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Quality Assessment of Sharpened Images: Challenges, Methodology, and Objective Metrics.

Authors
  • Krasula, Lukas
  • Le Callet, Patrick
  • Fliegel, Karel
  • Klima, Milos
Type
Published Article
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Jan 10, 2017
Identifiers
DOI: 10.1109/TIP.2017.2651374
PMID: 28092541
Source
Medline
License
Unknown

Abstract

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, etc. In this work, possible strategies for the quality assessment 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 quality assessment of sharpened images and objective metrics comparison in this context is introduced. However, the framework can be adopted in other quality assessment 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 state-ofthe- art objective quality metrics for the assessment of sharpened images. The comparison was performed by novel procedure using ROC 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.

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