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).

Leonardi, M., Napoletano, P., Schettini, R., Rozza, A. (2021). No reference, opinion unaware image quality assessment by anomaly detection. SENSORS, 21(3), 1-16 [10.3390/s21030994].

No reference, opinion unaware image quality assessment by anomaly detection

Leonardi M.;Napoletano P.
;
Schettini R.;
2021

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).
Articolo in rivista - Articolo scientifico
Convolutional neural network; Gram matrix; Image quality assessment;
English
2-feb-2021
2021
21
3
1
16
994
none
Leonardi, M., Napoletano, P., Schettini, R., Rozza, A. (2021). No reference, opinion unaware image quality assessment by anomaly detection. SENSORS, 21(3), 1-16 [10.3390/s21030994].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/318880
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