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Journal of Theoretical and Applied Computer Science

Tytuł artykułu

Simulation of signal oriented algorithm with lossy data compression

Autorzy Gubareni, N.  Katkow, A. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN In this paper the additive algorithm of spectral analysis is considered. This algorithm consists of algebraic summation of samples of basis functions taken at certain points of an interval of an independent variable of a given function. Two variants of simulation of the additive algorithm are considered. In the first variant the process of receiving discrete values of continuous spectrum of a continuous function is considered. The second variant uses the additive algorithm for Discrete Cosine Transform (DCT), which is widely used in practice for converting graphic images. The conception of the accelerated calculation of the DCT is considered on examples of real two dimensional graphic images. The fragments of proposed programs for simulation of the additive algorithm for continuous signals and for image processing are represented in meta language.
Słowa kluczowe
EN high performance computing   lossy data compression   spectral analysis  
Wydawca Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
Czasopismo Journal of Theoretical and Applied Computer Science
Rocznik 2015
Tom Vol. 9, nr 2
Strony 51--65
Opis fizyczny Bibliogr. 13 poz., rys., tab., wz.
autor Gubareni, N.
  • Czestochowa University of Technology, Czestochowa, Poland
autor Katkow, A.
[1] Shao X., Johnson S. G., Type-IV DCT, DST, and MDCT algorithms with reduced numbers of arithmetic operations, Signal Processing, Vol. 88, No. 6, 2008, pp. 1553-1564
[2] S. G. Johnson and M. Frigo, A modified split-radix FFT with fewer arithmetic operations, IEEE Trans. Signal Processing, vol. 55, no. 1, 2007, pp. 111–119.
[3] M. Vetterli, H. J. Nussbaumer, Simple FFT and DCT algorithms with reduced number of operations, Signal Processing 6 (4), 1984, pp. 267–278.
[4] M. Puschel and J. M. F. Moura, The algebraic approach to the discrete cosine and sine transforms and their fast algorithms, SIAMJ, vol. 32, no. 5, 2003, pp. 1280–1316.
[5] Z. Guo, B. Shi, and N. Wang, Two new algorithms based on product system for discrete cosine transform, Signal Processing, vol. 81, 2001, pp. 1899–1908.
[6] S. Sahami and M.G. Shayesteh, Bi-level image compression technique using neural networks, IET Image Process, Vol. 6, Iss. 5, pp. 496–506, 2012.
[7] Pralhadrao V Shantagiri and K.N.Saravanan, Pixel Size Reduction Lossless Image Compression Algorithm, IJCSIT, Vol 5, no. 2, 2013, pp. 87-95
[8] Tzong Jer Chen and Keh-Shih Chuang, A Pseudo Lossless Image Compression Method, IEEE, 2010, pp. 610-615.
[9] C.-H. Chen, B.-D. Liu, J.-F. Yang, Recursive architectures for realizing modified discrete cosine transform and its inverse, IEEE Trans. Circuits Syst. II 50 (1), 2003, pp. 38–45.
[10] Katkow A.F., Dual Formulas Additive Fourier Transformation. In: Mathematical Modeling and Theory of Electrical Circuit, Puchov G.E. (Ed.), Naukova Dumka, Kiev, No. 16, 1978, pp. 53-59.
[11] Katkov A., Wegrzyn-Skrzypczak E., Speedy Numerical Algorithms and Architecture for Additive Spectral Analysis, Journal of Mathematical Modeling and Algorithms, V.1, No.3, 2002, pp. 225-241.
[12] Katkow A., Additive Algorithms of Spectral Analysis of Signals in Real Time, In book: An Introductory Guide to Image and Video Processing, iConcept Press Ltd., USA, 2013, 251-269.
[13] Katkow A., Additive Algorithms for Accelerated Compression of Information. In book: Information Systems Architecture and Technology. Selected Aspects of Communication and Computational Systems, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław, ISBN 978-83-7493-856-3, 2014, pp. 147-158.
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