Preiss, Jens (2015)
Color-Image Quality Assessment: From Metric to Application.
Technische Universität Darmstadt
Ph.D. Thesis, Primary publication
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Item Type: | Ph.D. Thesis | ||||
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Type of entry: | Primary publication | ||||
Title: | Color-Image Quality Assessment: From Metric to Application | ||||
Language: | English | ||||
Referees: | Urban, Dr. Philipp ; Dörsam, Prof. Edgar ; Goesele, Prof. Michael | ||||
Date: | 2015 | ||||
Place of Publication: | Darmstadt | ||||
Date of oral examination: | 17 December 2014 | ||||
Abstract: | In digital imaging, evaluating the visual quality of images is a crucial requirement for most image-processing systems. For such an image quality assessment, mainly objective assessments are employed which automatically predict image quality by a computer algorithm. The vast majority of objective assessments are so-called image difference metrics which predict the perceived difference between a distorted image and a reference. Due to the limited understanding of the human visual system, image quality assessment is not straightforward and still an open research field. The majority of image-difference metrics disregard color information which allows for faster computation. Even though their performance is sufficient for many applications, they are not able to correctly predict the quality for a variety of color distortions. Furthermore, many image-difference metrics do not account for viewing conditions which may have a large impact on the perceived image quality (e.g., a large display in an office compared with a small mobile device in the bright sunlight). The main goal of my research was the development of a new image difference metric called improved Color-Image-Difference (iCID) which normalizes images to standard viewing conditions and extracts chromatic features. The new metric was then used as objective function to improve gamut mapping as well as tone mapping. Both methods represent essential transformations for the reproduction of color images. The performance of the proposed metric was verified by visual experiments as well as by comparisons with human judgments. The visual experiments reveal significant improvements over state-of-the-art gamut-mapping and tone-mapping transformations. For gamut-mapping distortions, iCID exhibits the significantly highest correlation to human judgments and for conventional distortions (e.g., noise, blur, and compression artifacts), iCID outperforms almost all state-of-the-art metrics. |
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Uncontrolled Keywords: | Color Science, Image Processing, Image Quality, Gamut Mapping, High-Dynamic-Range Imaging | ||||
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URN: | urn:nbn:de:tuda-tuprints-43890 | ||||
Classification DDC: | 000 Generalities, computers, information > 004 Computer science 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering |
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Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Printing Science and Technology (IDD) |
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Date Deposited: | 09 Feb 2015 10:41 | ||||
Last Modified: | 09 Jul 2020 00:52 | ||||
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/4389 | ||||
PPN: | 354822403 | ||||
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