
Abstract
Recent years have seen the emergence of the visual saliency-based image and video compression for low dynamic range (LDR) visual content. The high dynamic range (HDR) imaging is yet to follow such an approach for compression as the state-of-the-art visual saliency detection models are mainly concerned with LDR content. Although a few HDR saliency detection models have been proposed in the recent years, they lack the comprehensive validation. Current HDR image compression schemes do not differentiate salient and non-salient regions, which has been proved redundant in terms of the Human Visual System. In this paper, we propose a novel visual saliency guided layered compression scheme for HDR images. The proposed saliency detection model is robust and highly correlates with the ground truth saliency maps obtained from eye tracker. The results show a reduction of bit-rates up to 50% while retaining the same high visual quality in terms of HDR-Visual Difference Predictor (HDR-VDP) and the visual saliency-induced index for perceptual image quality assessment (VSI) metrics in the salient regions.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © EURASIP 2018. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Mar 2019 14:42 |
Last Modified: | 03 Dec 2019 01:39 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/EUSIPCO.2018.8553456 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:142086 |