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Open AccessArticle

Error Resilience for Block Compressed Sensing with Multiple-Channel Transmission

1
Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung City 81148, Taiwan
2
Department of Innovative Information and Technology, Tamkang University, New Taipei City 25137, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(1), 161; https://doi.org/10.3390/app10010161
Received: 22 November 2019 / Revised: 18 December 2019 / Accepted: 19 December 2019 / Published: 24 December 2019
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information)
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. View Full-Text
Keywords: block compressed sensing; error resilience; reconstruction block compressed sensing; error resilience; reconstruction
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MDPI and ACS Style

Huang, H.-C.; Chen, P.-L.; Chang, F.-C. Error Resilience for Block Compressed Sensing with Multiple-Channel Transmission. Appl. Sci. 2020, 10, 161.

AMA Style

Huang H-C, Chen P-L, Chang F-C. Error Resilience for Block Compressed Sensing with Multiple-Channel Transmission. Applied Sciences. 2020; 10(1):161.

Chicago/Turabian Style

Huang, Hsiang-Cheh; Chen, Po-Liang; Chang, Feng-Cheng. 2020. "Error Resilience for Block Compressed Sensing with Multiple-Channel Transmission" Appl. Sci. 10, no. 1: 161.

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