Comparative Study of Image Compression Techniques based on Vector Quantization

Authors

  • Castro CV Dept. of CSE, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, India
  • Raj AT Dept. of CSE, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, India
  • Parithi IT Dept. of CSE, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, India
  • Balasubramanian R Dept. of CSE, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, India

DOI:

https://doi.org/10.26438/ijcse/v6i11.386890

Keywords:

SMVQ, MSVQ, TSVQ

Abstract

Image Compression is the art and Science of reducing the amount of data required to represent an image. It is one of the useful and commercially successful technique in the field of Digital Image Compression. The innumerable images are compressed and decompressed daily. Image compression techniques are classified into lossless and lossy compression techniques. This paper covers three lossy compression techniques such as Tree Structured Vector Quantization (TSVQ), TSVQ reduces the quantizer search complexity by replacing full search encoding with a sequence of tree decisions , and Multi Stage Vector Quantization (MSVQ) , Multistage Vector Quantization is a modification of Unconstrained Vector Quantization technique. It is also called as Multistep, Residual or Cascaded Vector Quantization. Multistage Vector Quantization (MSVQ) technique preserves all the features of Unconstrained Vector Quantization technique while decreasing the computational complexity, memory requirements and spectral distortion. And Side Match Vector Quantization (SMVQ), In SMVQ Neighbor pixels within an image are similar unless there is an edge across. But the topic of our interest is Side Match Vector Quantization

References

[1] V.Krishna, Dr. V.P.C.Rao, P.Naresh, P. Rajyalakshmi,“Incorporation of DCT and MSVQ to Enhance Image Compression Ratio of an image” International Research Journal of Engineering and Technology (IRJET), Volume: 03, Issue: 03 | Mar-2016.

[2] Sarita S. Kamble, A.S. Deshpande,“Image Compression Based on Side Match Vector Quantization” International Journal of Engineering Science and Computing, Volume 6 ,Issue No. 5,ISSN 2321 3361 © May 2016.

[3] Malwinder Kaur, Navdeep Kaur, “A Litreature Survey on Lossless Image Compression”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 3, March 2015.

[4] Chuan Qin, Chin-Chen Chang and Yi-Ping Chiu, “A Novel joint Data-Hiding and compression scheme based on SMVQ and Image Inpainting”, IEEE Trans. on Image Process., vol. 23, no. 3, pp. 969-978, March 2014.

[5] Richa Goyal, Jasmeen Jaura, “A Review of Various Image Compression Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 7, July 2014.

[6] Kaur M. and Kaur G. “A survey of lossless and lossy Image compression technique” International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 2, February 2013.

[7] Mukesh Mittal, Ruchika Lamba,“Image Compression Using Vector Quantization Algorithms: A Review” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 6, June 2013.

[8] Arup Kumar Bhattacharjee, Tanumon Bej, Saheb Agarwal, "Comparison Study of Lossless Data Compression Algorithms for Text Data", IOSR-JCE Volume 11, Issue 6, May-June 2013, pp 15-19.

[9] Shruti Porwal, Yashi Chaudhary, Jitendra Joshi, Manish Jain, "Data Compression Methodologies for Lossless Data and Comparison between Algorithms", IJESIT Volume 2, Issue2, March 2013.

[10] C.C.Chen and C. C. Chang, “High capacity SMVQ-based hiding scheme using adaptive index,” Signal Process vol. 90, no. 7,pp. 2141–2149, 2010.

[11] C.C.Chang, Y.C.Li, and J. B. Yeh, Fast codebook search algorithms based on tree-structured vector Quantization, Pattern Recognition Letters, vol. 27, no. 10, pp. 1077-1086, 2006.

[12] S.Mitra, R.Shuyu Yang Kumar, B.Nutter. “An optimized Hybrid Vector Quantization for efficient source Encoding IEEE 2002, 45th Midwest Symposium, Vol. 2, 2002.

[13] Subramanya A, “Image Compression Technique,” Potentials IEEE, Vol. 20, Issue 1, pp 19-23, FebMarch 2001.

[14] Z.M.Lu, J.S.Pan and S.H Sun, “Image Coding Based on classified side match vector quantization”,IEICE Trans. Inf. & Sys. Vol. E83-D(12),Pp.2189-2192, Dec.2000.

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Published

2025-11-18
CITATION
DOI: 10.26438/ijcse/v6i11.386890
Published: 2025-11-18

How to Cite

[1]
C. V. Castro, A. T. Raj, I. T. Parithi, and R. Balasubramanian, “Comparative Study of Image Compression Techniques based on Vector Quantization”, Int. J. Comp. Sci. Eng., vol. 6, no. 11, pp. 386–390, Nov. 2025.

Issue

Vol. 6 No. 11 (2018): IJCSE November Edition

Section

Research Article

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