3D video quality assessment

A key factor in designing 3D systems is to understand how different visual cues and distortions affect the perceptual quality of 3D video. The ultimate way to assess video quality is through subjective tests. However, subjective evaluation is time consuming, expensive, and in most cases not even possible. An alternative solution is objective quality metrics, which attempt to model the Human Visual System (HVS) in order to assess the perceptual quality. The potential of 3D technology to significantly improve the immersiveness of video content has been hampered by the difficulty of objectively assessing Quality of Experience (QoE). A no-reference (NR) objective 3D quality metric, which could help determine capturing parameters and improve playback perceptual quality, would be welcomed by camera and display manufactures. Network providers would embrace a full-reference (FR) 3D quality metric, as they could use it to ensure efficient QoE-based resource management during compression and Quality of Service (QoS) during transmission. In this thesis, we investigate the objective quality assessment of stereoscopic 3D video. First, we propose a full-reference Human-Visual-system based 3D (HV3D) video quality metric, which efficiently takes into account the fusion of the two views as well as depth map quality. Subjective experiments verified the performance of the proposed method. Next, we investigate the No-Reference quality assessment of stereoscopic video. To this end, we investigate the importance of various visual saliency attributes in 3D video. Based on the results gathered from our study, we design a learning based visual saliency prediction model for 3D video. Eye-tracking experiments helped verify the performance of the proposed 3D Visual Attention Model (VAM). A benchmark dataset containing 61 captured stereo videos, their eye fixation data, and performance evaluations of 50 state-of-the-art VAMs is created and made publicly available online. Finally, we incorporate the saliency maps generated by our 3D VAM in the design of the state-of-the- art no-reference (NR) and also full-reference (FR) 3D quality metrics.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat