Still Image and Video Compression with MATLAB by K. S. Thyagarajan

By K. S. Thyagarajan

This ebook describes the rules of photograph and video compression strategies and introduces present and renowned compression criteria, similar to the MPEG sequence. Derivations of proper compression algorithms are constructed in an easy-to-follow type. a number of examples are supplied in each one bankruptcy to demonstrate the strategies. The publication comprises complementary software program written in MATLAB SIMULINK to provide readers hands-on adventure in utilizing and making use of quite a few video compression tools. Readers can improve the software program via together with their very own algorithms.

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By K. S. Thyagarajan

This ebook describes the rules of photograph and video compression strategies and introduces present and renowned compression criteria, similar to the MPEG sequence. Derivations of proper compression algorithms are constructed in an easy-to-follow type. a number of examples are supplied in each one bankruptcy to demonstrate the strategies. The publication comprises complementary software program written in MATLAB SIMULINK to provide readers hands-on adventure in utilizing and making use of quite a few video compression tools. Readers can improve the software program via together with their very own algorithms.

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In the following section, we will develop the algorithm for such an optimal quantizer. 1 Lloyd–Max Quantizer A Lloyd–Max quantizer is an optimal quantizer in the mean square error (MSE) sense [3,4]. It depends on the input probability density function (pdf) and the number of levels allowed in the quantizer. We point out here that the input analog sample is a random variable. Hence, it is described by a pdf. The quantizer design is as follows: Let x be a real-valued scalar random variable with a continuous pdf px (x).

4 Illustration of aliasing distortion in a real image: (a) original image downsampled by a factor of 4 in both dimensions with no prefiltering, (b) reconstructed image and cropped to size 215 × 275, (c) original image prefiltered by a 7 × 7 Gaussian lowpass filter and then downsampled by 4 in both directions, (d) image reconstructed from that in (c). In (b) and (d), the reconstruction is carried out using bicubic spline interpolation. 2 Nonideal Sampling Practical sampling devices use rectangular pulses of finite width.

3 Convert a 256 × 256 array of pixels of alternating black and white (BW) dots to a hexagonal sampling grid and display both arrays as images. Assume the sample spacing to be equal to unity. Solution We will use the linear transformation t1 t2 = 1 1 1 −1 n1 n2 to transform pixels in the rectangular array into the hexagonal array. Note that the range for the hexagonal grid is [(2, 512) × (−255,255)]. 6c,d correspond to the zoomed versions with a zoom factor of 4. 3 is listed below. 22) and display them.

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