Non-reference Quality Assessment Model using Deep learning for Omnidirectional Images

Abstract

Image quality assessment (IQA) has been a popular research topic in image processing. However, most studies until now have been focusing on traditional images and only a few focused on omnidirectional images. Unlike in the case of traditional images, the users can only view a part of 360-degree images at a time, and thus tend to focus more on specific regions of the image. This makes predicting quality scores for omnidirectional images a challenging task since most existing models for traditional images usually treat all regions of the image equally. In this paper, we propose an omnidirectional image quality assessment model based on deep learning. This model focuses on learning the features of the middle region of input images. The model first automatically predicts the quality scores for patches sampled from the input image. The quality score of the image will then be calculated by weighted averaging of the patch quality scores based on their positions. Experimental results show that the proposed model provides very promising accuracy for predicting quality scores of omnidirectional images.