Abstract
This paper presents a vector quantization algorithm for image compression based on extended associative memories. The proposed algorithm is divided in two stages. First, an associative network is generated applying the learning phase of the extended associative memories between a codebook generated by the LBG algorithm and a training set. This associative network is named EAM-codebook and represents a new codebook which is used in the next stage. The EAM-codebook establishes a relation between training set and the LBG codebook. Second, the vector quantization process is performed by means of the recalling stage of EAM using as associative memory the EAM-codebook. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantages offered by the proposed algorithm is high processing speed and low demand of resources (system memory); results of image compression and quality are presented.
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References
Gray, R.M.: Vector Quantization. IEEE ASSP Magazine 1, 4–9 (1984)
Nasrabadi, N.M., King, R.A.: Image Coding Using Vector Quantization: A Review. IEEE Trans.on Communications 36(8), 957–971 (1988)
Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer, Norwell (1992)
Linde, Y., Buzo, A., Gray, R.: An Algorithm for Vector Quantizer Design. IEEE Trans. on Communications 28(1), 84–95 (1980)
Lloyd, L.P.: Least Squares Quantization in PCM. IEEE Trans. Inform. Theory IT-28, 129–137 (1982)
Bahram, S., Azami, Z., Feng, G.: Robust Vector Quantizer Design Using Competitive Learning Neural Networks. In: Proc. of European Workshop on Emerging Techniques for Communications Terminals, pp. 72–75. IEEE Press, Toulouse France (1997)
Kohonen, T.: Automatic Formation of Topological Maps of Patterns in a Self-organizing System. In: Oja, E., Simula, O. (eds.) Proc. 2SCIA, Scand. Conf. on Image Analysis, Helsinki, Finland, pp. 214–220 (1981)
Kohonen, T.: The Self-Organizing Map. IEEE Proc. 78(9), 1464–1480 (1990)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)
Nasrabadi, N., Feng, Y.: Vector Quantization of Images based upon the Kohonen Self-Organizing Feature Maps. In: IEEE International Conference on Neural Networks, vol. 1, pp. 101–108 (1988)
Amerijckx, C., Verleysen, M., Thissen, P., Legat, J.-D.: Image Compression by Self-Organized Kohonen Map. IEEE Trans. on Neural Networks 9, 503–507 (1998)
Amerijckx, C., Legat, J.-D., Verleysen, M.: Image Compression Using Self-Organizing Maps. Systems Analysis Modelling Simulation 43(11), 1529–1543 (2003)
Guzmán, E., Pogrebnyak, O., Yañez, C.: Design of an Evolutionary Codebook Based on Morphological Associative Memories. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 601–611. Springer, Heidelberg (2007)
Guzmán, E., Pogrebnyak, O., Yañez, C.: A Fast Search Algorithm for Vector Quantization based on Associative Memories. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. LNCS, vol. 5197, pp. 487–495. Springer, Heidelberg (2008)
Sossa, H., Barrón, R., Vázquez, A.: Real-valued Patterns Classification based on Extended Associative Memory. In: Fifth Mexican International Conference on Computer Science, ENC 2004, pp. 213–219. IEEE Computer Society, México (2004)
Barron, R.: Associative Memories and Morphological Neural Networks for Patterns Recall (in Spanish). PhD Thesis, Center for Computing Research (2005)
Steinbuch, K.: Die Lernmatrix. Kybernetik 1(1), 26–45 (1961)
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Guzmán, E., Pogrebnyak, O., Yáñez, C., Manrique, P. (2009). Vector Quantization Algorithm Based on Associative Memories. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds) MICAI 2009: Advances in Artificial Intelligence. MICAI 2009. Lecture Notes in Computer Science(), vol 5845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05258-3_29
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DOI: https://doi.org/10.1007/978-3-642-05258-3_29
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