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

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Extra info for Compact Image Coding from Multiscale Edges

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M. Livstone Wavelets: A Conceptual Overview, May 1994. [3] Stephane Mallat, Characterization of Signals from Multiscale Edges, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 14 No 7, July 1992. [4] Stephane Mallat and S. Zhong, Compact Image Coding from Edges with Wavelets, IEEE proceedings ICASSP, 1991, p 2393. [5] Stephane Mallat and S. Zhong, Zero-crossings of a Wavelet Transform, IEEE Transactions on Information Theory, vol 37, No 4, July 1991. [6] S. P. Watson editor, p 179, MIT Press 1993.

4 Perceptual distortion of reconstructed images Once the mapping of probabilities of error detection by the eye have been computed for the reconstructed image, it can be useful, in order to compare more easily our coding scheme with some other reference scheme, to convert this mapping into a single number. To do so, we may use the following formula: Dist = 100 N p(i, j)2 i,j where Dist will be the perceptual distortion for the entire image, N is the total number of pixel in the image, and p(i, j) is the probability of error detection for the pixel (i, j).

Therefore we subtract the numerical value dist0 obtained for no coding at all, which corresponds to the case of a perceptually perfect image quality for the reconstructed image. We then obtain the following formula: Distnorm = 100 N p(i, j)2 − dist0 i,j We used a factor of 100 to obtain values of a convenient order of magnitude. 5 Results We used this perceptual distortion measure to assess the quality of the reconstructed images obtained from our coding scheme. Typical results of the evaluation are shown in Figure 14.

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

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