Image manipulation quality assessment

Wu, Xinbo, Lou, Jianxun, Wu, Yingying, Liu, Wanan, Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884, Colombo, Gualtiero, Allen, Stuart ORCID: https://orcid.org/0000-0003-1776-7489, Whitaker, Roger ORCID: https://orcid.org/0000-0002-8473-1913 and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2024. Image manipulation quality assessment. IEEE Transactions on Circuits and Systems for Video Technology 10.1109/tcsvt.2024.3504854

Preview

PDF - Accepted Post-Print Version Download (25MB) | Preview

Abstract

Image quality assessment (IQA) and its computational models play a vital role in modern computer vision applications. Research has traditionally focused on signal distortions arising during image compression and transmission, and their impact on perceived image quality. However, little attention is paid to image manipulation that alters an image using various filters. With the prevalence of image manipulation in real-life scenarios, it is critical to understand how humans perceive filter-altered images and to develop reliable IQA models capable of automatically assessing the quality of filtered images. In this paper, we build a new IQA database for filter-altered images, comprised of 360 images manipulated by various filters. To ensure the subjective IQA faithfully reflects human visual perception, we conduct a fully-controlled psychovisual experiment. Building upon the ground truth, we propose an innovative deep learning-based no-reference IQA (NR-IQA) model named IMQA that can accurately predict the perceived quality of filter-altered images. This model involves constructing an image filtering-aware module to learn discriminatory features for filter-altered images; and fuses these features with the representations generated by an image quality-aware module. Experimental results demonstrate the superior performance of the proposed IMQA model.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html, Start Date: 2024-01-01
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1051-8215
Date of First Compliant Deposit:	16 December 2024
Date of Acceptance:	19 November 2024
Last Modified:	16 Dec 2024 15:30
URI:	https://orca.cardiff.ac.uk/id/eprint/174482

Actions (repository staff only)

Edit Item