New quality metrics for digital image resizing

Digital image rescaling by interpolation has been intensively researched over past decades, and still getting constant attention from many applications such as medical diagnosis, super-resolution, image blow-up, nano-manufacturing, etc. However, there are no consented metrics to objectively assess and compare the quality of resized images. Some existing measures such as peak-signal-to-noise ratio (PSNR) or mean-squared error (MSE), widely used in image restoration area, do not always coincide with the opinions from viewers. Enlarged digital images generally suffer from two major artifacts: blurring, zigzagging, and those undesirable effects especially around edges significantly degrade the overall perceptual image quality. We propose two new image quality metrics to measure the degree of the two major defects, and compare several existing interpolation methods using the proposed metrics. We also evaluate the validity of image quality metrics by comparing rank correlations.