Saliency Inspired Quality Assessment of Stereoscopic 3D Video

To study the visual attentional behavior of Human Visual System (HVS) on 3D content, eye tracking experiments are performed and Visual Attention Models (VAMs) are designed. One of the main applications of these VAMs is in quality assessment of 3D video. The usage of 2D VAMs in designing 2D quality metrics is already well explored. This paper investigates the added value of incorporating 3D VAMs into Full-Reference (FR) and No-Reference (NR) quality assessment metrics for stereoscopic 3D video. To this end, state-of-the-art 3D VAMs are integrated to quality assessment pipeline of various existing FR and NR stereoscopic video quality metrics. Performance evaluations using a large scale database of stereoscopic videos with various types of distortions demonstrated that using saliency maps generally improves the performance of the quality assessment task for stereoscopic video. However, depending on the type of distortion, utilized metric, and VAM, the amount of improvement will change.