A survey of metrics for evaluating the performance of generative models in image creation

Abstract

A survey of metrics for evaluating the performance of generative models in image creation

Kataev A.V., Vlasova Y.M., Gusynin D.A., Kim V.A.

Incoming article date: 13.04.2025

This paper provides a survey of metrics used to assess the quality of images generated by generative models. Specialized metrics are required to objectively evaluate image quality. A comparative analysis showed that a combination of different metrics is necessary for a comprehensive evaluation of generation quality. Perceptual metrics are effective for assessing image quality from the perspective of machine systems, while metrics evaluating structure and details are useful for analyzing human perception. Text-based metrics allow for the assessment of image-text alignment but cannot replace metrics focused on visual or structural evaluation. The results of this study will be beneficial for specialists in machine learning and computer vision, as well as contribute to the improvement of generative algorithms and the expansion of diffusion model applications.

Keywords: deep learning, metric, generative model, image quality, image