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Screen Content Video Quality Assessment: Subjective and Objective Study.

Shan Cheng, Huanqiang Zeng, Jing Chen

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 27, 2020
    PubMed
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    This summary is machine-generated.

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    This study introduces the first subjective and objective quality assessment for screen content videos (SCVs). A new database (SCVD) and a spatiotemporal Gabor feature tensor-based model (SGFTM) were developed for evaluating SCV perceptual quality.

    Area of Science:

    • Computer Vision
    • Multimedia Signal Processing
    • Human-Computer Interaction

    Background:

    • Screen content videos (SCVs) present unique challenges for quality assessment due to their distinct characteristics compared to natural videos.
    • Existing video quality assessment (VQA) methods may not adequately capture the perceptual nuances of SCVs.

    Purpose of the Study:

    • To establish the first comprehensive subjective and objective quality assessment framework for SCVs.
    • To create a large-scale, publicly available database for SCV quality research.
    • To propose a novel VQA model tailored for SCVs.

    Main Methods:

    • Construction of the Screen Content Video Database (SCVD) with 16 reference SCVs, 800 distorted SCVs across 10 distortion types and 5 degradation levels.
    • Subjective quality assessment involving at least 32 subjects per distorted SCV.

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  • Development of the spatiotemporal Gabor feature tensor-based model (SGFTM) utilizing 3D-Gabor filters for feature extraction and a novel pooling strategy.

    • Main Results:

    • The SCVD provides a valuable resource for SCV quality research.
    • The proposed SGFTM demonstrates high consistency with subjective quality perceptions.
    • SGFTM significantly outperforms existing classical and state-of-the-art VQA models on the SCVD.

    Conclusions:

    • The developed SCVD and SGFTM represent significant advancements in SCV quality assessment.
    • The SGFTM offers a robust and accurate objective method for evaluating SCV perceptual quality.
    • This work provides a foundation for future research in SCV quality assessment and related applications.