Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Image Compression using DCT based Compressive Sensing and Vector Quantization

Print
PDF
International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 20
Year of Publication: 2012
Authors:
Dipti Bhatnagar
Sumit Budhiraja
10.5120/7922-1231

Dipti Bhatnagar and Sumit Budhiraja. Article: Image Compression using DCT based Compressive Sensing and Vector Quantization. International Journal of Computer Applications 50(20):34-38, July 2012. Full text available. BibTeX

@article{key:article,
	author = {Dipti Bhatnagar and Sumit Budhiraja},
	title = {Article: Image Compression using DCT based Compressive Sensing and Vector Quantization},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {20},
	pages = {34-38},
	month = {July},
	note = {Full text available}
}

Abstract

Compressive sensing (CS) provides a mathematical framework for utilizing the potentiality of sparse nature of the commonly used signals and has been the subject of scientific research in recent years. CS involves the compression of the data at the first step of image acquisition. This paper presents an image compression algorithm based on DCT based CS and Vector Quantization (VQ). It has been observed from the implementation of the Proposed CS-VQ algorithm that the proposed algorithm gives better PSNR and visual quality when compared with the existing CS-VQ algorithm. The results obtained are even comparable with JPEG algorithm but only when small queue size is considered. The basic concept behind the CS states that small collections of non-adaptive linear projections of a sparse signal can efficiently helps in the reconstruction of the image through the image data sent to the decoder making use of some optimization procedure.

References

  • E. J. Candès, "Compressive sampling," in 2006 IEEE International Congress of Mathematics,2006. ICM 06,Spain, Madrid,Mathematicians. , aug 2006.
  • R. Baraniuk, "Compressive sensing [lecture notes]," IEEE Signal Processing Magazine, vol. 24, pp. 118 –121, july 2007.
  • A. Fletcher, S. Rangan, and V. Goyal, "On the rate-distortion performance of compressed sensing," in IEEE International Conference on Acoustics, Speech and Signal Processing, 2007. ICASSP 2007, vol. 3, (Honolulu, Hawaii, USA), pp. III–885 –III– 888, april 2007.
  • J Wen, Z Chen, Y Hen,J D Villasenor and S Yang "A Compressive sensing image compression algorithm using quantized DCT and Noiselet information" IEEE Signal Processing Magazine, 2010.
  • S. Kadambe and J. Davis, Compressive Sensing and Vector Quantization Based Image Compression, IEEE Signal Processing Conference, 978-1-4244-9721, 2010.
  • Chenwei Deng, Weisi Lin, Bu-sung Lee and Chiew Tong Lau, "Robust Image Compression based on Compressive Sensing", IEEE Signal Processing Conference, 2010.
  • Karim Kanoun, Hossein Mamaghanian, Nadia Khaled and David Atienze, "A Real time Compressed Sensing-based personal Electrocardiogram Monitoring System", EDAA, 2011.
  • T. S. Gunawan, O. O. Khalifa, A. A. Shafie, E. Ambikairajah, "Speech Compression using Compressive Sensing on a Multicore System", IEEE, 4th International Conference on Mechatronics (ICOM), 17-19 May 2011.
  • Wei Dai and Olgica Milenkovic, "Information Theoretical and Algorithmic approaches to Quantized Compressive Sensing" IEEE Transaction on Communication, vol. 59, No. - 7, pp. 1857 – 1866, July 2011.
  • D. A. Huffman, " A method for the construction of Minimum Redundancy Codes", Proc. Of Institute of Radio Engineers (IRE), vol. 40, Issue: 9, pp. 1098-1101, Sept. 1952.
  • R. Gray, "Vector quantization," IEEE Acoustics, Speech and Signal Processing (ASSP) Magazine, vol. 1, Issue: 2, pp. 4 – 29, April 1984.