Quality Assessment of Stereoscopic 360degree Images from Multiviewports Objective quality assessment of stereoscopic panoramic images becomes a challenging problem owing to the rapid growth of 360degree contents. Different from traditional 2D image quality assessment (IQA), more complex aspects are involved in 3D omnidirectional IQA, especially unlimited field of view (FoV) and extra depth perception, which brings difficulty to evaluate the quality of experience (QoE) of 3D omnidirectional images. In this paper, we propose a multiviewport based fullreference stereo 360 IQA model. Due to the freely changeable viewports when browsing in the headmounted display, our proposed approach processes the image inside FoV rather than the projected one such as equirectangular projection (ERP). In addition, since overall QoE depends on both image quality and depth perception, we utilize the features estimated by the difference map between left and right views which can reflect disparity. The depth perception features along with binocular image qualities are employed to further predict the overall QoE of 3D 360 images. The experimental results on our public Stereoscopic OmnidirectionaL Image quality assessment Database (SOLID) show that the proposed method achieves a significant improvement over some wellknown IQA metrics and can accurately reflect the overall QoE of perceived images.

Quality Assessment for Omnidirectional Video with Consideration of Temporal Distortion Variations Omnidirectional video, also known as 360degree video, offers an immersive visual experience by providing viewers with an ability to look in all directions within a scene. The quality assessment for omnidirectional video is still a quite difficult task compared to 2D video. As the temporal changes of spatial distortions can considerably influence human visual perception, this paper proposes a full reference objective video quality assessment metric by considering both the spatial characteristics of omnidirectional video and the temporal variation of distortions across frames. Firstly, we construct a spatioxe2x80x93temporal quality assessment unit to evaluate the average distortion in temporal dimension at eye fixation level. The smoothed distortion value is then consolidated by the characteristics of temporal variations. Afterwards, a global quality score of the whole video sequence is produced by pooling. Finally, our experimental results show that our proposed VQA method improves the prediction performance of existing VQA methods for omnidirectional video.