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    Area of Science:

    • Computer Graphics
    • Artificial Intelligence
    • Image Processing

    Background:

    • AI text-to-image models offer fine-grained control, rivaling traditional rendering techniques.
    • Assessing AI-generated images requires specialized metrics beyond traditional ones like SSIM or PSNR.

    Purpose of the Study:

    • To provide a comprehensive overview of quality metrics for AI text-to-image generation.
    • To propose a taxonomy categorizing these metrics based on compositional and general quality.
    • To cover benchmark datasets and identify challenges in text-to-image evaluation.

    Main Methods:

    • Literature survey of existing text-to-image quality assessment metrics.
    • Development of a taxonomy for categorizing metrics.
    • Review of benchmark datasets used for metric evaluation.

    Main Results:

    • A comprehensive overview of text-to-image quality metrics is presented.
    • A novel taxonomy categorizes metrics into compositional and general quality.
    • Key benchmark datasets and their usage are discussed.

    Conclusions:

    • Dedicated metrics are essential for evaluating AI text-to-image models.
    • The proposed taxonomy provides a structured approach to metric selection and understanding.
    • Identifying limitations and challenges guides future research in text-to-image evaluation.