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Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
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The Performance of Quality Metrics in Assessing Error-Concealed Video Quality.

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    Assessing video quality after packet loss is crucial for interactive streaming. This study finds that objective video quality metrics, rather than just frame-level image metrics, better predict human perception of error concealment effectiveness.

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    Area of Science:

    • Computer Science
    • Signal Processing
    • Multimedia Systems

    Background:

    • Interactive video streaming (e.g., video conferencing) often omits retransmission due to strict deadlines, necessitating error concealment for lost data.
    • Existing error concealment techniques lack standardized methods for comparing their perceived video quality.

    Purpose of the Study:

    • To evaluate the performance of 16 image and video quality metrics in assessing error-concealed video quality.
    • To determine the effectiveness of these metrics in predicting subjective quality scores reported by human observers.
    • To identify which objective quality metrics are most suitable for evaluating error concealment techniques.

    Main Methods:

    • Encoded video streams were subjected to packet loss, and the resulting data loss was concealed using various techniques.
    • 16 objective image and video quality metrics (e.g., PSNR, SSIM, VQM) were applied to the error-concealed video sequences.
    • The objective metric scores were compared against subjective quality assessments provided by human subjects.

    Main Results:

    • Subjective video quality cannot always be predicted solely from the visual quality of an individual error-concealed frame.
    • Objective video quality metrics, considering error propagation over clips, generally offer a better assessment of error concealment performance than image metrics on single frames.
    • Some newly developed objective metrics demonstrated significant inaccuracies, correctly predicting human judgment only about 60% of the time.

    Conclusions:

    • Objective video quality metrics are more reliable than image quality metrics for evaluating error concealment techniques in interactive video streaming.
    • Current objective quality metrics may not perform optimally when assessing error-concealed video, highlighting the need for specialized metrics.
    • The study identifies specific quality metrics that show greater suitability for judging the effectiveness of video error concealment methods.