Compact Image Coding from Multiscale Edges by Dalbegue, Baras, Sidiropoulos

By Dalbegue, Baras, Sidiropoulos

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Sample text

For example, this representation can be used to denoise the image because, from this representation, one can compute the Lipschitz exponents and by putting a threshold on the exponents, remove white noise from a noisy image. Therefore, one can suggest to use this representation either for purposes other than image compression, or within another coding scheme. This representation can be very useful in pattern recognition algorithms or classification because it extracts important image structure, and classification is often performed using this type of information.

Barlaud, P. Mathieu, I. Daubechies, Image Coding Using Vector Quantization in the Wavelet Transform Domain, IEEE Proceedings ICASSP, p 2297, April 1990. [15] Lewis, Knowles, Image Compression using 2-D Wavelet Transform, IEEE Transactions on Image Processing, Vol 1, April 1992, p 244. C. L. A. M. Gray, Using Vector Quantization for Image Processing, Proceedings of the IEEE, No 9, Vol 81, Sept 1993. 28 [17] S. Ohtsuka and M. Inoue, Quality Evaluation of Pictures with Multiple Impairments Based on Visually Weighted Errors, Proceedings of the SID, vol 30, No 1, 1989.

Daly, R. Gray, Pruned Tree-structured Vector Quantization in Image Coding, IEEE Proceedings ICASSP, Glasgow 1989, p 1735. C. Duthou, Tree-structured Face Recognition Algorithm using a Multiresolution Scheme, Rapport de Stage , Ecole Nationale Superieure des Telecommunications, 1994. [22] A. Singh and V. Bove, Image Quality Measure Based on a Human Visual System Model, IEEE Journal on Selected Areas in Communications, Vol 11, No 1, Jan 1993. [23] J. Kim, H. Lee and J. Choi, Subband Coding Using Human Visual Characteristics for Image Signals, IEEE Journal on Selected Areas in Communications, Vol 11, No 1, Jan 93.

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