Evaluating Convolutional Neural Networks for No -Reference Image Quality Assessment
Kyriakos D. Apostolidis, Theodore Polyzos, Ioannis Grigoriadis, George A. Papakostas · 2021
In the past years, deep learning evolution has helped the development of computer vision systems. However, the quality of images plays a significant role in the effectiveness of these systems and it would be useful to know the quality of the images that are imported into our systems. No-reference image quality assessment is a challenging procedure, which tries to predict the quality of an image without using any reference image. In this paper, we evaluate the performance of widely used deep learning models in no-reference image quality assessment. To that end, we used transfer learning on 8 pre-trained models which we fit into 3 datasets related to image quality assessment. The performance of these models was studied in terms of mean absolute percentage error (MAPE). Although most of the models performed reasonably well in a MAPE range of 15% to 40% depending on the dataset used, the best performing one in a single dataset was DenseNet201 with a MAPE of 9.8%, while the overall best performing model was ResNet50.