From Event: SPIE Optical Engineering + Applications, 2018
Quality assessment of images is of key importance for mulmedia applications. In this paper we present a new full reference objective metric to predict the quality of images using deep neural networks. The network makes use of both the color as well as frequency information extracted from reference and distorted images. Our method comprises of extracting a number of equal sized random patches from the reference image and the corresponding patches from the distorted image, then feeding the patches themselves as well as their 3-scale wavelet transform coefficients as input to a neural network. The architecture of the network consists of four branches, with the first three generating frequency features and the fourth extracting color features. Feature extraction is carried out using 12 to 15 convolutional layers and one pooling layer, while two fully connected layers are used for regression. The overall image quality is computed as a weighted sum of patch scores, where local weights are also learned by the network using two additional fully connected layers. The network was trained using TID2013 and tested on TID2013, CSIQ and LIVE image databases. Our results show high correlations with subjective test scores, are generalizable for certain types of distortions and are competitive with respect to the state-of-the-art methods.
Pinar Akyazi and Touradj Ebrahimi, "A new objective metric to predict image quality using deep neural networks," Proc. SPIE 10752, Applications of Digital Image Processing XLI, 107521Q (Presented at SPIE Optical Engineering + Applications: August 23, 2018; Published: 17 September 2018); https://doi.org/10.1117/12.2322709.
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