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    This summary is machine-generated.

    This study introduces a novel method for estimating the just noticeable distortion (JND) profile using patch-level structural visibility learning. This approach improves image quality assessment by analyzing image patches for better human perception correlation.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Human-Computer Interaction

    Background:

    • Traditional pixel-level Just Noticeable Distortion (JND) estimation has limitations in correlating with human visual perception.
    • Image patches serve as a more effective unit for analyzing visual information and perceptual quality.

    Purpose of the Study:

    • To develop an effective approach for inferring the JND profile using patch-level structural visibility learning.
    • To improve the accuracy and relevance of JND estimation by considering structural degradation and human perception.

    Main Methods:

    • Decomposing image patches into three components for visibility estimation.
    • Employing a deep learning-based structural degradation estimation model to capture perceptual masking.
    • Establishing a comprehensive JND dataset with pristine and distorted images generated using Versatile Video Coding (VVC) standards.

    Main Results:

    • The proposed patch-level approach demonstrates superior performance compared to existing state-of-the-art methods.
    • The deep learning model effectively approximates structural visibility masking.
    • The established dataset facilitates robust JND profile learning.

    Conclusions:

    • Patch-level structural visibility learning offers a more effective method for JND profile inference.
    • The developed approach enhances image quality assessment by better aligning with human perception.
    • The publicly available dataset supports further research in perceptual image quality.