Fine-Grained Image Quality Assessment: A Revisit and Further Thinking

Image quality assessment (IQA) plays a central role in many image processing algorithms and systems. Although many popular IQA models achieves high performance on existing released databases, they are still not well accepted in practical applications due to the not-always satisfactory accuracy on re...

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Veröffentlicht in:IEEE transactions on circuits and systems for video technology Jg. 32; H. 5; S. 2746 - 2759
Hauptverfasser: Zhang, Xinfeng, Lin, Weisi, Huang, Qingming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Distortion
fine-grained
human visual system
Image coding
Image processing
Image quality
Image quality assessment
just-noticeable-difference
Quality assessment
Transform coding
Visualization
ISSN:1051-8215, 1558-2205
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Zusammenfassung:Image quality assessment (IQA) plays a central role in many image processing algorithms and systems. Although many popular IQA models achieves high performance on existing released databases, they are still not well accepted in practical applications due to the not-always satisfactory accuracy on real-world data and situations. In this paper, we revisit the IQA research, and point out an ignored but interesting problem in IQA: the coarse-grained (i.e., when quality variation is sufficiently big, as the setting of most IQA databases up to date) statistical results evaluated on existing databases mask the fine-grained differentiation. Accordingly, we present a survey on image quality assessment from a new perspective: fine-grained image quality assessment (FG-IQA). Recent FG-IQA research on five major kinds of images is introduced, and some popular IQA methods are analyzed from FG-IQA perspective. The potential problems for current IQA research based on existing coarse-grained databases are analyzed and the necessity of more FG-IQA research is justified. Finally, we discuss some challenges and possible directions for future works in FG-IQA.
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2021.3096528