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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

301
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...
301
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.5K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
1.5K
Acceleration Vectors01:30

Acceleration Vectors

19.8K
In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h...
19.8K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

390
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...
390
Sampling Methods: Overview01:06

Sampling Methods: Overview

1.1K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
1.1K
Rapidly Varying Flow01:24

Rapidly Varying Flow

274
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
274

You might also read

Related Articles

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

Sort by
Same author

Dual-Function Equol Molecule Suppresses Superoxide-Mediated Degradation in Perovskite Solar Cells.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Nutritional prehabilitation for robot-assisted radical prostatectomy: A proposed clinical framework integrating body composition analysis.

Clinical nutrition ESPEN·2026
Same author

Competitive Binding of UBA52 and HOPX Modulates β-catenin Stability in Colorectal Cancer in the Context of High-Iron Intake.

International journal of biological sciences·2026
Same author

Closing the loop: AI-driven integration of multi-omics and phenomics for systematic resilient crop engineering.

Biotechnology advances·2026
Same author

Effects of Tai Chi combined with transcranial direct current stimulation on pain in knee osteoarthritis: a randomized controlled neuroimaging trial.

BMC medicine·2026
Same author

Preference differences of different styles of oil paintings in various interior environments based on the PAD emotional state model and EEG.

Frontiers in psychology·2026

Related Experiment Video

Updated: Nov 28, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.4K

Fast Sample Adaptive Offset Jointly Based on HOG Features and Depth Information for VVC in Visual Sensor Networks.

Ruyan Wang1,2,3, Liuwei Tang1,2,3, Tong Tang1,2,3

  • 1School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Sensors (Basel, Switzerland)
|December 1, 2020
PubMed
Summary

This study introduces an efficient method to speed up video compression in Visual Sensor Networks (VSNs). By simplifying sample adaptive offset (SAO) encoding using HOG features and depth information, it significantly reduces computational complexity with minimal impact on video quality.

Keywords:
depthedge offsetsample adaptive offsetversatile video codingvisual sensor networks

More Related Videos

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

8.0K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.4K

Related Experiment Videos

Last Updated: Nov 28, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.4K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

8.0K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.4K

Area of Science:

  • Computer Vision
  • Video Compression
  • Network Engineering

Background:

  • Visual Sensor Networks (VSNs) generate massive data, posing transmission and compression challenges.
  • Versatile Video Coding (VVC) offers high compression but suffers from significant computational complexity.
  • Reducing VVC encoder complexity is crucial for VSN applications.

Purpose of the Study:

  • To propose an accelerated Sample Adaptive Offset (SAO) method for VVC in VSNs.
  • To reduce the computational complexity of VVC encoding without substantial quality loss.
  • To enable efficient deployment of VVC in resource-constrained VSN environments.

Main Methods:

  • Jointly utilized Histogram of Oriented Gradient (HOG) features and depth information for SAO acceleration.
  • Simplified offset mode selection (BO/EO) using Coding Tree Unit (CTU) partition depth.
  • Optimized directional pattern selection for EO mode via HOG features and Support Vector Machine (SVM).

Main Results:

  • Achieved an average of 67.79% reduction in SAO encoding time.
  • Introduced only 0.52% BD-rate degradation, indicating minimal quality loss.
  • Demonstrated significant computational savings compared to the VVC reference software (VTM 5.0).

Conclusions:

  • The proposed SAO acceleration method effectively reduces VVC encoding complexity in VSNs.
  • The approach balances computational efficiency with high video compression performance.
  • This method facilitates the practical application of advanced video coding standards in VSNs.