Progress in Blind Image Quality Assessment: A Brief Review

Pei Yang, Jordan Sturtz and Letu Qingge ()
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Pei Yang: Department of Computer Technology and Application, Qinghai University, Xining 810016, China
Jordan Sturtz: Department of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USA
Letu Qingge: Department of Computer Science, North Carolina A&T State University, Greensboro, NC 27411, USA

Mathematics, 2023, vol. 11, issue 12, 1-26

Abstract: As a fundamental research problem, blind image quality assessment (BIQA) has attracted increasing interest in recent years. Although great progress has been made, BIQA still remains a challenge. To better understand the research progress and challenges in this field, we review BIQA methods in this paper. First, we introduce the BIQA problem definition and related methods. Second, we provide a detailed review of the existing BIQA methods in terms of representative hand-crafted features, learning-based features and quality regressors for two-stage methods, as well as one-stage DNN models with various architectures. Moreover, we also present and analyze the performance of competing BIQA methods on six public IQA datasets. Finally, we conclude our paper with possible future research directions based on a performance analysis of the BIQA methods. This review will provide valuable references for researchers interested in the BIQA problem.

Keywords: blind image quality assessment; no-reference image quality assessment; natural scene statistics; mean opinion score; one-stage BIQA (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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