Quality Assessment of Sharpened Images: Challenges, Methodology, and Objective Metrics
Most of the effort in image quality assessment (QA) has been so far dedicated to the degradation of the image. However, there are also many algorithms in the image processing chain that can enhance the quality of an input image. These include procedures for contrast enhancement, deblurring, sharpening, up-sampling, denoising, transfer function compensation, and so on. In this paper, possible strategies for the QA of sharpened images are investigated. This task is not trivial, because the sharpening techniques can increase the perceived quality, as well as introduce artifacts leading to the quality drop (over-sharpening). Here, the framework specifically adapted for the QA of sharpened images and objective metrics comparison in this context is introduced. However, the framework can be adopted in other QA areas as well. The problem of selecting the correct procedure for subjective evaluation was addressed and a subjective test on blurred, sharpened, and over-sharpened images was performed in order to demonstrate the use of the framework. The obtained ground-truth data were used for testing the suitability of the state-of-the-art objective quality metrics for the assessment of sharpened images. The comparison was performed by novel procedure using rank order correlation analyses, which is found more appropriate for the task than standard methods. Furthermore, seven possible augmentations of the no-reference S3 metric adapted for sharpened images are proposed. The performance of the metric is significantly improved and also superior over the rest of the tested quality criteria with respect to the subjective data.