Research Article

AMSI - Semigraph approach for Lossless Image Compression and Image Processing

by  Gaidhani Y.S., Deshpande C.M.
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Issue 37
Published: September 2025
Authors: Gaidhani Y.S., Deshpande C.M.
10.5120/ijca2025925639
PDF

Gaidhani Y.S., Deshpande C.M. . AMSI - Semigraph approach for Lossless Image Compression and Image Processing. International Journal of Computer Applications. 187, 37 (September 2025), 41-46. DOI=10.5120/ijca2025925639

                        @article{ 10.5120/ijca2025925639,
                        author  = { Gaidhani Y.S.,Deshpande C.M. },
                        title   = { AMSI - Semigraph approach for Lossless Image Compression and Image Processing },
                        journal = { International Journal of Computer Applications },
                        year    = { 2025 },
                        volume  = { 187 },
                        number  = { 37 },
                        pages   = { 41-46 },
                        doi     = { 10.5120/ijca2025925639 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2025
                        %A Gaidhani Y.S.
                        %A Deshpande C.M.
                        %T AMSI - Semigraph approach for Lossless Image Compression and Image Processing%T 
                        %J International Journal of Computer Applications
                        %V 187
                        %N 37
                        %P 41-46
                        %R 10.5120/ijca2025925639
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

Semigraph was defined by Sampathkumar as a generalization of a graph. In this paper a star semigraph is constructed by clustering a monochrome image, called semigraph of segmented image. Adjacency matrix of the semigraph of segmented image, AMSI, is used for storing the image. An algorithm for converting an image into AMSI and conversely an algorithm to retrieve the image from AMSI are given. Ratio of the size of original JPEG image to the size AMSI on an average is 1.6. AMSI gives a lossless image compression technique with compression ratio 1.6 for JPEG images. The same compressed representation of an image can further be used to do various operations on the image. Using AMSI, algorithms to find photographic negative and pseudocoloring of a grey scale image, and colour masking of a colour image are also given.

References
  • Ashutosh, N. Subhash Chandra Bose, Ashwini Kumar, A novel Image CompressionTech.: Wavelet-MFOCPN, (2012) 492-495.
  • Bapat R. B., Graphs and Matrices, First Edition, Hindustan Book Agency, New Delhi, (2012).
  • Firas Jasmim, Hind E Quasim, Five Modules Method for Image Compression, SIPIJ, (2012) Vol-3, No-5, 19-28.
  • Gaidhani Y. S., Deshpande C. M., Athawale B. P., Adjacency Matrix of a Semigraph, Electronic Notes in Discrete Mathematics, 63 (2017) 399–406.
  • Gonzalez Rafael, Richard Woods, Digital Image Processing, Third Edition (2011), Pearson.
  • Harary F., Graph Theory, Narosa Publishing House, New Delhi, (1988).
  • Jau -Ji Shen, Hsiu-Chaun Huang, An adoptive Image Compression Method based on Vector Quantization, IEEE, pp-377-381, (2010).
  • Muthiah R., Neelakantan K., Sharma V., Arora A., Image Compression and Reconstruction using cubic spline interpolation technique, Journal of Comp. Sci., (2019) Vol-5, No-5, 388-391.
  • Pralhadrao V Shantagiri, K. N. Saravanan, Pixelsize Reduction Lossless Image Compression Algorithm, IJCSIT, Vol-5, (2013).
  • Rajakumar K., T. Arivoli, Implementation of Multiwavelet transform coding for lossless Image Compression, IEEE, (2013) 634-637.
  • S. Sahani, M. G. Shayesteh, Bi-level image compression technique using neural networks, IET Image Process, (2012) Vol-16, No-5, 496-506.
  • Sampathkumar E., Semigraphs and their applications, Report on the Research project, submitted to Department of Science and Technology (DST), India, (May-2000).
  • Saravan C., R. Ponalagusamy, Lossless Image Compression using Source symbol reduction and Huffman Coding, International Journal of Image Processing, Vol-3, No-5, (2009).
  • Sharma D. K., Gaur Loveleen, Image Compression and feature extraction using Kohonen’s SOM neural network, Journal of Strategic E-commerce, Vol-5, No-1, (2005).
  • Srikant S., Sukhdev Meher, Compression Efficiency for Combining Different Embedded Image Algorithms, IEEE, pp-816-820, (2013).
  • Weinberger M. J., Seroussi G., The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS, IEEE International Conference on Image Processing, USA, pp-68-72, (1999).
  • Yei-Fei Tan, Wooi-Nee Tan, Image Compression Technique Utilizing Reference Points Coding with Threshold values, IEEE, IET, pp-74-77, (2012).
  • Yerva Suresh, Smita Nair, Krishnan Kutty, Lossless Image Compression based on data folding, IEEE, pp-19-28, (2012).
  • Zukoski, Mathew J., Boult, Terrance, Iyriboz, Tunc, A novel approach to medical Image Compression, International Journal of Bioinformatics Research and Applications, Vol-2, No-1, (2006).
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Adjacency matrix of semigraph semigraph of segmented image (SSI) adjacency matrix of semigraph of segmented image (AMSI)

Powered by PhDFocusTM