LIU Zhuo, WANG Anhong, ZENG Bing, ZHANG Xue, BAI Huihui, LI Zhihong. Distributed Compressive Video Sensing with Adaptive Measurements Based on Structural Similarity[J]. Chinese Journal of Electronics, 2013, 22(3): 594-598.
Citation: LIU Zhuo, WANG Anhong, ZENG Bing, ZHANG Xue, BAI Huihui, LI Zhihong. Distributed Compressive Video Sensing with Adaptive Measurements Based on Structural Similarity[J]. Chinese Journal of Electronics, 2013, 22(3): 594-598.

Distributed Compressive Video Sensing with Adaptive Measurements Based on Structural Similarity

Funds:  This work is supported in part by Sino-Singapore Joint Research Projet (No.2010DFA11010), the National Natural Science Foundation of China (No.61073142, No.61272262, No.61210006, No.61272051), the Doctor Startup Foundation of TYUST (No.20092011), the International Cooperative Program of Shanxi Province (No.2011081055)
  • Received Date: 2012-07-01
  • Rev Recd Date: 2012-09-01
  • Publish Date: 2013-06-15
  • This paper presents a Distributed compressive video sensing scheme with Adaptive measurements (DCVS-AM). In this approach, the key frame in each Group of pictures (GOP) is coded by Compressive sensing (CS) with a fixed measurement rate; whereas other frames in the same GOP are compressed by an adaptive random projection in two stages, yielding the Adaptive compressive sensing (ACS) frames. The first stage uses a small and fixed measurement rate and recovers a coarse version. In the second stage, each coarse-version ACS-frame together with its proceeding and following key frames will go through a joint analysis at the decoder side and the analysis result Structural similarity (SSIM) that is based on a motion-guided interpolation and calculated in a multilevel discrete wavelet transform domain is sent back to the encoder side to facilitate a re-sampling of the ACS-frame with an adaptive measurement rate. Experimental results show that our proposed DCVS-AM consistently outperforms the state-of-the-art DCVS with a fixed measurement.
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