Robust Distributed Multiview Video Compression for Wireless Camera Networks

We present a novel framework for robustly delivering video data from distributed wireless camera networks that are characterized by packet drops. The main focus in this work is on robustness which is imminently needed in a wireless setting. We propose two alternative models to capture interview correlation among cameras with overlapping views. The view-synthesis-based correlation model requires at least two other camera views and relies on both disparity estimation and view interpolation. The disparity-based correlation model requires only one other camera view and makes use of epipolar geometry. With the proposed models, we show how interview correlation can be exploited for robustness through the use of distributed source coding. The proposed approach has low encoding complexity, is robust while satisfying tight latency constraints and requires no intercamera communication. Our experiments show that on bursty packet erasure channels, the proposed H.263+¹ based method outperforms baseline methods such as H.263+ with forward error correction and H.263+ with intra refresh by up to 2.5 dB. Empirical results further support the relative insensitivity of our proposed approach to the number of additional available camera views or their placement density.