Distributed compressive video sensing

Low-complexity video encoding has been applicable to several emerging applications. Recently, distributed video coding (DVC) has been proposed to reduce encoding complexity to the order of that for still image encoding. In addition, compressive sensing (CS) has been applicable to directly capture co...

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Bibliographic Details
Main Authors: Li-Wei Kang, Chun-Shien Lu
Format: Conference Proceeding
Language:English
Subjects:
(distributed) compressive sampling/sensing
compressive video sensing
Decoding
Discrete wavelet transforms
distributed video coding
Encoding
Image coding
Image reconstruction
Reconstruction algorithms
Video coding
Video compression
Videoconference
Wireless sensor networks
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Summary:Low-complexity video encoding has been applicable to several emerging applications. Recently, distributed video coding (DVC) has been proposed to reduce encoding complexity to the order of that for still image encoding. In addition, compressive sensing (CS) has been applicable to directly capture compressed image data efficiently. In this paper, by integrating the respective characteristics of DVC and CS, a distributed compressive video sensing (DCVS) framework is proposed to simultaneously capture and compress video data, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. At the decoder, compressed video can be efficiently reconstructed using the modified GPSR (gradient projection for sparse reconstruction) algorithm. With the assistance of the proposed initialization and stopping criteria for GRSR, derived from statistical dependencies among successive video frames, our modified GPSR algorithm can terminate faster and reconstruct better video quality. The performance of our DCVS method is demonstrated via simulations to outperform three known CS reconstruction algorithms.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2009.4959797