A Survey on Quality Metrics for Text-to-Image Models
arxiv(2024)
Abstract
Recent AI-based text-to-image models not only excel at generating realistic
images, they also give designers more and more fine-grained control over the
image content. Consequently, these approaches have gathered increased attention
within the computer graphics research community, which has been historically
devoted towards traditional rendering techniques that offer precise control
over scene parameters such as objects, materials, and lighting, when generating
realistic images. While the quality of rendered images is traditionally
assessed through well-established image quality metrics, such as SSIM or PSNR,
the unique challenges presented by text-to-image models, which in contrast to
rendering interweave the control of scene and rendering parameters, necessitate
the development of novel image quality metrics. Therefore, within this survey,
we provide a comprehensive overview of existing text-to-image quality metrics
addressing their nuances and the need for alignment with human preferences.
Based on our findings, we propose a new taxonomy for categorizing these
metrics, which is grounded in the assumption that there are two main quality
criteria, namely compositionality and generality, which ideally map to human
preferences. Ultimately, we derive guidelines for practitioners conducting
text-to-image evaluation, discuss open challenges of evaluation mechanisms, and
surface limitations of current metrics.
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