No reference image quality assessment using gabor convolutional neural networks

Abstract

With the emergence of image capturing devices and increase in usage of internet, massive volume of network data was occupied with digital images. For efficient data transmission, the image may undergo several processing units in from the point it captures to the display/storage device. It may result in loss of the perceptual image quality. Therefore, it is necessary to estimate the image quality to measure the quality of experience. It was found that the convolutional neural networks serve as potential tool for effective feature extraction in many image processing applications. Particularly, with the first layer as Gabor filters, the robustness of the network can be reinforced with learnable Gabor parameters. This paper proposes Gabor Convolutional Neural Network method for No-Reference Image Quality Assessment. Their well-defined spatial structured filters are promising in extracting quality features from the local patches and maps them to perceptual quality scores. Our proposed architecture was tested over synthetic and authentic databases such as LIVE, TID2013, CSIQ, LIVE-MD, MDID2016, LIVE Wild and KoNiQ-10k. The proposed approach was also tested on the Waterloo 3D phase-II database, which contains high-resolution images of both the eyes individually with their respective DMOS scores. The proposed approach out performs over LIVE-MD and LIVE Wild and competes with existing algorithm over other databases.