Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Upsampling

Upsampling

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...

You might also read

Related Articles

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

Sort by

Same author

A Timestamp-Independent Haptic-Visual Synchronization Method for Haptic-Based Interaction System.

Sensors (Basel, Switzerland)·2022

Same author

[Effects of a glucocorticoid on development of kidney deficiency syndrome in a rat model of asthma].

Zhong xi yi jie he xue bao = Journal of Chinese integrative medicine·2010

Same author

[A case of respiratory epithelial adenomatoid hamartoma in nasal cavity.].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery·2010

Same author

[Construct cosmid libraries by isolating large genomic DNA fragments from Monascus ruber].

Wei sheng wu xue bao = Acta microbiologica Sinica·2010

Same author

Retinal tissue engineering using mouse retinal progenitor cells and a novel biodegradable, thin-film poly(e-caprolactone) nanowire scaffold.

Journal of ocular biology, diseases, and informatics·2010

Same author

[Correlation between MR diffusion weighted imaging with malignant degree of rabbit liver VX2 tumor models].

Zhonghua yi xue za zhi·2010

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

Search research articles

Related Experiment Video

Updated: Oct 17, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Compressed Video Quality Index Based on Saliency-Aware Artifact Detection.

Liqun Lin¹, Jing Yang¹, Zheng Wang¹

¹Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350002, China.

Sensors (Basel, Switzerland)

|October 13, 2021

Summary

This summary is machine-generated.

This study introduces the Saliency-Aware Artifact Measurement (SAAM) metric to objectively assess video quality degradation from compression artifacts. SAAM effectively predicts perceived video quality without reference, outperforming existing methods.

Keywords:

Dense Convolutional Network (DenseNet)perceivable encoding artifacts saliency detection video quality assessment

More Related Videos

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

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

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Oct 17, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

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

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

Area of Science:

Computer Vision
Signal Processing
Multimedia Engineering

Background:

Video compression reduces bitrate but can introduce perceivable encoding artifacts (PEAs) like blurring and blocking.
These artifacts degrade the viewer's perceived video quality, necessitating objective measurement methods.
Existing video quality metrics may not accurately capture artifact impact, especially in reference-free scenarios.

Purpose of the Study:

To propose a novel, reference-free objective video quality metric named Saliency-Aware Artifact Measurement (SAAM).
To accurately detect and quantify perceivable encoding artifacts (PEAs) in compressed videos.
To evaluate SAAM's performance against established video quality metrics.

Main Methods:

Video saliency detection is employed to identify regions of interest.
Identified regions are segmented into image patches for detailed analysis.
A data-driven model evaluates artifact intensity within patches.
Support Vector Regression (SVR) fuses patch-level assessments into a single quality score.

Main Results:

The SAAM metric demonstrated promising performance in predicting video quality.
SAAM showed superior quality prediction compared to most existing compressed video quality evaluation models.
Experiments were conducted on multiple public databases (LIVE, CSIQ, IVP, FERIT-RTRK).

Conclusions:

SAAM offers an effective approach for objective, reference-free video quality assessment.
The saliency-aware methodology enhances the accuracy of artifact detection and measurement.
SAAM represents a significant advancement in evaluating video quality degraded by compression.