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Related Experiment Video

Updated: Aug 7, 2025

Visualizing Visual Adaptation
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Just Noticeable Difference Model for Images with Color Sensitivity.

Zhao Zhang1, Xiwu Shang1, Guoping Li1

  • 1School of Electrical Engineering, Shanghai University of Engineering Science, No. 333, Longteng Road, Songjiang District, Shanghai 201620, China.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary

A new Color-Sensitivity-based Just Noticeable Difference (JND) model improves image/video processing by accounting for human visual system (HVS) limitations. This model enhances perceptual redundancy removal by considering visual saliency and color sensitivity.

Keywords:
color sensitivityjust noticeable differencemasking effectperceptual redundancyvisual saliency

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Area of Science:

  • Computer Vision
  • Image Processing
  • Human Visual System Modeling

Background:

  • Just Noticeable Difference (JND) models are crucial for perceptual image/video processing and redundancy removal, reflecting human visual system (HVS) limitations.
  • Existing JND models often treat color channels equally and inadequately estimate masking effects.
  • Improvements are needed to better align JND models with HVS perception.

Purpose of the Study:

  • To introduce a novel Color-Sensitivity-based JND (CSJND) model to enhance perceptual image/video processing.
  • To improve the estimation of masking effects and incorporate visual saliency and color sensitivity.
  • To achieve better consistency with the human visual system compared to existing JND models.

Main Methods:

  • Developed a comprehensive masking effect estimation combining contrast masking, pattern masking, and edge protection.
  • Incorporated visual saliency to adaptively modulate the masking effect.
  • Implemented color sensitivity modulation based on HVS perceptual sensitivities to adjust Y, Cb, and Cr component thresholds.

Main Results:

  • The proposed CSJND model was constructed by integrating visual saliency and color sensitivity.
  • Extensive experiments and subjective tests demonstrated the effectiveness of the CSJND model.
  • The CSJND model showed superior consistency with the HVS compared to state-of-the-art JND models.

Conclusions:

  • The CSJND model offers a more accurate representation of human visual perception in image/video processing.
  • By considering visual saliency and differential color sensitivity, the model improves perceptual redundancy removal.
  • The CSJND model represents a significant advancement in developing perceptually relevant image/video processing techniques.