Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance
1
PRISME Laboratory, University of Orleans, 45072 Orleans, France
2
Department of Computer Science, Norwegian University of Science and Technology, 2802 Gjøvik, Norway
*
Author to whom correspondence should be addressed.
Academic Editor: Byung-Gyu Kim
Appl. Sci. 2021, 11(10), 4661; https://doi.org/10.3390/app11104661
Received: 14 April 2021 / Revised: 10 May 2021 / Accepted: 12 May 2021 / Published: 19 May 2021
(This article belongs to the Special Issue Advances in Perceptual Image Quality Metrics)
An abundance of objective image quality metrics have been introduced in the literature. One important essential aspect that perceived image quality is dependent on is the viewing distance from the observer to the image. We introduce in this study a novel image quality metric able to estimate the quality of a given image without reference for different viewing distances between the image and the observer. We first select relevant patches from the image using saliency information. For each patch, a feature vector is extracted from a convolutional neural network model and concatenated at the viewing distance, for which the quality is predicted. The resulting vector is fed to fully connected layers to predict subjective scores for the considered viewing distance. The proposed method was evaluated using the Colourlab Image Database: Image Quality and Viewing Distance-changed Image Database. Both databases provide subjective scores at two different viewing distances. In the Colourlab Image Database: Image Quality we obtain a Pearson correlation of 0.87 at both 50 cm and 100 cm viewing distances, while in the Viewing Distance-changed Image Database we obtained a Pearson correlation of 0.93 and 0.94 at viewing distance of four and six times the image height. The results show the efficiency of our method and its generalization ability.
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Keywords:
image quality assessment; convolutional neural network; viewing distance; feature combination
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MDPI and ACS Style
Chetouani, A.; Pedersen, M. Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance. Appl. Sci. 2021, 11, 4661. https://doi.org/10.3390/app11104661
AMA Style
Chetouani A, Pedersen M. Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance. Applied Sciences. 2021; 11(10):4661. https://doi.org/10.3390/app11104661
Chicago/Turabian StyleChetouani, Aladine; Pedersen, Marius. 2021. "Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance" Appl. Sci. 11, no. 10: 4661. https://doi.org/10.3390/app11104661
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