No-Reference Quality Assessment Based on Spatial Statistic for Generated Images

In recent years, generative adversarial networks has made remarkable progress in the field of text-to-image synthesis whose task is to obtain high-quality generated images. Current evaluation metrics in this field mainly evaluate the quality distribution of the generated image dataset rather than the quality of single image itself. With the deepening research of text-to-image synthesis, the quality and quantity of generated images will be greatly improved. There will be a higher demand for generated image evaluation. Therefore, this paper proposes a blind generated image evaluator(BGIE) based on BRISQUE model and sparse neighborhood co-occurrence matrix, which is specially used to evaluate the quality of single generated image. Through experiments, BGIE surpasses all no-reference methods proposed in the past. Compared to VSS method, the surpassing ratio: SRCC is 8.8%, PLCC is 8.8%. By the “One-to-Multi” high-score image screening experiment, it is proved that the BGIE model can screen out best image from multiple images.