By Pankaj N. Topiwala (auth.), Pankaj N. Topiwala (eds.)
An fascinating new improvement has taken position within the electronic period that has captured the mind's eye and ability of researchers around the world - wavelet photo compression. This know-how has deep roots in theories of imaginative and prescient, and can provide functionality advancements over all different compression equipment, equivalent to these in response to Fourier transforms, vectors quantizers, fractals, neural nets, and so forth. it truly is this innovative new expertise that's offered in Wavelet picture and Video Compression, in a sort that's available to the biggest viewers attainable.
Wavelet photo and Video Compression is split into 4 elements. half I, Background Material, introduces the elemental mathematical constructions that underly photo compression algorithms with the purpose of supplying a simple advent to the mathematical thoughts which are must haves for the rest of the publication. It explains such themes as switch of bases, scalar and vector quantization, bit allocation and rate-distortion idea, entropy coding, the discrete-cosine rework, wavelet filters and different comparable issues. half II, Still picture Coding, offers a spectrum of wavelet nonetheless snapshot coding ideas. half III, Special themes in nonetheless snapshot Coding, presents numerous instance coding schemes with a distinct taste in both process or software area. half IV, Video Coding, examines wavelet and pyramidal coding strategies for video facts.
Wavelet snapshot and Video Compression serves as a very good reference and will be used as a textual content for complicated classes overlaying the subject.
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Extra resources for Wavelet Image and Video Compression
Preliminaries 27 FIGURE 6. A simple example of lowpass and highpass filtering using the Haar filters, and (a) A sinusoid with a low-magnitude white noise signal superimposed; (b) the lowpass filtered signal, which appears smoother than the original; (c) the highpass filtered signal, which records the high frequency noise content of the original signal. is sampled at a rate below its Nyquist rate, resulting in a lower frequency signal (10 cycles implies Nyquist = 20 samples, whereas only 14 samples are taken).
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Time-Frequency Analysis, Wavelets And Filter Banks 51 So we see that orthonormal wavelet bases give rise to lowpass and highpass filters, whose coefficients are given by various dot products between the dilated and translated scaling and wavelet functions.