Stereoscopic visual saliency prediction based on stereo contrast and stereo focus

Abstract

In this paper, we exploit two characteristics of stereoscopic vision: the pop-out effect and the comfort zone. We propose a visual saliency prediction model for stereoscopic images based on stereo contrast and stereo focus models. The stereo contrast model measures stereo saliency based on the color/depth contrast and the pop-out effect. The stereo focus model describes the degree of focus based on monocular focus and the comfort zone. After obtaining the values of the stereo contrast and stereo focus models in parallel, an enhancement based on clustering is performed on both values. We then apply a multi-scale fusion to form the respective maps of the two models. Last, we use a Bayesian integration scheme to integrate the two maps (the stereo contrast and stereo focus maps) into the stereo saliency map. Experimental results on two eye-tracking databases show that our proposed method outperforms the state-of-the-art saliency models.

Quality Assessment Based on Edge and Saliency for Synthesized Virtual View

“…The edge extraction method needs to be improved, and the extraction results have a great relationship with the edge extraction factor and texture complexity, so the results have great subjectivity [4,5]. The salient region of the human visual system is also called the region of interest (ROI), and the attention of the human eye to the region of interest is usually higher than that of the non interested region [6]. In this paper, we proposed a novel image quality assessment metric based on edge pixel differences and ROI.…”