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

Downsampling01:20

Downsampling

745
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...
745
Upsampling01:22

Upsampling

682
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...
682
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

816
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...
816
Sampling Methods: Overview01:06

Sampling Methods: Overview

3.7K
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...
3.7K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

813
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
813
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

388
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
388

You might also read

Related Articles

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

Sort by
Same author

A Multi-Module Fusion Framework for Restoring Human and Machine Vision Quality in Compressed Video.

Sensors (Basel, Switzerland)·2026
Same author

On Demand Secure Scalable Video Streaming for Both Human and Machine Applications.

Sensors (Basel, Switzerland)·2026
Same author

Image/Video Coding and Processing Techniques for Intelligent Sensor Nodes.

Sensors (Basel, Switzerland)·2024
Same author

Computer-Vision-Oriented Adaptive Sampling in Compressive Sensing.

Sensors (Basel, Switzerland)·2024
Same author

A Highly Pipelined and Highly Parallel VLSI Architecture of CABAC Encoder for UHDTV Applications.

Sensors (Basel, Switzerland)·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Mar 15, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

16.1K

Leveraging Temporal Down-Sampling Structure and Spatio-Temporal Fusion for Efficient Video Coding.

Keren He1, Yufei Gao1, Qi Wang1

  • 1Graduate School of Science and Engineering, Hosei University, Tokyo 184-8584, Japan.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel temporal down-sampling video compression system. It preserves key frames and enhances intermediate ones, significantly improving compression efficiency and reducing data rates compared to VVC and HEVC standards.

Keywords:
deep learninglow-bitratevideo codingvideo enhancement

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

852

Related Experiment Videos

Last Updated: Mar 15, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

16.1K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

852

Area of Science:

  • Computer Vision
  • Image and Video Processing
  • Data Compression

Background:

  • Modern video compression relies on down-sampling, but current methods overlook spatial-temporal redundancy, leading to information loss.
  • Uniform frame down-sampling in existing systems degrades compression efficiency and video quality.

Purpose of the Study:

  • To propose an advanced temporal down-sampling video compression system that addresses limitations of current methods.
  • To enhance compression efficiency by preserving key frames and intelligently down-sampling intermediate frames.

Main Methods:

  • A novel temporal down-sampling strategy is introduced, selectively down-sampling intermediate frames while retaining high-quality key frames.
  • A frame-recurrent enhancement mechanism is employed at the decoder to leverage temporal redundancy.
  • A Multi-scale Temporal-Spatial Attention (MTSA) module, comprising Multi-Temporal Attention (MTA) and Pyramid Spatial Attention (PSA), is designed for the enhancement stage.

Main Results:

  • The proposed system achieves consistent BD-rate reductions across various configurations (All-Intra, Low-Delay-P, Random Access).
  • Significant BD-rate reductions of 14% to 39% for I, P, and B frames were observed compared to the Versatile Video Coding (VVC) standard.
  • The approach demonstrates superior performance over existing methods, including those based on the High Efficiency Video Coding (HEVC) standard.

Conclusions:

  • The proposed temporal down-sampling system effectively utilizes spatial and temporal redundancy for improved video compression.
  • The MTSA module plays a crucial role in enhancing frame quality by modeling temporal correlations and spatial saliency.
  • This method offers a promising direction for next-generation video compression, outperforming current state-of-the-art standards.