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

Ogive Graph01:07

Ogive Graph

6.6K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.6K
Graphing Antiderivatives01:30

Graphing Antiderivatives

33
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
33
Bar Graph01:07

Bar Graph

21.4K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
21.4K
Time-Series Graph00:54

Time-Series Graph

5.0K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.0K
Multiple Bar Graph01:07

Multiple Bar Graph

8.9K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
8.9K
Bacterial Transformation01:33

Bacterial Transformation

59.5K
In 1928, bacteriologist Frederick Griffith worked on a vaccine for pneumonia, which is caused by Streptococcus pneumoniae bacteria. Griffith studied two pneumonia strains in mice: one pathogenic and one non-pathogenic. Only the pathogenic strain killed host mice.
Griffith made an unexpected discovery when he killed the pathogenic strain and mixed its remains with the live, non-pathogenic strain. Not only did the mixture kill host mice, but it also contained living pathogenic bacteria that...
59.5K

You might also read

Related Articles

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

Sort by
Same author

Future cardiovascular events prediction from invasive coronary angiography: A graph representation learning perspective.

Medical image analysis·2026
Same author

Physics-informed model of epileptic seizure dynamics.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

AI-Driven multi-view learning from CCTA for myocardial infarction diagnosis.

The international journal of cardiovascular imaging·2025
Same author

Hierarchical Spherical CNNs With Lifting-Based Adaptive Wavelets for Pooling and Unpooling.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Decoding the interactions and functions of non-coding RNA with artificial intelligence.

Nature reviews. Molecular cell biology·2025
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Jan 21, 2026

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression
07:00

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Published on: May 7, 2019

9.3K

Graph Transform Optimization with Application to Image Compression.

Giulia Fracastoro, Dorina Thanou, Pascal Frossard

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 13, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel graph-based transform for signal compression, optimizing rate-distortion performance. The new method outperforms traditional transforms like DCT in image coding applications.

    More Related Videos

    Sequential Application of Glass Coverslips to Assess the Compressive Stiffness of the Mouse Lens: Strain and Morphometric Analyses
    07:56

    Sequential Application of Glass Coverslips to Assess the Compressive Stiffness of the Mouse Lens: Strain and Morphometric Analyses

    Published on: May 3, 2016

    7.7K
    Wideband Optical Detector of Ultrasound for Medical Imaging Applications
    08:21

    Wideband Optical Detector of Ultrasound for Medical Imaging Applications

    Published on: May 11, 2014

    11.7K

    Related Experiment Videos

    Last Updated: Jan 21, 2026

    Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression
    07:00

    Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

    Published on: May 7, 2019

    9.3K
    Sequential Application of Glass Coverslips to Assess the Compressive Stiffness of the Mouse Lens: Strain and Morphometric Analyses
    07:56

    Sequential Application of Glass Coverslips to Assess the Compressive Stiffness of the Mouse Lens: Strain and Morphometric Analyses

    Published on: May 3, 2016

    7.7K
    Wideband Optical Detector of Ultrasound for Medical Imaging Applications
    08:21

    Wideband Optical Detector of Ultrasound for Medical Imaging Applications

    Published on: May 11, 2014

    11.7K

    Area of Science:

    • Signal Processing
    • Graph Theory
    • Information Theory

    Background:

    • Traditional transforms like DCT have limitations in signal compression.
    • Graph-based methods offer potential for improved rate-distortion performance.
    • Efficiently representing graph topology is crucial for graph-based transforms.

    Purpose of the Study:

    • To propose a novel graph-based transform for signal compression.
    • To develop a graph estimation algorithm optimizing rate-distortion.
    • To demonstrate the framework's applicability to image coding.

    Main Methods:

    • Designed a graph structure optimizing rate-distortion performance.
    • Introduced a novel graph estimation algorithm considering signal and topology coding.
    • Treated graph edge weights as a signal on the dual graph for coding.
    • Formulated a convex optimization problem for efficient transform coding.

    Main Results:

    • The proposed graph-based transform outperforms DCT for natural and piecewise smooth images.
    • Achieved competitive results in depth map coding compared to specialized methods.
    • Demonstrated the general framework's effectiveness across different signal types.

    Conclusions:

    • The novel graph-based transform provides an efficient signal compression strategy.
    • The method offers superior performance over classical transforms in image coding.
    • The framework is versatile and adaptable for various signal processing tasks.