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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

741
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.
741
Deconvolution01:20

Deconvolution

197
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
197

You might also read

Related Articles

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

Sort by
Same author

Influence of food groups on plasma total homocysteine for specific MTHFR C677T genotypes in Chinese population.

Molecular nutrition & food research·2016
Same author

NIR Light Propulsive Janus-like Nanohybrids for Enhanced Photothermal Tumor Therapy.

Small (Weinheim an der Bergstrasse, Germany)·2016
Same author

Smart Hydrogels with Inhomogeneous Structures Assembled Using Nanoclay-Cross-Linked Hydrogel Subunits as Building Blocks.

ACS applied materials & interfaces·2016
Same author

Synergy between von Hippel-Lindau and P53 contributes to chemosensitivity of clear cell renal cell carcinoma.

Molecular medicine reports·2016
Same author

Aerobic Degradation of Sulfadiazine by Arthrobacter spp.: Kinetics, Pathways, and Genomic Characterization.

Environmental science & technology·2016
Same author

Downregulation of ClC-3 in dorsal root ganglia neurons contributes to mechanical hypersensitivity following peripheral nerve injury.

Neuropharmacology·2016

Related Experiment Video

Updated: Jul 24, 2025

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

7.7K

Unsupervised blind image quality assessment via joint spatial and transform features.

Chao Yang1, Qinglin He2, Ping An2

  • 1School of Communication and Information Engineering, Shanghai University, Shanghai, China. yangchaoie@shu.edu.cn.

Scientific Reports
|July 5, 2023
PubMed
Summary

This study introduces a new unsupervised blind image quality assessment (BIQA) method that does not require mean opinion scores for training. The novel approach uses combined spatial and transform features for accurate, no-reference image quality evaluation.

More Related Videos

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

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

Published on: February 23, 2024

634
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.6K

Related Experiment Videos

Last Updated: Jul 24, 2025

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

7.7K
Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

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

Published on: February 23, 2024

634
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.6K

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Blind Image Quality Assessment (BIQA) is crucial for evaluating image fidelity without reference images.
  • Existing BIQA methods often require large datasets with Mean Opinion Scores (MOS) for training, limiting their applicability.
  • Developing unsupervised BIQA methods that do not rely on MOS is a significant research challenge.

Purpose of the Study:

  • To propose a novel unsupervised blind image quality assessment (BIQA) method.
  • To develop a BIQA approach that does not require Mean Opinion Scores (MOS) for model training.
  • To enhance the accuracy and robustness of no-reference image quality assessment.

Main Methods:

  • Extraction of joint spatial features: phase congruency, gradient magnitude (GM), GM and Laplacian of Gaussian response, and local normalized coefficients.
  • Extraction of transform features: Karhunen-Loéve transform (KLT) coefficients and discrete cosine transform (DCT) coefficients.
  • Redundancy analysis of features followed by fitting to a multivariate Gaussian model for no-reference quality assessment.

Main Results:

  • The proposed method effectively utilizes combined spatial and transform features for quality assessment.
  • Feature redundancy is analyzed and reduced, leading to a more efficient model.
  • Experimental results on seven IQA databases show superior performance compared to state-of-the-art supervised and unsupervised BIQA methods.

Conclusions:

  • The developed unsupervised BIQA method achieves high performance without relying on MOS.
  • The joint use of spatial and transform features provides a robust approach to image quality evaluation.
  • This method offers a promising alternative for practical, large-scale image quality assessment applications.