Blind image quality assessment (BIQA) of authentically distorted images is a challenging problem due to the lack of a reference image and the coexistence of blends of distortions with unknown characteristics. In this article, we present a convolutional neural network based BIQA model. It encodes the input image into multi-level features to estimate the perceptual quality score. The proposed model is designed to predict the image quality score but is trained for jointly treating the image quality assessment as a classification, regression, and pairwise ranking problem. Experimental results on three different datasets of authentically distorted images show that the proposed method achieves comparable results with state-of-the-art methods in intra-dataset experiments and is more effective in cross-dataset experiments.

Celona, L., Schettini, R. (2022). Blind quality assessment of authentically distorted images. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION, 39(6), B1-B10 [10.1364/JOSAA.448144].

Blind quality assessment of authentically distorted images

Celona, Luigi
;
Schettini, Raimondo
2022

Abstract

Blind image quality assessment (BIQA) of authentically distorted images is a challenging problem due to the lack of a reference image and the coexistence of blends of distortions with unknown characteristics. In this article, we present a convolutional neural network based BIQA model. It encodes the input image into multi-level features to estimate the perceptual quality score. The proposed model is designed to predict the image quality score but is trained for jointly treating the image quality assessment as a classification, regression, and pairwise ranking problem. Experimental results on three different datasets of authentically distorted images show that the proposed method achieves comparable results with state-of-the-art methods in intra-dataset experiments and is more effective in cross-dataset experiments.
Articolo in rivista - Articolo scientifico
Image metrics; Image processing; Image quality; Image quality assessment; Neural networks; Visual system;
English
B1
B10
10
Celona, L., Schettini, R. (2022). Blind quality assessment of authentically distorted images. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION, 39(6), B1-B10 [10.1364/JOSAA.448144].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/356979
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