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

Upsampling01:22

Upsampling

363
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...
363
Deconvolution01:20

Deconvolution

322
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
322
Downsampling01:20

Downsampling

321
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...
321
Reducing Line Loss01:18

Reducing Line Loss

222
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
222
Differential Leveling01:12

Differential Leveling

403
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
403
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

162
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...
162

You might also read

Related Articles

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

Sort by
Same author

Non-Invasive Techniques for Early Alzheimer's Disease Detection: A Survey.

Studies in health technology and informatics·2026
Same author

SmartDetector: a valid and affordable AI-based markerless motion capture system for psychological experiments.

Cognitive processing·2026
Same author

Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches.

The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale·2022
Same author

VVC In-Loop Filtering Based on Deep Convolutional Neural Network.

Computational intelligence and neuroscience·2021
Same author

Alzheimer's disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2015
Same journal

Supporting human-agent communication for explainable planning in spatial-temporal planning problems.

Neural computing & applications·2026
Same journal

Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition.

Neural computing & applications·2026
Same journal

Sequential pattern transformer (SPT): a generative and interpretable framework for predicting disease trajectories.

Neural computing & applications·2026
Same journal

Balancing misclassification errors in image-based inference using problem domain semantics and a nested cascade architecture.

Neural computing & applications·2025
Same journal

Deep multi-objective reinforcement learning for utility-based infrastructural maintenance optimization.

Neural computing & applications·2025
Same journal

A fairness scale for real-time recidivism forecasts using a national database of convicted offenders.

Neural computing & applications·2025
See all related articles

Related Experiment Videos

Deep learning-based video quality enhancement for the new versatile video coding.

Soulef Bouaafia1, Randa Khemiri1,2, Seifeddine Messaoud1

  • 1Laboratory of Electronics and Microelectronics, Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia.

Neural Computing & Applications
|September 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel AI technique for enhancing video quality in multimedia IoT systems. The new method improves visual quality and user experience by reducing compression artifacts in Versatile Video Coding.

Keywords:
Artificial intelligenceMultimedia IoTVVCVideo coding

Related Experiment Videos

Area of Science:

  • Multimedia Internet of Things (M-IoT)
  • Artificial Intelligence (AI)
  • Video Coding Standards

Background:

  • M-IoT generates massive multimedia data, requiring high video quality for services like surveillance and streaming.
  • Versatile Video Coding (VVC) offers significant coding efficiency improvements over HEVC.
  • Existing AI-based filtering methods have limitations in addressing compression artifacts.

Purpose of the Study:

  • To enhance video quality and user Quality of Experience (QoE) in VVC-compressed multimedia.
  • To introduce a deep learning-based in-loop filtering technique for VVC.
  • To improve the efficiency of video compression and reconstruction.

Main Methods:

  • Developed a wide-activated squeeze-and-excitation deep convolutional neural network (WSE-DCNN).
  • Replaced conventional in-loop filtering in VVC with the proposed WSE-DCNN model.
  • Evaluated the technique's performance in reducing compression artifacts and improving visual quality.

Main Results:

  • The WSE-DCNN in-loop filtering achieved significant BD rate reduction for luma and chroma components.
  • Demonstrated efficient performance in terms of Rate-Distortion (RD) cost compared to traditional CNN methods.
  • Successfully eliminated compression artifacts, leading to improved visual fidelity.

Conclusions:

  • The proposed WSE-DCNN-based in-loop filtering is an effective method for VVC video quality enhancement.
  • This technique significantly improves user QoE in multimedia IoT applications.
  • The WSE-DCNN framework offers a promising advancement in AI-driven video compression.