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By Voloshynovskiy, Herrigel, Pun

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Walsh transforms, decay considerably rapidly with spectral coefficient indices. The latter is a manifestation of the transform energy compaction capabilities studied in chapter 2. The speed of the decay and the shapes of autocorrelation functions and power spectra are in a certain way associated with a kind of ‘average’ size and shape of objects in images. 9. Pixel neighborhoods: Cl-neighborhood. The set of exercises ‘Image autocorrelation functions and power spectra’ is intended for studying these features and associations.

14. In the section ‘DPCM coding: 1D versus 2D prediction’, DPCM with 1D and 2D predictions is implemented for comparison of prediction efficiency for different images and evaluation of immunity to impulse noise in communication channels. Parameters of the exercise are the dynamic range of the quantized prediction error and the probability of error in transmission of the quantized prediction error. 14. An example of output displays in the exercise ‘Prediction errors: 1D and 2D prediction’. 2-16 Advanced Digital Imaging Laboratory Using MATLAB® reconstruction error, is found for each particular test image selected by the user.

In the exercise, these attributes are given in the entrance menu under the label ‘Pixel statistical attributes’. 3. Image local mean, medians, standard deviations, maxima, minima and range. 4. Pixel global cardinalities and ranks. are displayed in the form of images homomorphic to the test image. After this the user is prompted to choose an option of displaying local pixel attributes in a running window and set the dimensions of the window. 5 present examples of outcomes of the exercise. Note that the replacement of pixels with their rank is a procedure called histogram equalization.

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Blur-Deblur Attack against Document Protection Systems Based on Digital Watermarking by Voloshynovskiy, Herrigel, Pun


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