The objective of this paper is to research a dynamic computation of Zero-Parallax-Setting (ZPS) for multi-view autostereoscopic displays in order to effectively alleviate blurry 3D vision for images with large disparity. Saliency detection techniques can yield saliency map which
is a topographic representation of saliency which refers to visually dominant locations. By using saliency map, we can predict what attracts the attention, or region of interest, to viewers. Recently, deep learning techniques have been applied in saliency detection. Deep learning-based salient
object detection methods have the advantage of highlighting most of the salient objects. With the help of depth map, the spatial distribution of salient objects can be computed. In this paper, we will compare two dynamic ZPS techniques based on visual attention. They are 1) maximum saliency
computation by Graphic-Based Visual Saliency (GBVS) algorithm and 2) spatial distribution of salient objects by a convolutional neural networks (CNN)-based model. Experiments prove that both methods can help improve the 3D effect of autostereoscopic displays. Moreover, the spatial distribution
of salient objects-based dynamic ZPS technique can achieve better 3D performance than maximum saliency-based method.