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 Experiment Video

Updated: Feb 21, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

Multiple-Level Feature-Based Measure for Retargeted Image Quality.

Yabin Zhang, Weisi Lin, Qiaohong Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 10, 2017
    PubMed
    Summary
    This summary is machine-generated.

    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

    Susceptibility Assessment of Glacier-Related Debris Flow in the Gaizi River Basin Using Different Hybrid Anomaly Detection Models.

    Sensors (Basel, Switzerland)·2026
    Same author

    Associative pathways of school bullying with adolescent internet addiction and the moderating role of emotional resilience.

    Frontiers in psychiatry·2026
    Same author

    Crosslinking of Linear Polyimines Into Aminal-Linked Porous Organic Polymers for C<sub>2</sub> Hydrocarbon/Methane Separation.

    Angewandte Chemie (International ed. in English)·2026
    Same author

    Symmetric Entropy-Constrained Video Coding for Machines.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same author

    Current Insights of Muscone: Source, Quality Control, and Protection Mechanism of Cerebral Ischemic Injury.

    Current neuropharmacology·2026
    Same author

    From Global to Granular: Revealing IQA Model Performance Via Correlation Surface.

    IEEE transactions on pattern analysis and machine intelligence·2026
    Same journal

    Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same journal

    Semantic Frame Interpolation.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same journal

    Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same journal

    Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same journal

    AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same journal

    BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    See all related articles

    This study introduces a Multiple-Level Feature (MLF) model for image retargeting quality assessment. The MLF model accurately predicts perceived image quality by analyzing multiple feature levels.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Perceptual Quality Assessment

    Background:

    • Image retargeting aims to adapt images to different display sizes while preserving visual quality.
    • Current computational models often struggle to accurately predict subjective perception of retargeted image quality.
    • Distortions in retargeted images can occur at various levels, from overall aspect ratio to specific object details.

    Purpose of the Study:

    • To develop a novel computational model for predicting the perceptual quality of retargeted images.
    • To improve the accuracy of objective image retargeting quality assessment by considering multiple feature levels.
    • To address limitations in existing methods by incorporating low, mid, and high-level features.

    Main Methods:

    • Proposed a Multiple-Level Feature (MLF) based quality measure.

    More Related Videos

    Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
    11:57

    Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

    Published on: May 20, 2013

    14.0K

    Related Experiment Videos

    Last Updated: Feb 21, 2026

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.9K
    Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
    11:57

    Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

    Published on: May 20, 2013

    14.0K
  • Analyzed low-level aspect ratio similarity.
  • Introduced mid-level edge group similarity for shape/structure distortion.
  • Designed high-level face block similarity for sensitive region deformation.
  • Utilized regression learning to combine complementary features for quality prediction.

    • Main Results:

    • The MLF measure effectively predicts perceptual quality by integrating diverse feature information.
    • Experimental results on benchmark databases show superior accuracy compared to state-of-the-art methods.
    • Demonstrated the complementary nature of low, mid, and high-level features in quality assessment.

    Conclusions:

    • The proposed MLF measure offers a more accurate and robust approach to image retargeting quality assessment.
    • Integrating multiple feature levels significantly enhances the prediction of subjective perceptual quality.
    • The method provides a valuable tool for evaluating and improving image retargeting algorithms.