  With the development of image recovery models,especially those based on
adversarial and perceptual losses,the detailed texture portions of images are
being recovered more naturally.However,these restored images are similar but
not identical in detail texture to their reference images.With traditional
image quality assessment methods,results with better subjective perceived
quality often score lower in objective scoring.Assessment methods suffer from
subjective and objective inconsistencies.This paper proposes a regional
differential information entropy (RDIE) method for image quality assessment to
address this problem.This approach allows better assessment of similar but not
identical textural details and achieves good agreement with perceived
quality.Neural networks are used to reshape the process of calculating
information entropy,improving the speed and efficiency of the operation.
Experiments conducted with this study image quality assessment dataset and the
PIPAL dataset show that the proposed RDIE method yields a high degree of
agreement with people average opinion scores compared to other image quality
assessment metrics,proving that RDIE can better quantify the perceived quality
of images.
