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

Parallel Processing01:20

Parallel Processing

205
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
205
Block Diagram Reduction01:22

Block Diagram Reduction

265
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
265
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

311
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
311
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

116
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...
116
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

126
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...
126
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

270
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
270

You might also read

Related Articles

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

Sort by
Same author

Deep-Based Film Grain Removal and Synthesis.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023
Same author

Predictive Uncertainty Estimation for Camouflaged Object Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023
Same author

A high-performance two-dimensional transform architecture of variable block sizes for the VVC standard.

Journal of real-time image processing·2022
Same author

Tunable VVC Frame Partitioning based on Lightweight Machine Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2019
Same author

Optimal Adaptive Quantization Based on Temporal Distortion Propagation Model for HEVC.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2019
Same journal

LEARNING COMPACT DNN MODELS FOR BEHAVIOR PREDICTION FROM NEURAL ACTIVITY OF CALCIUM IMAGING.

Journal of signal processing systems·2024
Same journal

Signal Processing Techniques for 6G.

Journal of signal processing systems·2023
Same journal

Fine-tuning-based Transfer Learning for Characterization of Adeno-Associated Virus.

Journal of signal processing systems·2023
Same journal

LSTM Network Integrated with Particle Filter for Predicting the Bus Passenger Traffic.

Journal of signal processing systems·2023
Same journal

Prediction of Bus Passenger Traffic using Gaussian Process Regression.

Journal of signal processing systems·2022
Same journal

Towards real-time 3D visualization with multiview RGB camera array.

Journal of signal processing systems·2022
See all related articles

Related Experiment Video

Updated: Aug 24, 2025

Characterization of Anisotropic Leaky Mode Modulators for Holovideo
09:36

Characterization of Anisotropic Leaky Mode Modulators for Holovideo

Published on: March 19, 2016

8.0K

OpenVVC Decoder Parameterized and Interfaced Synchronous Dataflow (PiSDF) Model: Tile Based Parallelism.

Naouel Haggui1,2, Wassim Hamidouche1, Fatma Belghith2

  • 1Univ Rennes, INSA Rennes, CNRS, IETR - UMR 6164, Rennes, 20 Avenue des Buttes de Coesmes, Rennes, 35700 France.

Journal of Signal Processing Systems
|October 21, 2022
PubMed
Summary
This summary is machine-generated.

The new Versatile Video Coding (VVC) standard offers significant gains but requires more processing power. A data flow modeling approach using PREESM software optimizes VVC decoding on multicore processors, achieving faster performance.

Keywords:
Dataflow modelingOpenVVCPiSDFTilesVVC

More Related Videos

A Rapid Method for Modeling a Variable Cycle Engine
04:58

A Rapid Method for Modeling a Variable Cycle Engine

Published on: August 13, 2019

7.6K
Human Fear Conditioning Conducted in Full Immersion 3-Dimensional Virtual Reality
10:38

Human Fear Conditioning Conducted in Full Immersion 3-Dimensional Virtual Reality

Published on: August 9, 2010

21.0K

Related Experiment Videos

Last Updated: Aug 24, 2025

Characterization of Anisotropic Leaky Mode Modulators for Holovideo
09:36

Characterization of Anisotropic Leaky Mode Modulators for Holovideo

Published on: March 19, 2016

8.0K
A Rapid Method for Modeling a Variable Cycle Engine
04:58

A Rapid Method for Modeling a Variable Cycle Engine

Published on: August 13, 2019

7.6K
Human Fear Conditioning Conducted in Full Immersion 3-Dimensional Virtual Reality
10:38

Human Fear Conditioning Conducted in Full Immersion 3-Dimensional Virtual Reality

Published on: August 9, 2010

21.0K

Area of Science:

  • Computer Engineering
  • Digital Signal Processing
  • Software Engineering

Background:

  • The Versatile Video Coding (VVC) standard provides substantial coding efficiency improvements over HEVC, but with increased computational complexity.
  • The demands of VVC and higher video resolutions necessitate multicore architectures for real-time processing.
  • Efficient design methodologies are crucial for exploring heterogeneous multicore architectures and optimizing code generation.

Purpose of the Study:

  • To present a data flow modeling methodology for designing and optimizing VVC decoders on multicore architectures.
  • To demonstrate the application of the PREESM software and Parameterized and Interfaced Synchronous Dataflow (PiSDF) for VVC decoder modeling.
  • To leverage tile-based parallelism within the OpenVVC decoder framework.

Main Methods:

  • Modeling a complete VVC decoder using the PiSDF data flow model within the PREESM software.
  • Implementing tile-based parallelism strategies from the OpenVVC decoder.
  • Evaluating the performance of the PiSDF-based VVC decoder on a 24-core processor.

Main Results:

  • The PiSDF-based VVC decoder achieved a speedup of up to 11% compared to the C/C++ handwritten OpenVVC decoder on a 24-core processor.
  • The proposed data flow model demonstrated superior performance over existing RVC-CAL model-based dataflow decoders.
  • The methodology facilitates efficient exploration of heterogeneous multicore architectures for video coding.

Conclusions:

  • Data flow modeling with PREESM and PiSDF offers an effective approach for optimizing VVC decoder performance on multicore systems.
  • The developed VVC decoder model significantly enhances processing speed and outperforms previous dataflow implementations.
  • This methodology accelerates the time-to-market for VVC-based real-time video applications.