Related Experiment Videos

Image compression based on fuzzy algorithms for learning vector quantization and wavelet image decomposition

N B Karayiannis, P I Pai, N Zervos

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 16, 2008
    PubMed

    Related Concept Videos

    Downsampling01:20

    Downsampling

    When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
    The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
    Deconvolution01:20

    Deconvolution

    Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
    Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
    Reconstruction of Signal using Interpolation01:10

    Reconstruction of Signal using Interpolation

    Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
    Convolution: Math, Graphics, and Discrete Signals01:24

    Convolution: Math, Graphics, and Discrete Signals

    In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
    To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...