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Convolution Properties II01:17

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
Convolution Properties I01:20

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
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...
Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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...

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Related Experiment Video

Updated: Jun 12, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A two-channel overlapped block transform for image compression.

Jianyu Lin, Mark J T Smith

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 10, 2010
    PubMed
    Summary
    This summary is machine-generated.

    A novel two-channel overlapped block transform, derived from the Haar transform, offers efficient image compression. This new method provides high visual quality and peak signal-to-noise ratio (PSNR), competing with existing popular filters.

    Related Experiment Videos

    Last Updated: Jun 12, 2026

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    Area of Science:

    • Digital Signal Processing
    • Image Compression
    • Filter Banks

    Background:

    • Existing image coding methods often rely on complex transforms like the 5/3 and 9/7 filters.
    • Computational efficiency and boundary handling are critical challenges in image transform design.

    Discussion:

    • A new two-channel overlapped block transform is proposed, achieved by prefiltering the Haar transform using plane rotations.
    • This transform is mathematically equivalent to a periodically time-varying (PTV) filter bank, offering unique properties.
    • The method achieves high computational efficiency through simple shifts and adds, reducing arithmetic complexity.

    Key Insights:

    • The proposed transform simplifies symmetric extension at image boundaries, even with asymmetric filters.
    • It demonstrates high performance in image compression, measured by visual quality and peak signal-to-noise ratio (PSNR).
    • The transform offers a competitive alternative to widely used 5/3 and 9/7 filters in subband/wavelet image coding.

    Outlook:

    • Further research could explore the application of this PTV filter bank in other signal processing domains.
    • Optimization of the rotation angle could potentially enhance compression performance further.
    • Integration into existing image and video coding standards may be explored.