Jove
Visualize
Contact Us
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 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...
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
Relation of DFT to z-Transform01:20

Relation of DFT to z-Transform

The Discrete Fourier Transform (DFT) is a crucial tool for analyzing the frequency content of discrete-time signals. It converts a sequence of N samples from the time domain into its corresponding sequence in the frequency domain, where each sample represents a specific frequency component.
To understand how the DFT works, it's helpful to consider the z-transform, which is a method for representing discrete sequences in the complex frequency domain. The z-transform involves summing the terms of...
Discrete Fourier Transform01:15

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
Properties of DTFT I01:24

Properties of DTFT I

In signal processing, Discrete-Time Fourier Transforms (DTFTs) play a critical role in analyzing discrete-time signals in the frequency domain. Various properties of the DTFTs such as linearity, time-shifting, frequency-shifting, time reversal, conjugation, and time scaling help understand and manipulate these signals for different applications.
The linearity property of DTFTs is fundamental. If two discrete-time signals are multiplied by constants a and b respectively, and then combined to...

You might also read

Related Articles

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

Sort by
Same author

The Poised Cannula Technique Reduces the Stereotactic Error of the Fiducial-Less Frameless DBS Procedure.

Stereotactic and functional neurosurgery·2021
Same author

A Comparative Study of Fiducial-Based and Fiducial-Less Registration Utilizing the O-Arm.

Stereotactic and functional neurosurgery·2019
Same author

In reply.

Neurosurgery·2012
Same author

A quantitative assessment of the accuracy and reliability of O-arm images for deep brain stimulation surgery.

Neurosurgery·2012
Same author

4D Cone-beam CT reconstruction using a motion model based on principal component analysis.

Medical physics·2011
Same author

Reconstruction of a cone-beam CT image via forward iterative projection matching.

Medical physics·2011

Related Experiment Video

Updated: Jul 7, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

The quantized DCT and its application to DCT-based video coding.

Alen Docef1, Faouzi Kossentini, Nguuyen-Phi Khanh

  • 1Dept. of Electr. and Comput. Eng., British Columbia Univ., Vancouver, BC, Canada. alen@ece.ubc.ca

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient method for video encoding by jointly implementing the discrete cosine transform (DCT) and quantization. This approach reduces computational complexity for DCT-based video encoders like MPEG-2 and H.263.

Related Experiment Videos

Last Updated: Jul 7, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Area of Science:

  • Digital Signal Processing
  • Video Compression Technology
  • Computer Vision Algorithms

Background:

  • The discrete cosine transform (DCT) and coefficient quantization are computationally intensive in video encoding.
  • Existing DCT-based video encoders face significant processing demands.

Purpose of the Study:

  • To develop an efficient joint implementation of the DCT and quantization steps.
  • To reduce computational complexity in DCT-based video encoding.

Main Methods:

  • Embedding quantization within the DCT computation.
  • Proposing a multiplierless integer implementation of the quantized DCT (QDCT) using shift and add operations.
  • Implementing early termination of DCT computations based on intermediate results.

Main Results:

  • Achieved computational savings by exchanging DCT precision for reduced complexity.
  • Developed a sequence of multiplierless QDCT algorithms with varying precision and computation levels.
  • Demonstrated effectiveness in high-quality MPEG-2 and low bit rate H.263 video encoding.

Conclusions:

  • The proposed joint implementation significantly reduces computational load for video encoders.
  • Multiplierless QDCT with early termination offers an efficient alternative for video compression.
  • The method is applicable to various video encoding standards.