Joint Tracking and Multiview Video Compression

Cha Zhang and Dinei Florencio

Abstract

In immersive communication applications, knowing the user’s 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 view-dependent compression schemes by jointly performing user tracking and compression. A face tracker tracks the user’s 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.

Details

Publication typeInproceedings
Published inVCIP
PublisherSPIE
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