Adaptive compressed sensing of multi-view videos based on the sparsity estimation

Senlin Yang; Xilong Li; Xin Chong

doi:10.1117/12.2295082

15 November 2017 Adaptive compressed sensing of multi-view videos based on the sparsity estimation

Senlin Yang, Xilong Li, Xin Chong

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 1060530 (2017) https://doi.org/10.1117/12.2295082
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

The conventional compressive sensing for videos based on the non-adaptive linear projections, and the measurement times is usually set empirically. As a result, the quality of videos reconstruction is always affected. Firstly, the block-based compressed sensing (BCS) with conventional selection for compressive measurements was described. Then an estimation method for the sparsity of multi-view videos was proposed based on the two dimensional discrete wavelet transform (2D DWT). With an energy threshold given beforehand, the DWT coefficients were processed with both energy normalization and sorting by descending order, and the sparsity of the multi-view video can be achieved by the proportion of dominant coefficients. And finally, the simulation result shows that, the method can estimate the sparsity of video frame effectively, and provides an active basis for the selection of compressive observation times. The result also shows that, since the selection of observation times is based on the sparsity estimated with the energy threshold provided, the proposed method can ensure the reconstruction quality of multi-view videos.

Citation Download Citation

Senlin Yang, Xilong Li, and Xin Chong "Adaptive compressed sensing of multi-view videos based on the sparsity estimation", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060530 (15 November 2017); https://doi.org/10.1117/12.2295082

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