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

You might also read

Related Articles

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

Sort by
Same author

[Arthroscopic reconstruction of anterior cruciate ligament with preservation of the remnant bundle].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2013
Same author

[Anterior cruciate ligament reconstruction with tendon graft enveloped by preserved remnants].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2013
Same author

Genetic and molecular biological characterization of two homologous cheR genes from Leptospira interrogans.

Acta biochimica et biophysica Sinica·2013
Same author

Upregulation of glycoprotein nonmetastatic B by colony-stimulating factor-1 and epithelial cell adhesion molecule in hepatocellular carcinoma cells.

Oncology research·2013
Same author

Effect of implantation of biodegradable magnesium alloy on BMP-2 expression in bone of ovariectomized osteoporosis rats.

Materials science & engineering. C, Materials for biological applications·2013
Same author

[Texture variation of CC 5052 aluminum alloy slab from surface to center layer by XRD].

Guang pu xue yu guang pu fen xi = Guang pu·2013
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026
See all related articles

Related Experiment Video

Updated: May 23, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.7K

A quality assessment algorithm for no-reference images based on transfer learning.

Yang Yang1, Chang Liu1, Hui Wu1

  • 1College of Media Engineering, Communication University of Zhejiang, Hang Zhou, China.

Peerj. Computer Science
|March 10, 2025
PubMed
Summary
This summary is machine-generated.

A new no-reference image quality assessment (NR-IQA) algorithm uses transfer learning and deep convolutional neural networks to effectively evaluate image quality without a reference image. This method shows improved performance across diverse datasets.

Keywords:
Adaptive fusion networkDeep convolutional neural networkImage quality assessment (IQA)Non-reference image quality assessment (IQA-NRTL)Transfer learning

More Related Videos

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

Related Experiment Videos

Last Updated: May 23, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.7K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Reference-based image quality assessment (IQA) is well-established.
  • No-reference IQA (NR-IQA) methods are less developed but crucial for automated defect detection and correction.
  • Existing NR-IQA algorithms struggle with diverse image complexities and distortions.

Purpose of the Study:

  • To propose a novel NR-IQA algorithm utilizing transfer learning (IQA-NRTL).
  • To enhance the accuracy and robustness of automated image quality evaluation.
  • To address the limitations of current NR-IQA approaches.

Main Methods:

  • Leveraging deep convolutional neural networks (CNNs) for multi-scale semantic feature extraction via a visual perception module.
  • Employing an adaptive fusion network to integrate extracted features.
  • Utilizing a fully connected regression network for final quality assessment based on fused and global semantic information.

Main Results:

  • The proposed IQA-NRTL algorithm demonstrated significant performance improvements over mainstream NR-IQA methods.
  • Effective evaluation across authentically distorted, synthetically distorted, and AI-generated image datasets.
  • Robustness shown across variations in image content and complexity.

Conclusions:

  • The IQA-NRTL algorithm offers a superior approach to NR-IQA.
  • Transfer learning combined with CNNs effectively captures essential image features for quality assessment.
  • The method shows promise for real-world applications in image processing and transmission.