Face quality analysis of single-image super-resolution based on SIFT

Abstract

Single-image super-resolution (SISR) aims at improving image quality, and there so far exist many SISR algorithms to hallucinate super-resolution (super-res) image from simulated low-res image. In order to evaluate SISR algorithms, objective image quality assessment (IQA), e.g., full reference IQA and no-reference IQA, and subjective quality are usually estimated. However, the objective IQA usually does not well match with the subjective quality. This paper therefore introduces a new measurement based on SIFT key-points. Both descriptors and locations of SIFT key-points are used to detect the matched SIFT key-points between super-res image and its high-res label image. The more the matched SIFT key-points are, the closer super-res image should be to its high-res label image, that is the SISR algorithm is able to recover more SIFT key-points. Both simulated low-res faces and real low-res face are employed to validate the evaluation strategy. The normalization of the number of SIFT key-points is proposed and mean opinion score from 30 raters are collected to evaluate SISR algorithms. The experimental results show that the objective IQA based on SIFT key-points are able to effectively evaluate SISR algorithms, and can well match with the subjective IQA.