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A Perceptual Distinguishability Predictor For JND-noise-contaminated Images.

Hadi Hadizadeh, Ahmad Reza Heravi, Ivan V Bajic

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    Summary
    This summary is machine-generated.

    This study introduces a perceptual distinguishability predictor (PDP) to objectively evaluate image quality metrics. The PDP model accurately determines if an image is perceptually distinguishable from its original, eliminating costly subjective tests.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Perceptual Coding

    Background:

    • Just noticeable difference (JND) models are crucial for estimating perceptual redundancy in images and videos.
    • Current methods for JND model accuracy assessment rely on time-consuming and expensive subjective tests.
    • Comparing JND models often involves subjective evaluation of image quality after noise injection.

    Purpose of the Study:

    • To develop a full-reference metric, the perceptual distinguishability predictor (PDP), for objectively assessing image perceptual distinguishability.
    • To enable efficient and cost-effective comparison of different JND models without subjective testing.
    • To provide a tool for obtaining scaling factors to enhance JND model estimations.

    Main Methods:

    • The proposed PDP metric utilizes sparse coding to extract feature vectors from image pairs.
    • A multilayer neural network is employed for classifying the perceptual distinguishability based on extracted features.
    • A public database of 999 natural images with JND thresholds from four models was created using subjective experiments for training.

    Main Results:

    • The PDP metric achieved a high classification accuracy of 97.1% in distinguishing perceptually different images.
    • The developed method allows for objective comparison of JND models.
    • The PDP can be used to optimize JND thresholds for improved perceptual quality.

    Conclusions:

    • The perceptual distinguishability predictor (PDP) offers an objective and efficient alternative to subjective testing for evaluating JND models.
    • This approach significantly reduces the cost and time associated with image quality assessment.
    • The PDP metric has practical applications in refining image and video compression algorithms and JND model development.