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    Area of Science:

    • Computer Vision
    • Image Processing
    • Human-Computer Interaction

    Background:

    • Assessing digital image quality is crucial for visual information consumed by humans.
    • Standard dynamic range (SDR) images have limitations compared to High Dynamic Range (HDR) and Multi-Exposure Fusion (MEF) techniques.
    • Existing HDR and MEF databases are often small and lack diverse, real-world viewing conditions, limiting the development of robust image quality assessment models.

    Purpose of the Study:

    • To address limitations in existing image quality databases.
    • To create a comprehensive database for developing and evaluating image quality assessment models for HDR and MEF images.
    • To facilitate realistic assessment of image quality under varied viewing conditions.

    Main Methods:

    • Conducted a massively crowdsourced online subjective study involving over 5000 observers.
    • Collected over 300,000 human opinion scores on 1811 diverse HDR and MEF images.
    • Created the ESPL-LIVE HDR Image Database, including images processed by various tone-mapping operators and MEF algorithms, with and without post-processing.

    Main Results:

    • Developed the ESPL-LIVE HDR Image Database, a valuable resource for image quality research.
    • Gathered a large-scale dataset of human perception scores for HDR and MEF images.
    • Evaluated the performance of state-of-the-art no-reference image quality assessment algorithms against the collected human opinion scores.

    Conclusions:

    • The ESPL-LIVE HDR Image Database provides a robust foundation for advancing image quality assessment research.
    • The study highlights the importance of large-scale, crowdsourced data for realistic image quality evaluation.
    • Findings facilitate the development of more accurate and reliable algorithms for assessing digital image quality.