Robust Transmission of Compressed Sensing Signals with Error Correction Codes
Compressed sensing is one of the developing techniques with superior compression performances over existing schemes in the field of lossy data compression. Due to the observations that compressed signals are vulnerable to channel errors during transmission, data protection techniques may also be applied to the compressed signals at the encoder before transmitting to the decoder over lossy channels. We apply the error correction codes for the protection of compressed sensing signals of digital images to reach robust transmission in this paper. With the error correction codes, some redundancies would be introduced to protect compressed signals. By doing so, reconstructed image qualities would be improved; however, this may cause the reduction of compression performances in compressed sensing. And there comes the trade-off between data protection capability and compression performance for robust transmission. We propose to use different levels of redundancies for the protection of compressed sensing signals. Simulation results have presented the effectiveness of applying error correction codes and majority voting to protect compressed signals. For different lossy rates, reconstructed image qualities would be improved with error correction codes. Induced errors from lossy channels during transmission can be alleviated, which leads to the effective protection of compressed sensing signals.
Keywordscompressed sensing error correction codes transmission
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- 1.Romberg, J.: Imaging via Compressive Sampling. IEEE Signal Processing Magazine, 25, 14–20 (2008)Google Scholar
- 2.Candes, E.J., Wakin, M.B.: An Introduction to Compressive sampling. IEEE Signal Processing Magazine, 25, 21–30 (2008)Google Scholar
- 3.Arildsen, T., Larsen, T.: Compressed Sensing with Linear Correlation between Signal and Measurement Noise. Signal Processing, 98, 275–283 (2014)Google Scholar
- 4.Huang, H.C., Chang, F.C.: Error Resilience for Compressed Sensing with Multiple-Channel Transmission. Journal of Information Hiding and Multimedia Signal Processing, 6, 847–856 (2015)Google Scholar
- 5.Huang, H.C., Chang, F.C.: Robust Image Watermarking Based on Compressed Sensing Techniques. Journal of Information Hiding and Multimedia Signal Processing, 5, 275–285 (2014)Google Scholar
- 6.Chang, F.C., Huang, H.C.: The Analysis of Reconstruction Efficiency with Compressive Sensing in Different K-Spaces. Proc. Int’l Conf. on Robot, Vision and Signal Processing, 67–70 (2015)Google Scholar
- 7.Candes, E.J., Tao, T.: Decoding by Linear Programming. IEEE Trans. Inform. Theory, 51, 4203–4215 (2005)Google Scholar
- 8.Chambolle, A.: An Algorithm for Total Variation Minimization and Applications. Journal of Mathematical Imaging and Vision, 20, 89–97 (2004)Google Scholar
- 9.Wang, Y., Orchard, M.T., et al.: Multiple Description Coding Using Pairwise Correlating Transforms. IEEE Trans. Image Processing, 10, 351–366 (2001)Google Scholar
- 10.Goyal, V.K., Kovacevic, J.: Generalized Multiple Description Coding with Correlating Transforms. IEEE Trans. Inform. Theory, 47, 2199–2224 (2001)Google Scholar
- 11.Huang, H.C., Chen, P.L., Chang, F.C.: Error Resilient Transmission for Compressed Sensing of Color Images with Multiple Description Coding. Proc. Int’l Conf. on Robot, Vision and Signal Processing, 63–66 (2015)Google Scholar
- 12.Lin, S. and Costello, D.J.: Error Control Coding, 2nd Ed. Pearson Prentice Hall (2004)Google Scholar