Wavelets in Signal and Image Analysis pp 281-304 | Cite as
Multi-Layered Image Representation
Abstract
The main contribution of this work is a new paradigm for image representation and image compression. We describe a new multi-layered representation technique for images. An image is parsed into a superposition of coherent layers: smooth-regions layer, textures layer, etc. The multi-layered decomposition algorithm consists in a cascade of compressions applied successively to the image itself and to the residuals that resulted from the previous compressions. During each iteration of the algorithm, we code the residual part in a lossy way: we only retain the most significant structures of the residual part, which results in a sparse representation. Each layer is encoded independently with a different transform, or basis, at a different bitrate; and the combination of the compressed layers can always be reconstructed in a meaningful way. The strength of the multi-layer approach comes from the fact that different sets of basis functions complement each others: some of the basis functions will give reasonable account of the large trend of the data, while others will catch the local transients, or the oscillatory patterns. This multi-layered representation has a lot of beautiful applications in image understanding, and image and video coding. We have implemented the algorithm and we have studied its capabilities.
Keywords
Discrete Cosine Transform Wavelet Coefficient Image Compression Wavelet Packet Good BasisPreview
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References
- Antonini, M., Barlaud, M., Mathieu, P., and Daubechies, I. (1992) . Image coding using wavelet transform. IEEE Trans. on Image Processing, 1(2): 205–220.CrossRefGoogle Scholar
- Auscher, P., Weiss, G., and Wickerhauser, M. (1992). Local sine and cosine bases of Coifman and Meyer. In Wavelets-A Tutorial, pages 237–256. Academic Press.Google Scholar
- Bottou, L., Haffner, P., Howard, P., Simard, P., Bengio, Y., and Cun, Y. L. (1998). High quality document image compression with DjVu. To appear in Journal of Electronic Imaging.Google Scholar
- Chang, T. and Kuo, C. (1993). Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. on Image Processing, 2, (4) :429–441.CrossRefGoogle Scholar
- Chen, S. (1995). Basis Pursuit. PhD thesis, Stanford University, Dept. of Statistics.Google Scholar
- Coifman, R. and Meyer, Y. (1991). Remarques sur l’analyse de Fourier à fenêtre. C.R. Acad. Sci. Paris I, pages 259–261.Google Scholar
- Coifman, R. and Meyer, Y. (1992) . Size properties of wavelet packets. In Ruskai et al, editor, Wavelets and their Applications, pages 125–150. Jones and Bartlett.Google Scholar
- Coifman, R. and Wickerhauser, M. (1992). Entropy-based algorithms for best basis selection. IEEE Trans. on Information Theory, 38(2):713–718.MATHCrossRefGoogle Scholar
- Davis, G. (1998). A wavelet-based analysis of fractal image compression. IEEE Trans. on lmage Proc e ssinn7(2):141–142MATHCrossRefGoogle Scholar
- Davis, G. and Chawla, S. (1997). Imnage coding using optimized significance tree. In IEEE Data Compression Conference -DCC’97, pages 387–396.Google Scholar
- DeVore, R., Jawerth, B., and Lucier, B. (1992). Image compression through wavelet transform coding. IEEE Trans. on Information Theory, 38, (2) : 719–746.CrossRefGoogle Scholar
- Lewis, A. and Knowles, G. (1992). Imnage compression using the 2-D wavelet transform. IEEE Trans. on Image Processing, 1, (2) :244–250.CrossRefGoogle Scholar
- Li, J., Cheng, P., and Kuo, C. (1995). An embedded wavelet packet transform technique for texture compression. In SPIE Vol 2569, pages 602–613.Google Scholar
- Mallat, S. (1998). A Wavelet Tour of Signal Processing. Academic Press.MATHGoogle Scholar
- Mallat, S. and Zhang, Z. (1993). Matching pursuits with time-frequency dictionaries. IEEE Trans. on Signal Processing, 41(12):3397–3415.MATHCrossRefGoogle Scholar
- Malvar, H. (1998). Biorthogonal and nonuniform lapped transforms for transform coding with reduced blocking and ringing artifacts. IEEE Transactions on Signal Processing, 46(4):1043–1053.CrossRefGoogle Scholar
- Matviyenko, G. (1996). Optimized local trigonometric bases. Applied and Computational Harmonic Analysis, 3:301–323.MathSciNetMATHCrossRefGoogle Scholar
- Meyer, F. (2001). Image compression with adaptive local cosines : A comparative study. In International Conference on Image Processing, ICIP’01, Thessaloniki, Greece, Oct. 2001. IEEE Press.Google Scholar
- Meyer, F., Averbuch, A., and Strömberg, J.-O. (1998). Fast wavelet packet image compression. In IEEE Data Compression Conference -DCC’98. Google Scholar
- Meyer, F., Averbuch, A., and Strömberg, J.-O. (2000). Fast adaptive wavelet packet image compression. IEEE Trans. on Image Processing, pages 792–800.Google Scholar
- Meyer, F. and Coifman, R. (1997). Brushlets: a tool for directional image analysis and image compression. Applied and Computational Harmonic Analysis, pages 147–187.Google Scholar
- Neff, R. and Zakhor, A. (1997). Very low bit-rate video coding based on matching pursuits. IEEE Trans. Circ. Sys. for Video Tech., 7, 1:158–171.CrossRefGoogle Scholar
- Ramchandran, K. and Vetterli, M. (1993). Best wavelet packet bases in a rate-distortion sense. IEEE Trans. on Image Processing, 2(2):160–175.CrossRefGoogle Scholar
- Rao, K. and Hwang, J. (1996). Techniques and Standards for Image, Video, and Audio Coding. Prentice Hall.Google Scholar
- Said, A. and Pearlman, W. A. (1996). A new fast and efficient image codec based on set partioning in hierarchical trees. IEEE Trans.on Circ. Ѡ Sys. for Video Tech., 6:243–250.CrossRefGoogle Scholar
- Shapiro, J. (1993). Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. on Signal Processing, 41(12):3445–3462.MATHCrossRefGoogle Scholar
- Sriram, P. and Marcellin, M. (1995). Image coding using wavelet transforms and entropy-constrained treillis quantization. IEEE Trans. on Image Processing, 4:725–733.CrossRefGoogle Scholar
- Wickerhauser, M. (1995). Adapted Wavelet Analysis from Theory to Software. A.K. Peters.Google Scholar
- Witten, I., Neal, R., and Cleary, J. (1987). Arithmetic coding for data compression. Communications of the ACM, 30,6:520–540.CrossRefGoogle Scholar
- Xiong, Z., Ramchandran, K., and Orchard, M. (1997). Space-frequency quantization for wavelet image coding. IEEE Trans. on Image Process., 6(5):677–693.CrossRefGoogle Scholar