TranSalNet+: Distortion-aware saliency prediction

Lou, Jianxun, Wu, Xinbo, Corcoran, Padraig ORCID: https://orcid.org/0000-0001-9731-3385, Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2024. TranSalNet+: Distortion-aware saliency prediction. Neurocomputing 600 , 128155. 10.1016/j.neucom.2024.128155

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Abstract

Predicting the saliency of images affected by distortion is a challenging but emerging research problem. Given a distorted image, we wish to accurately predict saliency as perceived by humans. A recent distortion-aware saliency benchmark – the CUDAS database – reveals the inadequacy of existing saliency models in handling distorted images. In this paper, we devise a deep learning Distortion-Aware Saliency Module (DASM) that enables capturing saliency features related to image distortions, and integrates this module into a saliency prediction architecture. To achieve the high expressive capability of DASM using supervised learning, we create a dedicated dataset that draws upon a large-scale saliency dataset and machine-generated image quality assessments. Experimental results demonstrate the superior performance of the proposed model in predicting the saliency of distorted images.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Start Date: 2024-07-03
Publisher:	Elsevier
ISSN:	0925-2312
Date of First Compliant Deposit:	9 July 2024
Date of Acceptance:	29 June 2024
Last Modified:	01 Aug 2024 14:28
URI:	https://orca.cardiff.ac.uk/id/eprint/170437

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