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Pages 301-1 - 301-6,  This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. 2023
Volume 35
Issue 8
Abstract

Most cameras use a single-sensor arrangement with Color Filter Array (CFA). Color interpolation techniques performed during image demosaicing are normally the reason behind visual artifacts generated in a captured image. While the severity of the artifacts depends on the demosaicing methods used, the artifacts themselves are mainly zipper artifacts (block artifacts across the edges) and false-color distortions. In this study and to evaluate the performance of demosaicing methods, a subjective pair-comparison method with 15 observers was performed on six different methods (namely Nearest Neighbours, Bilinear interpolation, Laplacian, Adaptive Laplacian, Smooth hue transition, and Gradient-Based image interpolation) and nine different scenes. The subjective scores and scene images are then collected as a dataset and used to evaluate a set of no-reference image quality metrics. Assessment of the performance of these image quality metrics in terms of correlation with the subjective scores show that many of the evaluated no-reference metrics cannot predict perceived image quality.

Digital Library: EI
Published Online: January  2023
  182  15
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Page ,  © Society for Imaging Science and Technology 2021
Volume 29
Issue 1

Over the years, a high number of different objective image quality metrics have been proposed. While some image quality metrics show a high correlation with subjective scores provided in different datasets, there still exists room for improvement. Different studies have pointed to evaluating the quality of images affected by geometrical distortions as a challenge for current image quality metrics. In this work, we introduce the Colourlab Image Database: Geometric Distortions (CID:GD) with 49 different reference images made specifically to evaluate image quality metrics. CID:GD is one of the first datasets which include three different types of geometrical distortions; seam carving, lens distortion, and image rotation. 35 state-ofthe-art image quality metrics are tested on this dataset, showing that apart from a handful of these objective metrics, most are not able to show a high performance. The dataset is available at <ext-link ext-link-type="url" xlink:href="http://www.colourlab.no/cid">www.colourlab.no/cid</ext-link>.

Digital Library: CIC
Published Online: November  2021

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