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Volume 21
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10.3390/s21030994
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Article

No Reference, Opinion Unaware Image Quality Assessment by Anomaly Detection

by
Marco Leonardi
1,
Paolo Napoletano
1,*,
Raimondo Schettini
1 and
Alessandro Rozza
2
1
Department of Computer Science, Systems and Communications, University of Milano-Bicocca, 20126 Milan, Italy
2
lastminute.com Group, 6830 Chiasso, Switzerland
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(3), 994; https://doi.org/10.3390/s21030994
Submission received: 30 December 2020 / Revised: 23 January 2021 / Accepted: 29 January 2021 / Published: 2 February 2021
(This article belongs to the Section Sensing and Imaging)
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Abstract

We propose an anomaly detection based image quality assessment method which exploits the correlations between feature maps from a pre-trained Convolutional Neural Network (CNN). The proposed method encodes the intra-layer correlation through the Gram matrix and then estimates the quality score combining the average of the correlation and the output from an anomaly detection method. The latter evaluates the degree of abnormality of an image by computing a correlation similarity with respect to a dictionary of pristine images. The effectiveness of the method is tested on different benchmarking datasets (LIVE-itW, KONIQ, and SPAQ).
Keywords: image quality assessment; Gram matrix; convolutional neural network image quality assessment; Gram matrix; convolutional neural network

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MDPI and ACS Style

Leonardi, M.; Napoletano, P.; Schettini, R.; Rozza, A. No Reference, Opinion Unaware Image Quality Assessment by Anomaly Detection. Sensors 2021, 21, 994. https://doi.org/10.3390/s21030994

AMA Style

Leonardi M, Napoletano P, Schettini R, Rozza A. No Reference, Opinion Unaware Image Quality Assessment by Anomaly Detection. Sensors. 2021; 21(3):994. https://doi.org/10.3390/s21030994

Chicago/Turabian Style

Leonardi, Marco, Paolo Napoletano, Raimondo Schettini, and Alessandro Rozza. 2021. "No Reference, Opinion Unaware Image Quality Assessment by Anomaly Detection" Sensors 21, no. 3: 994. https://doi.org/10.3390/s21030994

APA Style

Leonardi, M., Napoletano, P., Schettini, R., & Rozza, A. (2021). No Reference, Opinion Unaware Image Quality Assessment by Anomaly Detection. Sensors, 21(3), 994. https://doi.org/10.3390/s21030994

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