Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Vector Representation of Complex Numbers

Vector Representation of Complex Numbers

Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the denominator.

Downsampling

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...

Vector Algebra: Method of Components

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...

Vector Operations

Vector Operations

Vectors are physical quantities that have both magnitude and direction. The vector operations include addition, subtraction, and scalar multiplication.
A vector multiplied by a scalar value is called scalar multiplication. The result obtained is a new vector with a different magnitude. If the scalar is positive, the direction of the vector remains the same, but if it is negative, the direction of the vector is reversed. For example, the product of the mass and velocity yields the momentum.

Conservative Vector Fields

Conservative Vector Fields

A conservative vector field describes a force or field in which the work done between two points depends only on the initial and final positions. For a ball moving in Earth’s gravitational field, gravity performs work determined by the difference in height, regardless of whether the ball moves vertically or follows a curved trajectory.A vector field is conservative if it can be expressed as the gradient of a scalar potential function, f. In two dimensions, this is written...

Uniform Depth Channel Flow

Uniform Depth Channel Flow

Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...

You might also read

Related Articles

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

Sort by

Same author

Glycolysis Inhibition as a Strategy for Hepatocellular Carcinoma Treatment?

Current cancer drug targets·2018

Same author

Graph analysis of cell clusters forming vascular networks.

Royal Society open science·2018

Same author

Magnitude and Allele Frequency of Cry1F Resistance in Field Populations of the Fall Armyworm (Lepidoptera: Noctuidae) in Brazil.

Journal of economic entomology·2017

Same author

Quantitative trait locus mapping and functional genomics of an organophosphate resistance trait in the western corn rootworm, Diabrotica virgifera virgifera.

Insect molecular biology·2015

Same author

Inheritance of Cry1F resistance, cross-resistance and frequency of resistant alleles in Spodoptera frugiperda (Lepidoptera: Noctuidae).

Bulletin of entomological research·2013

Same author

Inheritance of resistance to the Cry1Ab Bacillus thuringiensis toxin in Ostrinia nubilalis (Lepidoptera: Crambidae).

Journal of economic entomology·2006

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

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

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

Published on: December 3, 2013

A very low bit rate video coder based on vector quantization.

L Corte-Real¹, A P Alves

¹Dept. de Engenharia Electrotecnica e de Computadores, Porto Univ.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1996

Summary

This study introduces a novel video coding algorithm using hybrid differential pulse-code modulation (DPCM) and vector quantization for efficient 8-16 kb/s bit rates. The method enhances motion estimation quality and achieves good signal-to-noise ratios with simpler decoder implementation.

More Related Videos

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Related Experiment Videos

Last Updated: Jul 7, 2026

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

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

Published on: December 3, 2013

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Area of Science:

Digital video compression
Image processing and computer vision
Information theory and coding

Background:

Traditional video coding methods face challenges in achieving high compression ratios at low bit rates.
Hybrid differential pulse-code modulation (DPCM) and vector quantization (VQ) offer a framework for efficient video compression.
Optimizing motion estimation and quantization is crucial for maintaining video quality at reduced bit rates.

Purpose of the Study:

To develop and evaluate a novel video coder utilizing a hybrid DPCM-vector quantization algorithm.
To enhance motion estimation quality by employing variable-size and variable-shape block segmentation.
To achieve efficient video compression at low bit rates (8-16 kb/s) with improved signal-to-noise ratios.

Main Methods:

Segmenting difference images into variable-size and variable-shape blocks for simultaneous segmentation and motion compensation.
Utilizing decimation for larger blocks to simplify vector quantization.
Applying a fuzzy classified vector quantizer (FCVQ) for highly active, small blocks.
Implementing a hybrid DPCM-vector quantization approach.

Main Results:

The proposed video coding algorithm demonstrates good performance in compressing test sequences at 8 and 16 kb/s.
Achieved good signal-to-noise ratios for both tested bit rates.
The coder implementation complexity is comparable to conventional hybrid coders.
The decoder complexity is significantly reduced compared to existing methods.

Conclusions:

The hybrid DPCM-vector quantization video coder effectively achieves high compression at low bit rates.
The approach improves motion estimation quality, particularly at object edges.
The algorithm offers a favorable trade-off between compression efficiency, video quality, and decoder complexity.