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    This study introduces a new model for estimating the just noticeable difference (JND) in images by incorporating pattern complexity alongside contrast masking. This approach more accurately reflects how the human visual system (HVS) perceives image details.

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    Area of Science:

    • Image processing and computer vision
    • Human visual perception
    • Signal processing

    Background:

    • The just noticeable difference (JND) quantifies human visual system (HVS) limitations and is crucial for image redundancy estimation.
    • Current JND models often use contrast masking, which inadequately captures complex interactions within visual content.
    • Cognitive science research highlights the HVS's efficiency in recognizing repeated patterns for visual representation.

    Purpose of the Study:

    • To develop an improved Just Noticeable Difference (JND) estimation model that accounts for pattern complexity.
    • To enhance the accuracy of spatial masking estimation by integrating pattern complexity with luminance contrast.
    • To create a model that better aligns with human perceptual judgments in image processing.

    Main Methods:

    • Formulating pattern complexity based on the diversity of pixel orientation in local image regions, inspired by orientation selectivity in the visual cortex.
    • Developing a novel spatial masking estimation function that combines pattern complexity and luminance contrast.
    • Building an improved JND estimation model incorporating the new masking function.

    Main Results:

    • The proposed JND model demonstrates superior performance compared to existing state-of-the-art models.
    • Experimental results show high consistency between the model's predictions and human perception.
    • The model effectively accounts for the intricate interplay between visual content and masking effects.

    Conclusions:

    • The integration of pattern complexity offers a more robust approach to spatial masking estimation in JND models.
    • The developed JND model provides a more accurate representation of human visual perception for image processing tasks.
    • The findings contribute to more effective visual redundancy estimation and image compression techniques.