Visible to the public "DPCM-quantized block-based compressed sensing of images using Robbins Monro approach"Conflict Detection Enabled

Title"DPCM-quantized block-based compressed sensing of images using Robbins Monro approach"
Publication TypeConference Paper
Year of Publication2015
AuthorsA. Pramanik, S. P. Maity
Conference Name2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)
Date PublishedDec. 2015
ISBN Number978-1-4673-8786-6
Accession Number15903161
Keywordsblock SPL algorithm, channel coding, compressed sensing, compressive sampling, differential pulse code modulation, Differential pulse code modulation component, differential pulse coded modulation, DPCM-quantized block-based compressed sensing, Filtering, frequency domain filtering, frequency-domain analysis, Image coding, Image reconstruction, Iterative methods, Lempel-Ziv-Welch channel coding, Lempel-Ziv-Welch channel coding technique, Nyquist rate, parametric iterative CS reconstruction technique, pubcrawl170104, RM approach, Robbins Monro approach, signal reconstruction

Compressed Sensing or Compressive Sampling is the process of signal reconstruction from the samples obtained at a rate far below the Nyquist rate. In this work, Differential Pulse Coded Modulation (DPCM) is coupled with Block Based Compressed Sensing (CS) reconstruction with Robbins Monro (RM) approach. RM is a parametric iterative CS reconstruction technique. In this work extensive simulation is done to report that RM gives better performance than the existing DPCM Block Based Smoothed Projected Landweber (SPL) reconstruction technique. The noise seen in Block SPL algorithm is not much evident in this non-parametric approach. To achieve further compression of data, Lempel-Ziv-Welch channel coding technique is proposed.

Citation Key7443944