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

Blind Procedures02:07

Blind Procedures

13.6K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
13.6K
Blinding01:11

Blinding

4.0K
Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
4.0K
Quality Control01:05

Quality Control

2.7K
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
2.7K
Quality Assurance01:19

Quality Assurance

2.4K
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
2.4K
Quality of Water01:19

Quality of Water

573
In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
573
Pulse amplitude and quality01:17

Pulse amplitude and quality

3.2K
Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
A weak or absent pulse may indicate reduced cardiac output or poor left ventricular contraction, which can be signs of cardiovascular dysfunction or...
3.2K

You might also read

Related Articles

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

Sort by
Same author

An AI-driven, wearable, conformal ring system for real-time and user-independent sign language interpretation.

Science advances·2026
Same author

Selecting serum-free hepatocyte cryopreservation stage and storage temperature for the application of an "off-the-shelf" bioartificial liver system.

Scientific reports·2024
Same author

Author Correction: Meiotic protein SYCP2 confers resistance to DNA-damaging agents through R-loop-mediated DNA repair.

Nature communications·2024
Same author

Optimal Dietary Intake of Riboflavin Associated with Lower Risk of Cervical Cancer in Korea: Korean National Health and Nutrition Examination Survey 2010-2021.

Life (Basel, Switzerland)·2024
Same author

<i>Rbpj</i> deletion in hepatic progenitor cells attenuates endothelial responses and fibrosis in DDC-fed mice.

bioRxiv : the preprint server for biology·2024
Same author

Characterization of Ceftriaxone-Resistant <i>Haemophilus influenzae</i> Among Korean Children.

Journal of Korean medical science·2024
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Feb 8, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K

Deep CNN-Based Blind Image Quality Predictor.

Jongyoo Kim, Anh-Duc Nguyen, Sanghoon Lee

    IEEE Transactions on Neural Networks and Learning Systems
    |July 12, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel deep convolutional neural network (CNN) framework for no-reference image quality assessment (NR-IQA). The method achieves state-of-the-art accuracy by training in two stages and incorporating a reliability map.

    More Related Videos

    In vivo Imaging of Deep Cortical Layers using a Microprism
    09:45

    In vivo Imaging of Deep Cortical Layers using a Microprism

    Published on: August 27, 2009

    11.9K
    Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
    09:55

    Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

    Published on: January 5, 2024

    1.9K

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    10.0K
    In vivo Imaging of Deep Cortical Layers using a Microprism
    09:45

    In vivo Imaging of Deep Cortical Layers using a Microprism

    Published on: August 27, 2009

    11.9K
    Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
    09:55

    Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

    Published on: January 5, 2024

    1.9K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Convolutional Neural Networks (CNNs) excel in computer vision tasks.
    • No-reference image quality assessment (NR-IQA) faces challenges due to limited training data.

    Purpose of the Study:

    • To propose a CNN-based framework for NR-IQA that overcomes data limitations.
    • To develop a method that achieves state-of-the-art accuracy in image quality assessment.

    Main Methods:

    • A two-stage training approach for the deep image quality assessor (DIQA) framework.
    • Predicting objective error maps and then subjective scores using CNNs.
    • Introducing a reliability map to address inaccuracies in homogeneous regions and incorporating handcrafted features.

    Main Results:

    • The proposed DIQA framework demonstrates state-of-the-art accuracy across various image databases.
    • Visualizations of perceptual error maps offer insights into the CNN model's learning process.

    Conclusions:

    • The DIQA framework effectively addresses data scarcity in NR-IQA.
    • The two-stage training and reliability map contribute to high accuracy in image quality assessment.