Abstract
The gray levels quantization technique is used to generate images which limit the number of color levels resulting in a reduction of the image size, while it preserves the quality perceived by human observers. The problem is very relevant for image storage and web distribution, as well as in the case of devices with limited bandwidth, storage and/or computational capabilities. An efficient evolutionary algorithm for the inverse gray level quantization problem, based on a technique of dynamical local fitness evaluation, is presented. A population of blur operators is evolved with a fitness given by the energy function to be minimized. In order to avoid the unfeasible computational overhead due to the fitness evaluation calculated on the entire image, an innovative technique of dynamical local fitness evaluation has been designed and integrated in the evolutionary scheme. The sub–image evaluation area is dynamically changed during evolution of the population, and the evolutionary scheme operates a form of machine learning while exploring subarea which are significatively representative of the global image. The experimental results confirm the adequacy of such a method.
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References
Bedini, L., Gerace, I., Salerno, E., Tonazzini, A.: Models and Algorithms for Edge-Preserving Image Reconstruction. Advances in Imaging and Electron Physics 97, 86–189 (1996)
Bedini, L., Gerace, I., Tonazzini, A.: A Deterministic Algorithm for Reconstruction Images with Interacting Discontinuities. CVGIP: Graphical Models Image Process 56, 109–123 (1994)
Blake, A.: Comparison of the Efficiency of Deterministic and Stochastic Algorithms for Visual Reconstruction. IEEE Trans. Pattern Anal. Machine Intell. 11, 2–12 (1989)
Blake, A., Zisserman, A.: Visual Reconstruction. MIT Press, Cambridge, MA (1987)
Gerace, I., Martinelli, F., Sanchini, G.: Estimation of the free parameters in th problem of edge-preserving image reconstraction by a shooting method. In: SMMSP 2006. The 2006 International TICSP Workshopo on Spectral Methods and Multirate Signal Processing, Florence, Italy, pp. 205–212 (2006)
Gerace, I., Pandolfi, R., Pucci, P.: A new GNC Algorithm for Spatial Dithering. In: SMMSP2003. proceedings of the 2003 International Workshop on Spectral Methods and Multirate Signal Processing, pp. 109–114 (2003)
Gerace, I., Pandolfi, R., Pucci, P.: A new estimation of blur in the blind restoration problem. In: ICIP 2003. proceeding of IEEE International Conference on Image Processing, p. 4. IEEE Computer Society Press, Los Alamitos (2003)
Geman, D., Reynolds, G.: Constrained Restoration and the Recovery of Discontinuities. IEEE Trans. Pattern Anal. Machine Intell. 14, 367–383 (1992)
Goldberg, D., Debb, K.: A comparative analysis of selection schemes used in Genetic Algorithms. In: Rawlins, G.J.E. (ed.) Foundation of genetic Algorithms, Morgan Kaufman, San Francisco (1991)
Hansen, C.: Analysis of Discrete Ill-Posed Problems By Means of the L-Curve. SIAM Review 34, 561–580 (1992)
Holland, S.H.: Adaptation in natural and artificial systems. The University of Michigan press, Ann Arbor, MI (1975)
Li, S.Z.: Roof-Edge Preserving Image Smoothing Based on MRFs. IEEE Trans. Image Process 9, 1134–1138 (2000)
Nikolova, M.: Markovian Reconstruction Using a GNC Approach. IEEE Trans. Image Process 8, 1204–1220 (1999)
Reginska, T.: A Regularization Parameter in Discrete Ill-Posed Problems. SIAM J. Sci. Comput. 17, 740–749 (1996)
Sokolov, A., Whitley, D.: Unbiased tournament selection. In: Proceeding of GECCO 2005, Washington, DC, USA, pp. 1131–1138 (2005)
Stevenson, R.L.: Inverse Halftoning via MAP Estimation. IEEE Trans. Image Process 6, 574–583 (1997)
Tonazzini, A.: Blur Identification Analysis in Blind Image Deconvolution Using Markov Random Fields. Pattern Recogn. and Image Analysis 11, 669–710 (2001)
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Gerace, I., Mastroleo, M., Milani, A., Moraglia, S. (2007). An Evolutionary Approach to Inverse Gray Level Quantization. In: Qiu, G., Leung, C., Xue, X., Laurini, R. (eds) Advances in Visual Information Systems. VISUAL 2007. Lecture Notes in Computer Science, vol 4781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76414-4_25
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DOI: https://doi.org/10.1007/978-3-540-76414-4_25
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