Optimizing Multiview Video plus Depth Prediction Structures for Interactive Multiview Video Streaming

Several multiview video coding standards have been developed to efficiently compress images from different camera views capturing the same scene by exploiting the spatial, the temporal and the interview correlations. However, the compressed texture and depth data have typically many interview coding dependencies, which may not suit interactive multiview video streaming (IMVS) systems, where the user requests only one view at a time. In this context, this paper proposes an algorithm for the effective selection of the interview prediction structures (PSs) and associated texture and depth quantization parameters (QPs) for IMVS under relevant constraints. These PSs and QPs are selected such that the visual distortion is minimized, given some storage and point-to-point transmission rate constraints, and a user interaction behavior model. Simulation results show that the novel algorithm has near-optimal compression efficiency with low computational complexity, so that it offers an effective encoding solution for IMVS applications.