Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model

Omnidirectional video enables spherical stimuli with the $360 \times 180^ \circ$ viewing range. Meanwhile, only the viewport region of omnidirectional video can be seen by the observer through head movement (HM), and an even smaller region within the viewport can be clearly perceived through eye movement (EM). Thus, the subjective quality of omnidirectional video may be correlated with HM and EM of human behavior. To fill in the gap between subjective quality and human behavior, this paper proposes a large-scale visual quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which collects 60 reference sequences and 540 impaired sequences. Our VQA-OV dataset provides not only the subjective quality scores of sequences but also the HM and EM data of subjects. By mining our dataset, we find that the subjective quality of omnidirectional video is indeed related to HM and EM. Hence, we develop a deep learning model, which embeds HM and EM, for objective VQA on omnidirectional video. Experimental results show that our model significantly improves the state-of-the-art performance of VQA on omnidirectional video.Comment: Accepted by ACM MM 201

A joint motion & disparity motion estimation technique for 3D integral video compression using evolutionary strategy

3D imaging techniques have the potential to establish a future mass-market in the fields of entertainment and communications. Integral imaging, which can capture true 3D color images with only one camera, has been seen as the right technology to offer stress-free viewing to audiences of more than one person. Just like any digital video, 3D video sequences must also be compressed in order to make it suitable for consumer domain applications. However, ordinary compression techniques found in state-of-the-art video coding standards such as H.264, MPEG-4 and MPEG-2 are not capable of producing enough compression while preserving the 3D clues. Fortunately, a huge amount of redundancies can be found in an integral video sequence in terms of motion and disparity. This paper discusses a novel approach to use both motion and disparity information to compress 3D integral video sequences. We propose to decompose the integral video sequence down to viewpoint video sequences and jointly exploit motion and disparity redundancies to maximize the compression. We further propose an optimization technique based on evolutionary strategies to minimize the computational complexity of the joint motion disparity estimation. Experimental results demonstrate that Joint Motion and Disparity Estimation can achieve over 1 dB objective quality gain over normal motion estimation. Once combined with Evolutionary strategy, this can achieve up to 94% computational cost saving

Streaming and User Behaviour in Omnidirectional Videos

Omnidirectional videos (ODVs) have gone beyond the passive paradigm of traditional video, offering higher degrees of immersion and interaction. The revolutionary novelty of this technology is the possibility for users to interact with the surrounding environment, and to feel a sense of engagement and presence in a virtual space. Users are clearly the main driving force of immersive applications and consequentially the services need to be properly tailored to them. In this context, this chapter highlights the importance of the new role of users in ODV streaming applications, and thus the need for understanding their behaviour while navigating within ODVs. A comprehensive overview of the research efforts aimed at advancing ODV streaming systems is also presented. In particular, the state-of-the-art solutions under examination in this chapter are distinguished in terms of system-centric and user-centric streaming approaches: the former approach comes from a quite straightforward extension of well-established solutions for the 2D video pipeline while the latter one takes the benefit of understanding users’ behaviour and enable more personalised ODV streaming