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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Large-Scale Study of Perceptual Video Quality.

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    Existing no-reference video quality models struggle with diverse distortions. A new large-scale database (LIVE-VQC) with authentic distortions and extensive subjective scores advances video quality prediction.

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    Area of Science:

    • Computer Vision
    • Signal Processing
    • Human-Computer Interaction

    Background:

    • Video quality assessment faces challenges due to diverse video sources, processing, and display technologies, leading to varied impairments.
    • Current no-reference (NR) video quality models are limited by small, unrepresentative datasets that fail to capture real-world video complexity and distortions.
    • Existing databases lack the diversity in content, capture conditions, and authentic, complex distortions needed to train robust NR video quality predictors.

    Purpose of the Study:

    • To address limitations in current video quality assessment datasets and advance the development of NR video quality prediction models.
    • To create a large-scale, diverse video quality assessment database that reflects real-world video complexities and authentic distortions.
    • To provide a benchmark for evaluating and improving NR video quality prediction algorithms.

    Main Methods:

    • Construction of the LIVE Video Quality Challenge Database (LIVE-VQC) with 585 unique videos.
    • Collection of subjective video quality scores from 4776 participants, totaling over 205,000 opinion scores.
    • Inclusion of videos with a wide range of complex, authentic distortions captured under diverse conditions.

    Main Results:

    • The LIVE-VQC database represents a significant expansion in scale and diversity compared to existing video quality datasets.
    • Initial evaluations show the value of LIVE-VQC for benchmarking leading NR video quality predictors.
    • The study highlights the need for more comprehensive datasets to improve NR video quality prediction accuracy.

    Conclusions:

    • The LIVE-VQC database is a valuable resource for advancing research in no-reference video quality assessment.
    • This large-scale study provides critical insights into the challenges and requirements for robust video quality prediction.
    • The availability of LIVE-VQC will facilitate the development of more accurate and generalizable NR video quality models.