Image interpolation for progressive transmission by using radial basis function networks

IEEE Transactions on Neural Networks

Volumn 10, Issue 2, 1999, Pages 381-390

  Sigitani, Teruki ^a   Iiguni, Youji ^a,b   Maeda, Hajime ^a,b  

^a OSAKA UNIVERSITY   (Japan)

^b IEEE

Author keywords

Interpolation; Kronecker product; Lossless image compression; Progressive image transmission; Radial basis function network

Indexed keywords

COMPUTATIONAL METHODS; ERROR ANALYSIS; IMAGE ANALYSIS; IMAGE CODING; IMAGE COMMUNICATION SYSTEMS; IMAGE COMPRESSION; INTERPOLATION;

KRONECKER PRODUCT; LOSSLESS IMAGE COMPRESSION; PROGRESSIVE IMAGE TRANSMISSION; RADIAL BASIS FUNCTION NETWORKS (RBFN);

NEURAL NETWORKS;

EID: 0033100650     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.750567     Document Type: Article

References (34)

1. K.-H. Tzou, "Progressive image transmission: A review and comparison," Opt. Eng., vol. 26, no. 7, pp. 581-589, 1987.
2. S. Wong, L. Zaremba, D. Gooden, and H. K. Huang, "Radiologic image compression - A review," Proc. IEEE, vol. 83, pp. 194-219, 1995.
3. P. J. Burt and E. H. Adelson, "The Laplacian pyramid as a compact image code," IEEE Trans. Commun., vol. 31, pp. 532-540, 1983.
4. T. Endo and Y. Yamazaki, "Progressive coding scheme for multi level images," in Proc. Picture Coding Symp., vol. 21, 1986, pp. 21-22.
5. P. Roos, M. A. Viergever, M. C. A. Van Dijke, and J. H. Peters, "Reversible interframe compression of medical images," IEEE Trans. Med. Imag., vol. 7, pp. 328-336, 1988.
6. L. Wang and M. Goldberg, "Reduced-difference pyramid: A data structure for progressive image transmission," Opt. Eng., vol. 28, no. 7, pp. 708-716, 1989.
7. M. Goldberg and L. Wang, "Comparative performance of pyramid data structures for progressive image transmission," IEEE Trans. Commun., vol. 39, pp. 540-548, 1991.
8. B. Aiazzi, L. Alparone, S. Baronti, and F. Lotti, "Lossless image compression by quantization feedback in a content-driven enhanced Laplacian pyramid," IEEE Trans. Image Processing, vol. 6, pp. 831-843, 1997.
9. B. Aiazzi, L. Alparone, and S. Baronti, "A reduced Laplacian pyramid for lossless and progressive image communication," IEEE Trans. Commun., vol. 44, pp. 18-22, 1996.
10. D. Houlding and J. Vaisey, "Low entropy image pyramids for efficient lossless coding," IEEE Trans. Image Processing, vol. 4, pp. 1150-1153, 1995.
11. S. G. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation," IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-11, pp. 674-693, 1989.
12. J. Lu, V. Ralph, and R. R. Estes, Jr., "Comparative study of wavelet image coders," Opt. Eng., vol. 35, no. 9, pp. 2605-2619, 1996.
13. ∼ipl/psnr_results.html
14. J. M. Shapiro, "Embedded image coding using zerotrees of wavelet coefficients," IEEE Trans. Signal Process., vol. SP-41, pp. 3445-3462, 1993.
15. A. Said and W. A. Pearlman, "A new, fast, and efficient image codec based on set partitioning in hierarchical trees," IEEE Trans. Circuits Syst. Video Technol., vol. 6, pp. 243-250, 1996.
16. _, "An image multiresolution representation for lossless and lossy compression," IEEE Trans. Image Processing, vol. 5, pp. 1303-1310, 1996.
17. Z. Xiong, K. Ramchandran, and M. T. Orchard, "Space-frequency quantization for wavelet image coding," IEEE Trans. Image Processing, vol. 6, pp. 677-693, 1997.
18. T. Poggio and F. Girosi, "Networks for approximation and learning," Proc. IEEE, vol. 78, pp. 1481-1497, 1990.
19. J. Park and I. W. Sandberg, "Universal approximation using radial-basis-function networks," Neural Comput., vol. 3, no. 2, pp. 246-257, 1991.
20. F. Girosi, "Some extensions of radial basis functions and their applications in artificial intelligence," Comput. Math. Applic., vol. 24, no. 12, pp. 61-80, 1992.
21. F. M. A. Acosta, "Radial basis function and related models: An overview," Signal Processing, vol. 45, pp. 37-58, 1995.
22. J. Moody and C. J. Darken, "Fast learning in networks of locally-tuned processing units," Neural Comput., vol. 1, no. 2, pp. 281-294 1989.
23. M. Casdagli, "Nonlinear prediction of chaotic times series," Physica D, vol. 35, pp. 335-356, 1989.
24. S. Chen and S. A. Billing, "Neural networks for nonlinear dynamic system modeling and identification," Int. J. Contr., vol. 52, no. 2, pp. 319-346, 1992.
25. G.-Q. Wei and G. Hirzinger, "Parametric shape-from-shading by radial basis functions," IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-19, pp. 353-365, 1997.
26. S. Chen, B. Mulgrew, and P. M. Grant, "A clustering technique for digital communications channel equalization using radial basis function networks," IEEE Trans. Neural Networks, vol. 4, pp. 570-579, 1993.
27. B. Mulgrew, "Applying radial basis functions," IEEE Signal Processing Mag., vol. 13, no. 2, pp. 50-65, 1996.
28. Y. Iiguni, I. Kawamoto, and N. Adachi, "A nonlinear adaptive estimation method based upon local approximation," IEEE Trans. Signal Processing, vol. 45, pp. 1831-1841, 1997.
29. Y. Iiguni, "Hierarchical image coding via cerebellar model arithmetic computers," IEEE Trans. Image Processing, vol. 5, pp. 1393-1401, 1996.
30. P. C. Parks and J. Militzer, "Improved allocation of weights for associative memory storage in learning control systems," in Proc. IFAC Design Methods Contr. Syst., 1991, pp. 507-512.
31. C. A. Micchelli, "Interpolation of scattered data: Distance matrices and conditionally positive definite functions," Constr. Approx., vol. 2, pp. 11-22, 1986.
32. A. Rosenfeld et al., Digital Picture Processing, 2nd ed. New York: Academic, 1982.
33. The Independent JPEG Group's JPEG software, release 6b, 27 Mar. 1998. Available ftp://ftp.uu.net/graphics/jpeg
34. A. C. Huang, "PVRG-JPEG CODEC 1.1," 1993.