PSNR and SSIM are the most widely used metrics in super-resolution problems,
because they are easy to use and can evaluate the similarities between
generated images and reference images. However, single image super-resolution
is an ill-posed problem, there are multiple corresponding high-resolution
images for the same low-resolution image. The similarities can't totally
reflect the restoration effect. The perceptual quality of generated images is
also important, but PSNR and SSIM do not reflect perceptual quality well. To
solve the problem, we proposed a method called regional differential
information entropy to measure both of the similarities and perceptual quality.
To overcome the problem that traditional image information entropy can't
reflect the structure information, we proposed to measure every region's
information entropy with sliding window. Considering that the human visual
system is more sensitive to the brightness difference at low brightness, we
take γ quantization rather than linear quantization. To accelerate the
method, we reorganized the calculation procedure of information entropy with a
neural network. Through experiments on our IQA dataset and PIPAL, this paper
proves that RDIE can better quantify perceptual quality of images especially
GAN-based images.Comment: 8 pages, 9 figures, 4 table