Jove
Visualize
Contact Us

Related Concept Videos

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...
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...
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...
Inverse z-Transform by Partial Fraction Expansion01:20

Inverse z-Transform by Partial Fraction Expansion

The inverse z-transform is a crucial technique for converting a function from its z-domain representation back to the time domain. One effective method for finding the inverse z-transform is the Partial Fraction Method, which involves decomposing a function into simpler fractions with distinct coefficients. These fractions correspond to known z-transform pairs, facilitating the inverse transformation process.
To begin the process, the poles of the function are identified and the function is...
Transformations of Functions III01:20

Transformations of Functions III

Transformations modify the graphical representation of a function without changing its fundamental form. One common transformation is reflection, which flips the graph across a designated axis. When the vertical coordinates of all points are multiplied by the negative one, the entire graph is mirrored over the horizontal axis. This transformation reverses the vertical orientation of peaks and troughs, akin to signal inversion in electrical systems, where a waveform is flipped, but the timing of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Complex-valued error diffusion for off-axis computer-generated holograms.

Applied optics·2010
Same author

Comparison of error diffusion methods for computer-generated holograms.

Applied optics·2010
Same author

Comparison of binary encoding schemes for electron-beam fabrication of computer generated holograms.

Applied optics·2010
Same author

Processing JPEG-compressed images and documents.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Nonexpansive pyramid for image coding using a nonlinear filterbank.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

On independent color space transformations for the compression of CMYK images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 7, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Fast downscaled inverses for images compressed with M-channel lapped transforms.

R L de Queiroz1, R Eschbach

  • 1Xerox Corp., Webster, NY.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
Summary
This summary is machine-generated.

This study introduces faster image decompression methods using partial decompression and reduced inverse transforms. These techniques simplify image recovery and reduce computational costs for various display resolutions.

Related Experiment Videos

Last Updated: Jul 7, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Area of Science:

  • Digital image processing
  • Signal processing
  • Computer vision

Background:

  • Image decompression and display involve significant computational cost, especially when rescaling.
  • Current methods for full decompression and spatial domain rescaling are resource-intensive.

Purpose of the Study:

  • To investigate efficient image decompression techniques.
  • To develop methods for faster image recovery and display at different resolutions.
  • To simplify the image synthesis process.

Main Methods:

  • Studied downscaled inverses involving partial decompression.
  • Employed reduced inverse transforms for image recovery.
  • Developed general solutions for M-channel finite impulse response (FIR) filterbanks.
  • Designed faster inverses for block and lapped transforms.

Main Results:

  • Partial decompression significantly reduces computational expense.
  • Reduced inverse transforms simplify the image synthesis process.
  • Faster inverse designs were achieved for common block and lapped transforms.

Conclusions:

  • Downscaled inverses offer an efficient alternative to full decompression and rescaling.
  • The proposed methods simplify image recovery and reduce processing costs.
  • Efficient inverse transform designs are crucial for high-resolution image display.