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Towards a Perceptual Image Quality A...

Lee, Dohyoung.

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Towards a Perceptual Image Quality Assessment Framework for Color Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards a Perceptual Image Quality Assessment Framework for Color Data./
作者:	Lee, Dohyoung.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	172 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10244652
ISBN:	9780355448122

Towards a Perceptual Image Quality Assessment Framework for Color Data.
Lee, Dohyoung.

Towards a Perceptual Image Quality Assessment Framework for Color Data. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 172 p.

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2017.

Quality assessment of image data plays a vital role in various applications, e.g., the evaluation and optimization of visual processing algorithms and the monitoring of visual communication systems. Although subjective assessment is the most reliable means to measure image quality, it is not always feasible in practical applications. Therefore, objective image quality metrics (IQMs) that can accurately predict the subjective judgments of average human observers have gained considerable attentions from research community. In the past few decades, numerous IQMs have been proposed to estimate the perceived quality of visual data. Depending on the availability of a reference (i.e., perfect quality) image to compare with, they can be categorized into full-reference (FR) and no-reference (NR) IQMs. Most existing IQMs are designed to rely on image features in the grayscale domain. Despite their reasonable performance in dealing with traditional distortions (e.g. additive white Gaussian noise or Gaussian blur), such grayscale IQMs tend to underestimate the visual disturbance caused by chromatic distortions, e.g., degradation caused from color gamut mapping or tone mapping algorithms.

ISBN: 9780355448122Subjects--Topical Terms:

649834
Electrical engineering.

Towards a Perceptual Image Quality Assessment Framework for Color Data.
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520 $a Quality assessment of image data plays a vital role in various applications, e.g., the evaluation and optimization of visual processing algorithms and the monitoring of visual communication systems. Although subjective assessment is the most reliable means to measure image quality, it is not always feasible in practical applications. Therefore, objective image quality metrics (IQMs) that can accurately predict the subjective judgments of average human observers have gained considerable attentions from research community. In the past few decades, numerous IQMs have been proposed to estimate the perceived quality of visual data. Depending on the availability of a reference (i.e., perfect quality) image to compare with, they can be categorized into full-reference (FR) and no-reference (NR) IQMs. Most existing IQMs are designed to rely on image features in the grayscale domain. Despite their reasonable performance in dealing with traditional distortions (e.g. additive white Gaussian noise or Gaussian blur), such grayscale IQMs tend to underestimate the visual disturbance caused by chromatic distortions, e.g., degradation caused from color gamut mapping or tone mapping algorithms.
520 $a This study proposes new color IQMs capable of handling image data exhibiting both chromatic and achromatic distortions by incorporating perceptual color attributes, e.g., hue and chroma. Both FR and NR IQMs are introduced for different target applications. In particular, the proposed solutions properly process directional hue data using directional statistical tools, addressing the general limitation of existing approaches that treating hue data as linear data. Extensive validation performed on large-scale databases demonstrates the proposed IQMs correlate well with the subjective ratings over commonly encountered chromatic and achromatic distortions, indicating that the appropriate handling of highly informative hue data improves the prediction accuracy of color IQMs. These promising results indicate that they can be deployed on a wide range of color image processing problems as generalized quality assessment solutions.
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