By Srdjan Stanković, Irena Orović, Ervin Sejdić
This ebook is designed for college students, execs and researchers within the box of multimedia and similar fields with a necessity to profit the fundamentals of multimedia structures and sign processing. Emphasis is given to the research and processing of multimedia signs (audio, photographs, and video). unique perception into the main correct mathematical gear and alterations utilized in multimedia sign processing is given. a special courting among diversified modifications is usually incorporated, establishing new views for outlining novel transforms in particular purposes. certain realization is devoted to the compressive sensing quarter, which has an outstanding strength to give a contribution to extra development of recent multimedia structures. as well as the theoretical options, quite a few general and extra lately authorized algorithms for the reconstruction of alternative sorts of indications are thought of. additional info and info also are supplied to let a finished research of audio and video compression algorithms. eventually, the e-book connects those ideas to different vital parts of multimedia platforms, resembling the research of optical media, electronic watermarking, and telemedicine.
New to this edition:
Introduction of the generalization suggestion to consolidate the time-frequency sign research, wavelet transformation, and Hermite transformation
Inclusion of renowned powerful transformation idea utilized in the processing of noisy multimedia facts in addition to
advanced multimedia information filtering ways, together with photo filtering concepts for impulse noise environment
Extended video compression algorithms
Detailed assurance of compressive sensing in multimedia functions
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Additional info for Multimedia Signals and Systems: Basic and Advanced Algorithms for Signal Processing
N À Nα; ð1:61Þ ai ¼ N ð1 À 2αÞ þ 4α : 0, otherwise: where N is even, while the parameter α takes values within the range [0,1/2]. For α ¼ 0 the standard STFT is obtained, while for α ¼ 1/2 we obtain the marginal median STFT. Larger values of α provide better reduction of heavy-tailed noise, while smaller values of α better preserve the spectral characteristics. Accordingly, the choice of α should provide a good trade-off between these two requirements. Similarly to the STFT, we can define robust forms of other time-frequency representations/distributions.
11a), while the second consists of three sinusoids of short duration and a chirp (Fig. 11b). The ideal time-frequency representations are presented as well. Using a narrow window, we achieve a good time resolution for sinusoidal signal components as shown in the second column of Fig. 11. Using a wide window, a good frequency resolution of these components is achieved, but the time resolution is significantly decreased. Notice that the time-frequency resolution of the chirp component is poor in both cases.
Some of the most commonly used windows are: Rectangular, Gaussian, Hann(ing), Hamming and Blackman, defined as follows: & 1, for jτj < T 1. Rectangular window: wðτÞ ¼ 0, otherwise 2 2 2. Gaussian window: wðτÞ ¼ eÀτ =α (it localizes signal in time although it is not time-limited). & 0:5ð1 þ cos ðπτ=T ÞÞ, for jτj < T 3. Hann(ing) window: wðτÞ ¼ 0, otherwise & Á 0:54 þ 0:46 cos ðπτ=T Þ , for jτj < T 4. Hamming window: wðτÞ ¼ 0, otherwise 5. 6 Á 0:42 þ 0:5 cos ðπτ=T Þ þ 0:08 cos ð2πτ=T Þ , for jτj < T 0, otherwise Wigner Distribution In order to improve the localization of a signal in the time-frequency domain, a number of quadratic distributions are introduced.