Abstract
We address the problem of pseudocolor image compression. Image values represent indices into a look up table (palette). Due to quantization, the neighbouring pixel values (indices) change too much. This deteriorates performance of both lossless and lossy image compression methods. We suggest a preprocessing phase that (a) analyses statistics of the adjacency relations of index values, (b) performs palette optimization, and (c) permutes indices to palette to achieve more smooth image. The smoother image causes that the lossless image compression methods yield less output data. The task to optimally permute palette indices is a NP complete combinatorial optimization. Instead of checking all possibilities, we suggest a reasonable: initial guess and a fast suboptimal hill climbing optimization. The proposed permutation of indices should enhance performance of most lossless compression method used after it. To our knowledge, the proposed reordering followed by our own nonlinear compression technique [IIF97b, HF97a] yields the best compression. Experiments with various images show that the indices reordering provides data savings from 10% to 50%.
This research was supported by the Czech Ministry of Education grant VS96049, the Grant Agency of the Czech Republic 102/97/0480, 102/97/0855.
Chapter PDF
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Zaccarin André and Bede Liu. A novel approach for coding color quantized images. IEEE Transactions on Image Processing, 2(4):442–453, October 1993.
Jaroslav Fojtík and Václav Hlaváč Invisible modification of palette color image for increasing compression ratio of lossless compression methods. Technical l1eport K335/98/159, Czech Technical University, Faculty of Electrical Engineering, Karlovo Niměsti 13, Prague 2, May 1998
Michael Frydrych. Image compression. Master's thesis, Charles University, Faculty of Mathematics Physics, Prague, Czech Reepublic, 1993.
V. Hlaváč and J. Fojtík Adaptive non-linear predictor for lossless image compression. In G. Sommer, K. Daniilidis, and J. Pauli, editors, Proceedings of the conference Computer Analysis of Images and Patterns'97, Kiel, Germany, pages 279–288. Springer-Verlag, LNCS 1296, September 1997.
V. Hlaváč and J. Fojtík. Predictor based on frequency analysis of the local configurations used for lossless image compression. In Proceedings of the 1st IAPR TC1 workshop on Statistical Techniques in Pattern Recognition, Prague, Czech Republic, June 9–11, 1997, pages 73–78, Prague, Czech Republic, June 1997. Institute of Information Theory and Automation, Czech Acadeiny of Sciences.
Andrew C. Hadenfeldt and Khaid Sayood. Compression of color-mapped images. IEEE Transactions on Geoscience and Remote Sensing, 32(3):534–541, May 1994.
R. M. Haralick and L. G. Shapiro. Computer and Robot Vision, Volume I. Addison Wesley, Reading, Ma., 1992.
Nasir D. Memon and Ayalur Venkateswaran. On ordering color maps for lossless predictive coding. IEEE Transactions on Image Processing, 5(11):1522–1527, November 1996.
M.I. Schlesinger. Matematiceskie sredstva obrabotki izobrazenij, in Russian, (Mathematic tools for image processing). Naukova Dumka, Kiev, Ukraine, 1989.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fojtík, J., Hlaváč, V. (1998). Invisible modification of the palette color image enhancing lossless compression. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033334
Download citation
DOI: https://doi.org/10.1007/BFb0033334
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64858-1
Online ISBN: 978-3-540-68526-5
eBook Packages: Springer Book Archive