A comparative study of dnn-based models for blind image quality prediction

Yang, Xiaohan, Li, Fan and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2019. A comparative study of dnn-based models for blind image quality prediction. Presented at: The 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 22-25 September 2019. 2019 IEEE International Conference on Image Processing (ICIP). IEEE, pp. 1019-1023. 10.1109/ICIP.2019.8804268

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Abstract

Recently, deep learning methods have gained substantial attention in the research community and have proven useful for blind image quality assessment (BIQA). Although previous study of deep neural networks (DNN) methods is presented, some novelty methods, which are recently proposed, are not summarized. In this paper, we provide a comparative study on the application of DNN methods for BIQA. First, we systematically analyze the existing DNN-based quality assessment methods. Then, we compare the predictive performance of various methods in synthetic and authentic databases, providing important information that can help understand the underlying properties between different methods. Finally, we describe some emerging challenges in designing and training DNN-based BIQA, along with few directions that are worth further investigations in the future.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	IEEE
ISBN:	978-1-5386-6250-2
ISSN:	1522-4880
Date of First Compliant Deposit:	3 May 2019
Date of Acceptance:	30 April 2019
Last Modified:	17 Jun 2026 15:15
URI:	https://orca.cardiff.ac.uk/id/eprint/122155

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