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

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

332
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
332
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

66
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
66
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

76
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...
76
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

91
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
91
Upsampling01:22

Upsampling

238
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...
238

You might also read

Related Articles

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

Sort by
Same author

Hybrid Compression Method for Trained 3D Gaussian Splatting Models Based on VQ and HEVC.

Sensors (Basel, Switzerland)·2026
Same author

Clinical application of the Panbio™ COVID-19 Ag rapid test device and SSf-COVID19 kit for the detection of SARS-CoV-2 infection.

BMC research notes·2022
Same author

Incomplete Region Estimation and Restoration of 3D Point Cloud Human Face Datasets.

Sensors (Basel, Switzerland)·2022
Same author

Smart Bioinspired Actuators: Crawling, Linear, and Bending Motions through a Multilayer Design.

ACS applied materials & interfaces·2021

Related Experiment Video

Updated: Jul 9, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K

Coarse-to-Fine Network-Based Intra Prediction in Versatile Video Coding.

Dohyeon Park1, Gihwa Moon1, Byung Tae Oh1

  • 1Department of Electronics and Information Engineering, Korea Aerospace University, Goyang 10540, Republic of Korea.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
Summary

This study introduces a novel neural network intra prediction method to improve Versatile Video Coding (VVC). The coarse-to-fine network architecture enhances prediction accuracy for intricate images, achieving significant BD-rate savings.

Keywords:
Neural Network-Based Video Coding (NNVC)Versatile Video Coding (VVC)intra predictionvideo codingvideo compression

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

406
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

Related Experiment Videos

Last Updated: Jul 9, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

406
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

Area of Science:

  • Computer Vision
  • Digital Signal Processing
  • Machine Learning for Video Coding

Background:

  • The Versatile Video Coding (VVC) standard is an active area of research for future video compression.
  • Traditional intra prediction methods struggle with complex images lacking spatial redundancy.
  • Neural network-based approaches show promise for overcoming these limitations.

Purpose of the Study:

  • To enhance Versatile Video Coding (VVC) intra prediction performance using a novel neural network architecture.
  • To address the limitations of traditional intra prediction in complex visual data.
  • To improve coding efficiency for intricate images with limited spatial redundancy.

Main Methods:

  • Development of a coarse-to-fine neural network architecture combining convolutional and fully connected layers.
  • Coarse networks adapt prediction based on reference sample conditions and positions.
  • Fine networks refine predictions considering adjacent sample continuity and enable upscaling for unsupported block sizes.

Main Results:

  • Integration of the proposed network as an additional intra prediction mode within the VVC Test Model (VTM).
  • Achieved an average Bjøntegaard delta-rate (BD-rate) saving of 1.31% for the luma component against VTM 11.0.
  • Demonstrated an average BD-rate saving of 0.47% compared to previous related works.

Conclusions:

  • The proposed coarse-to-fine neural network intra prediction method effectively enhances VVC performance.
  • This approach offers a viable solution for improving video coding standards, particularly for challenging image content.
  • The method provides measurable coding gains, indicating its potential for future video compression technologies.