Abstract
In this paper a combination of algorithms useful for image compression standard is discussed. The main algorithm, named predictive vector quantization (PVQ), is based on competitive neural networks quantizer and neural networks predictor. Additionally, the noiseless Huffman coding is used. The experimental results are presented and the performance of the algorithm is discussed.
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References
Gray, R.: Vector quantization. IEEE ASSP Magazine, 4–29 (1984)
Gersho, A., Gray, R.M.: Vector quantization a. signal compression. Kluwer Academic Publishers, Dordrecht (1992)
Rutkowski, L., Cierniak, R.: Image compression by competitive learning neural networks and predictive vector quantization. Applied Mathematics and Computer Science 6 (1996)
Manikopoulos, C.N.: Neural networks approach to DPCM system designe for image coding. IEE Proceedings-I (1992)
Cierniak, R., Rutkowski, L.: On image compression by competitive neural networks and optimal linear predictors. Signal Processing: Image Communication - a Eurosip Journal 15, 559–565 (2000)
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© 2004 Springer-Verlag Berlin Heidelberg
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Cierniak, R. (2004). An Image Compression Algorithm Based on Neural Networks. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_108
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DOI: https://doi.org/10.1007/978-3-540-24844-6_108
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
Online ISBN: 978-3-540-24844-6
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