Error Resilience for Block Compressed Sensing with Multiple-Channel Transmission

Compressed sensing is well known for its superior compression performance, in existing schemes, in lossy compression. Conventional research aims to reach a larger compression ratio at the encoder, with acceptable quality reconstructed images at the decoder. This implies looking for compression performance with error-free transmission between the encoder and the decoder. Besides looking at compression performance, we applied block compressed sensing to digital images for robust transmission. For transmission over lossy channels, error propagation or data loss can be expected, and protection mechanisms for compressed sensing signals are required for guaranteed quality of the reconstructed images. We propose transmitting compressed sensing signals over multiple independent channels for robust transmission. By introducing correlations with multiple-description coding, which is an effective means for error resilient coding, errors induced in the lossy channels can effectively be alleviated. Simulation results presented the applicability and superiority of performance, depicting the effectiveness of protection of compressed sensing signals.

[1]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[2]  Linglong Dai,et al.  Compressive Sensing Based Time Domain Synchronous OFDM Transmission for Vehicular Communications , 2013, IEEE Journal on Selected Areas in Communications.

[3]  Moncef Gabbouj,et al.  Adaptive sampling for compressed sensing based image compression , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

[4]  Richard G. Baraniuk,et al.  From Denoising to Compressed Sensing , 2014, IEEE Transactions on Information Theory.

[5]  Wendong Wang,et al.  A perturbation analysis of nonconvex block-sparse compressed sensing , 2015, Commun. Nonlinear Sci. Numer. Simul..

[6]  Bu-Sung Lee,et al.  Robust Image Coding Based Upon Compressive Sensing , 2012, IEEE Transactions on Multimedia.

[7]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[8]  R. B. Deshmukh,et al.  A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications , 2018, IEEE Access.

[9]  Thomas Arildsen,et al.  Compressed sensing with linear correlation between signal and measurement noise , 2013, Signal Process..

[10]  Thomas Blumensath,et al.  Compressed Sensing With Nonlinear Observations and Related Nonlinear Optimization Problems , 2012, IEEE Transactions on Information Theory.

[11]  Chun-Shien Lu,et al.  Compressive image sensing for fast recovery from limited samples: A variation on compressive sensing , 2015, Inf. Sci..

[12]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[13]  Pierre Weiss,et al.  An Analysis of Block Sampling Strategies in Compressed Sensing , 2013, IEEE Transactions on Information Theory.

[14]  Chengyi Xiong,et al.  Image representation using block compressive sensing for compression applications , 2013, J. Vis. Commun. Image Represent..

[15]  Sheila S. Hemami,et al.  Reconstruction-optimized lapped orthogonal transforms for robust image transmission , 1996, IEEE Trans. Circuits Syst. Video Technol..

[16]  J. Romberg,et al.  Imaging via Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[17]  Michael T. Orchard,et al.  Multiple description coding using pairwise correlating transforms , 2001, IEEE Trans. Image Process..