Digital images are an important form of data
and are used for in almost every applications. Personal
navigation is one of the modern field of image compression,
where the user needs map in real time. Map images cannot
be used directly, because of its huge size. There are various
techniques that can be used to compress these images. The
basic approach of image compression is to reduce the
number of image pixels without affecting the quality of the
image. In order to achieve compression, the redundancy
present in the image is to be removed. The principle
objective of this paper is to analyse various image
compression techniques and thereby we are presenting a
survey on research papers focused on map image
compression.
Krishna. V : received her B.Tech Degree in Computer Science
& Engg. from University of Kerala in 2007. Currently she is
pursuing her Masters in Computer Science & Engineering
from University of Kerala. She worked at T.K.M College of
Engg, Kollam as lecturer. Her research interests include Image
Processing and Networking.
Cinly Thomas : is currently working as Asst. Professor in
Computer Science Dept, Baselios Mathews II College of Engg,
Sasthamcotta, Kollam. Her research interests include Image
processing, Networking and Database Technology.
Radhakrishnan B : is working as Asst. Professor in Computer
Science department. He has more than 14 years experience in
teaching and has published papers on data mining and image
processing. His research interests include image processing, data
mining, and image mining.
Image Compression
Huffman Coding
Context
Tree
LZW Coding
Map Image
We have discussed different compression techniques and
papers related to the compression of map images. It is
difficult to compare the performance of compression
technique, if identical data sets and performance
measures are not used. After the study of all the
techniques it is found that lossless compression
techniques are more effective over lossy compression
techniques. Lossy provides higher compression ratio
than lossless. From the above discussed papers, we have
concluded that the context tree modelling with
arithmetic coding provide better compression
performance than the other methods, but the
decompression performance of dictionary based coding
is 10-20 time faster than context tree modelling with
arithmetic coding.
[1] Saif Zahir and arber Borici,"A Fast Lossless
Compression Scheme for Digital Map Image Using
Color Separation", IEEE International Conference on
Acoustics, Speech and Signal processing, Dallas, Texas,
USA ,June 2010
[2] Alexandre Akimov,Pavel Kopylov and Pasi Franti,
Semi-Adaptive Dictionary Based Compression of
MapImages",Graphicon'2002,Nizhny,Novogorod,Russi
a,pp.219-223,September 2002.
[3] Pavel Kopylov and Pasi Franti, "Compression of Map
images by Multilayer Context tree Modeling" ,IEEE
Transaction on Image Processing,Vol.14,NO.1,January
2005.
[4] Alexandre Akimov,Alexandre Kolesnikov and Pasi
Franti,"Lossless Compression of Color map images by
Context Tree Modeling",IEEE Transaction on Image
Processing,Vol.16,NO.1,Januray 2007.
[5] Ageenkon E and Franti P,"Compression of Large
Binary images in Digital Spatial Libraries",Computers
&Graphics 24(1):91-98,February 2000.
[6] Pasi Franti, Eugene I Ageenko,Pavel Kopylov,Sami
Grohn,Florian Berger,"Compression of Map Images
for real time applications",Image and Vision
Computing,Vol.22,NO.13,1105-1115,November 2004
[7] Forchhammer, S., & Jensen, "Content Layer
progressive Coding of Digital Maps",IEEE Transaction
on Image Processing, 11(12), 1349-1356,2002
[8] Eugene Ageenko,Pavel Kopylov,Pasi Franti,"On the
Size and Shape of Multi-Level Context Templates for
Compression of Map Images", in IEEE Conference
,February 2001.
[9] S.Srikanth and Sukadev Meher, "Compression
Efficiency for Combining Different Embedded Image
Compression Techniques with Huffman Encoding",
IEEE, pp. 816-820, 2013.
[10] Firas A. Jassim and Hind E. Qassim,"Five Modulus
Method for Image Compression", Signal And Image
processing :An International Journal(SIPIJ)
Vol.3,pp.19-28,2012.
[11] Ageenko EI,Franti P,"Forward-Adaptiv method for
compressing large binary images",Software Practise
&Experience 1999;29(11):943}52.
[12] Franti P,Ageenko EI,"On the use of Context tree for
binary image compression",IEEE Proceedings of the
Franti P,Ageenko EI,"On the use of Context tree for
binary image compression",IEEE Proceedings of the
International Conference on Image
Processing(ICIP '99).Japan:Kobe,1999.
[13] P. G. Howard, F. Kossentini, B. Martins, S.
Forchhammer, and Rucklidge,"The Emerging JBIG2
Standard",IEEE Trans,Circuit Syst.Video
Technol.Vol.8, pp.838-848,Nov.1998.
[14] B. Martins and S. Forchhammer,"Tree Coding of Bilevel
Images",IEEE Trans.Image processing,Vol.7,pp.
[15] V. Ratnakar,"RAPP:Lossless Image Compression with
runs of adaptive pixel patterns", in Proc. 32nd
Asilomar Conf. Signals, Systems and
Computers,Nov.1998.
[16] M. J.Weinberger, J. Rissanen, and R. B.
Arps,"Applications of universal context modeling to
losseless compression of grey-scale images",IEEE
Trans.Image Processing,Vol.5,pp.575-586,April 1996.
[17] S.Jayaraman,S.Esaikkirajan and T.Veerakumar,Digital
Image Processing, New Delhi:Tata McGraw- Hill,2011
