Least Square Based View Synthesis Prediction for Multi-view Video Coding

Abstract

In the applications of Free View TV, pre-estimated depth information is available to synthesize the intermediate views as well as to assist texture video coding. Existing view synthesis prediction schemes generate virtual view picture only from interview pictures. However, there are many types of signal mismatches caused by depth errors, camera heterogeneity or illumination difference across views, and these mismatches decrease the prediction capability of virtual view picture. In this paper, we propose a least square based view synthesis prediction method to enhance the prediction capability of virtual view picture. This method integrates least square estimation with backward warping to synthesize the virtual view picture, which not only utilizes the adjacent views information but also the temporal information. Experiments show that the proposed method reduces the bitrate by up to 23% relative to the multi-view video coding standard, and about 16% relative to the conventional view synthesis prediction method.