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Just Noticeable Difference Estimation for Screen Content Images.

Shiqi Wang, Lin Ma, Yuming Fang

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    |June 2, 2016
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    Summary
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    This study introduces a new model for predicting visual perception thresholds (just noticeable difference) in screen content images. The model accurately predicts visual quality and improves perceptually lossless compression for text-heavy images.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Human Visual System Modeling

    Background:

    • Screen content images (SCI) possess unique characteristics affecting human visual perception.
    • Existing models often fail to accurately capture these specific visual behaviors, especially for text.

    Purpose of the Study:

    • To develop a novel just noticeable difference (JND) model tailored for screen content images.
    • To improve the accuracy of visual distortion prediction and enhance perceptually lossless compression algorithms.

    Main Methods:

    • A local parametric edge model with adaptive edge profile representation was employed.
    • Edge profiles were decomposed into luminance, contrast, and structure for visibility threshold evaluation.
    • Subjective experiments were conducted to analyze edge luminance adaptation, contrast masking, and structural distortion sensitivity.

    Main Results:

    • The proposed JND model accurately predicts the JND profile for SCIs.
    • It demonstrates superior distortion masking ability compared to state-of-the-art methods.
    • The model enables perceptually lossless SCI compression with better visual quality at equivalent bitrates.

    Conclusions:

    • The novel JND model effectively addresses the unique visual properties of SCIs.
    • It offers significant improvements in both distortion prediction and compression efficiency.
    • This research advances the field of image processing and visual quality assessment for screen content.