Joint Tracking and Multiview Video Compression In immersive communication applications, knowing the useru0027s viewing position can help improve the efficiency of multiview compression and streaming significantly, since often only a subset of the views are needed to synthesize the desired view(s). However, uncertainty regarding the viewer location can have negative impacts on the rendering quality. In this paper, we propose an algorithm to improve the robustness of viewdependent compression schemes by jointly performing user tracking and compression. A face tracker tracks the useru0027s head location and sends the probability distribution of the face locations as one or many particles. The server then applies motion model to the particles and compresses the multiview video accordingly in order to improve the expected rendering quality of the viewer. Experimental results show significantly improved robustness against tracking errors.

Robust distributed multiview video compression for wireless camera networks We propose a novel method of exploiting interview correlation among cameras that have overlapping views in order to deliver errorresilient video in a distributed multicamera system. The main focus in this work is on robustness which is imminently needed in a wireless setting. Our system has low encoding complexity, is robust while satisfying tight latency constraints, and requires no intersensor communication. In this work, we build on and generalize PRISM,1 an earlier proposed singlecamera distributed video compression system. Specifically, decoder motion search, a key attribute of singlecamera PRISM, is extended to the multiview setting to include decoder disparity search based on twoview camera geometry. Our proposed system, dubbed PRISMMC (PRISM multicamera), achieved PSNR gains of up to 1.7 dB over a PRISM based simulcast solution in experiments over a wireless channel simulator.