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Detecting and Mapping Video Impairments.

Todd R Goodall, Alan C Bovik

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    Summary
    This summary is machine-generated.

    This study introduces the Video Impairment Mapper (VIDMAP), a novel neural network for detecting video artifacts without a reference. VIDMAP accurately maps common video distortions, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Digital Signal Processing
    • Machine Learning

    Background:

    • Identifying video artifacts without a reference video is challenging.
    • Existing methods often lack accuracy or require reference data.

    Purpose of the Study:

    • To develop an efficient no-reference video artifact detection system.
    • To create a model capable of mapping artifact locations and severities.

    Main Methods:

    • A dual-path (parallel) excitatory/inhibitory neural network architecture was employed.
    • A statistical image model was used for distortion sensitization.
    • The system generates full-resolution space-time distortion maps.

    Main Results:

    • The Video Impairment Mapper (VIDMAP) achieved state-of-the-art performance in artifact detection.
    • VIDMAP accurately detects and maps eight common artifact categories.
    • The system outperforms previous methods on whole-image artifact detection tasks.

    Conclusions:

    • VIDMAP offers a novel and effective solution for no-reference video artifact analysis.
    • The system provides detailed, high-resolution maps of video impairments.
    • A software release is available for public use and evaluation.