Random projection and orthonormality for lossy image compression

Image and Vision Computing

Volumn 25, Issue 5, 2007, Pages 754-766

  Amador, José J ^a  

^a KENNEDY SPACE CENTER   (United States)

Author keywords

Image compression; Random projection; Transform image coding

Indexed keywords

FEATURE EXTRACTION; IMAGE ANALYSIS; IMAGE CODING; IMAGE RECONSTRUCTION; MATHEMATICAL TRANSFORMATIONS; SIGNAL TO NOISE RATIO;

DIMENSIONALITY REDUCTION; ORTHONORMALITY; RANDOM PROJECTION; TRANSFORM IMAGE CODING;

IMAGE COMPRESSION;

EID: 33847076240     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2006.05.018     Document Type: Article

References (40)

1. Sonka M., Hlavac V., and Boyle R. Image Processing, Analysis, and Machine Vision. second ed., (1999), Brooks/Cole, Pacific Grove
2. E. Bingham, H. Mannila, Random projection in dimensionality reduction: applications to image and text data, in: Proceedings on 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 245-250.
3. J. L. Crowley, K. Schwerdt, Robust tracking and compression for video communication, Proceedings of International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (1999) 2-9.
4. H. Chen, C. H. Chen, Hyperspectral image data unsupervised classification using Gauss-Markov random fields and PCA principle, Proceedings of IEEE International Geoscience and Remote Sensing Symposium (2002) 1431-1433.
5. E. Choi, H. Choi, C. Lee, Principal component analysis with pre-emphasis for compression of hyperspectral imagery, Proceedings of IEEE International Geoscience and Remote Sensing Symposium (2005) 704-706.
6. P. Waldermar, T. A. Ramstad, Hybrid KLT-SVD image compression, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (1997) 2713-2716.
7. S. Zyto, A. Grama, W. Szpankowski, Semi-discrete matrix transforms (SDD) for image and video compression, Proceedings of Data Compression Conference (2002) 484.
8. th International Conference on Machine Leaning and Cybernetics (2005) 5200-5204.
9. F-Z. N. Nacer, A. Zergai{dotless}̈noh, A. Merigot, Global discrete cosine transform for image compression, Proceedings of International Symposium on Signal Processing and its Applications (2001) 545-548.
10. Wu Y.-G., and Tai S.-C. Medical image compression by discrete cosine transform spectral similarity strategy. IEEE Trans. Inf. Tech. Biomed. 5 3 (2001) 236-243
11. I. Hacihaliloglu, M. Kartal, DCT and DWT based image compression in remote sensing images, Proceedings of International Symposium on Antennas and Propagation (2004) 3856-3858.
12. Kolda T.G., and O'Leary D.P. A semi-discrete decomposition for latent semantic indexing in information retrieval. ACM-Trans. Inf. Syst. 16 4 (1998) 322-346
13. th Symposium on Theory of Computing (1998) 604-613.
14. S. Kaski, Dimensionality reduction by random mapping: fast similarity computation for clustering, Proceedings of International Joint Conference on Neural Networks (1998) 413-418.
15. th Annual Symposium on Foundations of Computer Science (1999) 616-623.
16. S. Dasgupta, Experiments with random projection, Proceedings of Uncertainty in Artificial Intelligence (2000).
17. D. Achlioptas, Database-friendly random projections, Proceedings of ACM Symposium on the Principles of Database Systems (2001) 274-281.
18. th International Conference on Machine Learning (2002).
19. S. S. Vempala, Random projection, Tech. Rep., Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA, (2002).
20. M. Agrawal, N. Kayal, N. Saxena, PRIMES in P, Tech. Rep., Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India, (2002).
21. W. B. Johnson, J. Lindenstrauss, Extension of Lipschitz mappings into a Hilbert space, Proceedings of Conference in Modern Analysis and Probability, Contemporary Mathematics (1984) 189-206.
22. Frankl P., and Maehara H. The Johnson-Lindenstrauss lemma and the sphericity of some graphs. J. Comb. Theory Ser. B 44 (1988) 355-362
23. S. Dasgupta, A. Gupta, An elementary proof of the Johnson-Lindenstrauss lemma, Tech. Rep., TR-99-006, International Computer Science Institute, Berkeley, CA, USA, (1999).
24. Hecht-Nielsen R. Context vectors: general purpose approximate meaning representations self-organized from raw data. In: Zurada J.M., Marks II R.J., and Robinson C.J. (Eds). Computational Intelligence: Imitating Life (1994), IEEE Press, Cambridge 43-56
25. Cormen T., Leiserson C., and Rivest R. Introduction to Algorithms. second ed., (1997), McGraw-Hill, New York
26. Noble B., and Daniel J. Applied Linear Algebra. third ed. (1988), Prentice-Hall, Englewood Cliffs
27. Castleman K. Digital Image Processing. first ed. (1996), Prentice-Hall, Englewood Cliffs
28. Martucci S.A. Symmetric convolution and the discrete sine and cosine transforms. IEEE Trans. Sig. Process SP-42 (1994) 1038-1051
29. Narasimha M.J., and Perterson A.M. On the computation of the discrete cosine transform. IEEE Trans. Commun. COM-26 6 (1978) 934-936
30. Yip R., and Rao K.R. A fast computational algorithm for the discrete sine transform. IEEE Trans. Commun. COM-28 2 (1980) 304-307
31. Hartley R.V.L. A more symmetrical Fourier analysis applied to transmission problems. Proc. IRE 30 (1942) 144-150
32. Bracewell R.M. The discrete Hartley transform. J. Opt. Soc. Am. 73 12 (1983) 1832-1835
33. Sorenson H.V., Jones D.L., Burrus C.S., and Heideman M.T. On computing the discrete Hartley transform. IEEE Trans. Acoust. Speech Sig. Proc. ASSP 33 4 (1985) 1231-1238
34. Falkowski B.J. Transformation kernels and algorithms for the generation of forward and inverse Hadamard ordered multi-polarity generalized Walsh transforms. Proceedings of the 37th Midwest Symposium on Circuits and Systems (1994) 825-828
35. Anguh M.M., and Martin R.R. A Truncation method for computing slant transforms with applications to image processing. IEEE Trans. Commun. 43 6 (1995) 2103-2110
36. Kaiser G. The fast Haar transform. IEEE Potentials 17 2 (1998) 34-37
37. Rabbani M., and Jones P.W. Digital Image Compression Techniques (1991), SPIE - International Society for Optical Engineers, New York
38. N. Saito, J.-F. Remy, The polyharmonic local sine transform: a new tool for local image analysis and synthesis without edge effect, Tech. Rep., Department of Mathematics, University of California, Davis, CA, USA, (2004).
39. Press W.H., Teukolsky S.A., Vetterling W.T., and Flannery B.P. NUMERICAL RECIPES in C: The Art of Scientific Computing. second ed. (1999), Cambridge University Press, New York
40. Daubechies I. Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. 41 (1988) 909-996