A novel saliency mapping approach for joint multiview video plus depth coding

Abstract

The main consideration of compression efficiency in multi-view is to venture the temporal and inter-view analytical dependencies since all cameras capture the particular frame with variable viewpoints. The era of Multiview coding (MVC) predictor selection statistics is used to compress MVD representation resulting in separate bit streams of texture and video sequences. This coding schemes does not capture the similarities that arise in texture and depth video sequence, whereas joint multiview video plus depth coding (JMVDC) scheme employs the correlation co-efficiency of the motion in texture and depth sequence in rendering the object of interest in the scene. Large amount of data that is produced in this representation becomes a challenge for data storage and network transmission. In JMVDC, the structure enables inter-layer motion prediction mechanism by representing the base and enhancement layers as texture and depth. The inter-dependency of motion in texture and depth content of multi-view are accomplished and achieved by employing different correlation coefficient methods. The proposed Method utilizes depth map to enhance the salient region with the combination of local and global saliency information. The statistical analysis performs the accuracy measure of completeness of local, global and final saliency maps along with the combination of DTCWT by constituting precision, recalland F-measure. The experimental results show that the JMVDC method in saliency map with enhancement achieves the reduction of computational complexity to detect the distinctive region.