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A survey of DNN methods for blind image quality assessment

Yang, Xiaohan, Li, Fan and Liu, Hantao ORCID: 2019. A survey of DNN methods for blind image quality assessment. IEEE Access 7 , pp. 123788-123806. 10.1109/ACCESS.2019.2938900

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Blind image quality assessment (BIQA) methods aim to predict quality of images as perceived by humans without access to a reference image. Recently, deep learning methods have gained substantial attention in the research community and have proven useful for BIQA. Although previous study of deep neural networks (DNN) methods is presented, some novelty DNN methods, which are recently proposed, are not summarized for BIQA. In this paper, we provide a survey covering various DNN methods for BIQA. First, we systematically analyze the existing DNN-based quality assessment methods according to the role of DNN. Then, we compare the prediction performance of various DNN methods on the synthetic databases (LIVE, TID2013, CSIQ, LIVE multiply distorted) and authentic databases (LIVE challenge), providing important information that can help understand the underlying properties between different DNN methods for BIQA. 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: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 2169-3536
Date of First Compliant Deposit: 5 September 2019
Last Modified: 11 Nov 2023 16:40

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