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

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

730
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...
730
What is Variation?01:14

What is Variation?

18.5K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
18.5K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

10.0K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
10.0K
Variation01:19

Variation

8.0K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.0K
Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

6.8K
Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
The recognition sites for Cre recombinase called LoxP...
6.8K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K

You might also read

Related Articles

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

Sort by
Same author

[Identification of Placenta hominis and its adulterants using COI barcode].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2014
Same author

Two new species of Austrophthiracarus (Acari: Oribatida: phthiracaridae) from New Zealand.

Zootaxa·2014
Same author

The genus Notophthiracarus of New Zealand (Acari: Oribatida: Phthiracaridae): three new species and a key to 24 described species.

Zootaxa·2014
Same author

MHC class II restricted innate-like double negative T cells contribute to optimal primary and secondary immunity to Leishmania major.

PLoS pathogens·2014
Same author

Hepatic perfusion parameters of contrast-enhanced ultrasonography correlate with the severity of chronic liver disease.

Ultrasound in medicine & biology·2014
Same author

Dietary accumulation of tetrabromobisphenol A and its effects on the scallop Chlamys farreri.

Comparative biochemistry and physiology. Toxicology & pharmacology : CBP·2014

Related Experiment Video

Updated: Feb 2, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.9K

One-for-All: Grouped Variation Network-Based Fractional Interpolation in Video Coding.

Jiaying Liu, Sifeng Xia, Wenhan Yang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 27, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel deep learning method for fractional interpolation in video coding, improving motion compensation. The grouped variation convolutional neural network (GVCNN) efficiently generates all sub-pixel positions for various quantization parameters, saving bits.

    More Related Videos

    Using the GELFREE 8100 Fractionation System for Molecular Weight-Based Fractionation with Liquid Phase Recovery
    07:57

    Using the GELFREE 8100 Fractionation System for Molecular Weight-Based Fractionation with Liquid Phase Recovery

    Published on: December 3, 2009

    17.0K
    Isolation of High-density Lipoproteins for Non-coding Small RNA Quantification
    10:39

    Isolation of High-density Lipoproteins for Non-coding Small RNA Quantification

    Published on: November 28, 2016

    11.8K

    Related Experiment Videos

    Last Updated: Feb 2, 2026

    Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
    09:49

    Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

    Published on: September 25, 2021

    4.9K
    Using the GELFREE 8100 Fractionation System for Molecular Weight-Based Fractionation with Liquid Phase Recovery
    07:57

    Using the GELFREE 8100 Fractionation System for Molecular Weight-Based Fractionation with Liquid Phase Recovery

    Published on: December 3, 2009

    17.0K
    Isolation of High-density Lipoproteins for Non-coding Small RNA Quantification
    10:39

    Isolation of High-density Lipoproteins for Non-coding Small RNA Quantification

    Published on: November 28, 2016

    11.8K

    Area of Science:

    • Computer Vision
    • Digital Signal Processing
    • Video Compression

    Background:

    • Fractional interpolation is crucial for sub-pixel motion compensation in video coding.
    • Traditional methods struggle with discontinuous regions, while deep learning approaches have limitations regarding quantization parameters and sub-pixel coverage.

    Purpose of the Study:

    • To develop a versatile deep learning-based fractional interpolation method for video coding.
    • To address the limitations of existing methods by handling multiple quantization parameters and generating all sub-pixel positions.

    Main Methods:

    • A grouped variation convolutional neural network (GVCNN) is proposed for fractional interpolation.
    • The network predicts variations between integer-position pixels and sub-pixels for enhanced expressiveness.
    • Training data incorporates simulated video coding conditions, including blurring and reconstruction errors.

    Main Results:

    • The GVCNN method effectively handles video frames with different quantization parameters (QPs).
    • It generates all sub-pixel positions at a single sub-pixel level.
    • Experimental results show an average bit saving of 2.2% compared to HEVC, reaching up to 5.2% in specific configurations.

    Conclusions:

    • The proposed GVCNN offers an efficient and expressive solution for fractional interpolation in video coding.
    • It outperforms traditional and existing deep learning methods by providing a unified approach for various QPs and sub-pixel positions.
    • The method demonstrates practical efficiency and significant bit-saving potential in video compression.