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Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
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Subjective and Objective Audio-Visual Quality Assessment for User Generated Content.

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    This study introduces the first User Generated Content (UGC) audio-visual quality assessment (AVQA) database. A novel AVQA model is proposed, outperforming benchmarks for in-the-wild UGC video quality prediction.

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    Area of Science:

    • Multimedia analysis
    • Signal processing
    • Human-computer interaction

    Background:

    • User Generated Content (UGC) is prevalent in video sharing, necessitating quality assessment.
    • Existing Video Quality Assessment (VQA) often overlooks audio's impact on perceptual quality.
    • Audio-visual quality assessment (AVQA) is crucial for understanding user experience in UGC.

    Purpose of the Study:

    • To comprehensively study UGC audio-visual quality assessment (AVQA).
    • To construct the first UGC AVQA database (SJTU-UAV) with in-the-wild content.
    • To develop an effective AVQA model for UGC videos.

    Main Methods:

    • Collected 520 UGC audio-video sequences for the SJTU-UAV database.
    • Conducted subjective experiments to obtain Mean Opinion Scores (MOSs).
    • Developed a novel AVQA model learning joint audio-visual temporal features.

    Main Results:

    • The SJTU-UAV database provides diverse, in-the-wild UGC content.
    • Benchmark AVQA models showed limitations with real-world UGC.
    • The proposed AVQA model significantly outperformed benchmarks on UGC datasets.

    Conclusions:

    • Audio-visual quality is critical for UGC perception.
    • The proposed model offers a robust solution for UGC AVQA.
    • The SJTU-UAV database and model code will advance AVQA research.