Blind Quality Assessment of Panoramic Images Based on Multiple Viewport Sequences

With the development of virtual reality (VR) technology, panoramic image (PI), which is an important digital form of immersive multimedia, has drawn much attention from researchers. However, distortions are inevitably introduced in the process of processing, encoding and compression, which damages their quality and affects the user’s experience. Therefore, assessing the quality of panoramic images is urgent. In this paper, with the consideration of viewing behavior, we propose a novel blind panoramic image quality assessment model, which consists of three parts, viewport generation, feature extraction and quality prediction. Specifically, inspired by the viewing process of PI, we first generate multiple viewport sequences according to the real viewing trajectory and then extract multilevel features with a pre-trained backbone. The concatenated features are taken as the input of a recurrent neural network to evaluate the perceptual quality of PI. To validate the effectiveness of the proposed method, objective experiments are conducted on the public subjective panoramic image quality database. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.