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Modeling color rendition and color d...

Esposito, Tony.

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Modeling color rendition and color discrimination with average fidelity, average gamut, and gamut shape.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling color rendition and color discrimination with average fidelity, average gamut, and gamut shape./
作者:	Esposito, Tony.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	191 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
標題:	Architectural engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10583715
ISBN:	9781369625578

Modeling color rendition and color discrimination with average fidelity, average gamut, and gamut shape.
Esposito, Tony.

Modeling color rendition and color discrimination with average fidelity, average gamut, and gamut shape. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 191 p.

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2016.

Background The Illuminating Engineering Society (IES) has recognized the industry need for more accurate and predictive color rendition measures and recently published TM-30-15 The IES Method for Evaluating Light Source Color Rendition..

ISBN: 9781369625578Subjects--Topical Terms:

3174102
Architectural engineering.

Modeling color rendition and color discrimination with average fidelity, average gamut, and gamut shape.
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245 1 0 $a Modeling color rendition and color discrimination with average fidelity, average gamut, and gamut shape.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 191 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
500 $a Adviser: Kevin W. Houser.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2016.
520 $a Background The Illuminating Engineering Society (IES) has recognized the industry need for more accurate and predictive color rendition measures and recently published TM-30-15 The IES Method for Evaluating Light Source Color Rendition..
520 $a The technical memorandum (TM-30-15) outlines a two-metric system consisting of an average fidelity metric (Rf), an average gamut metric (Rg), a Color Vector Graphic (CVG), and a suite of other metrics. Rf and Rg were designed to utilize the same set of statistically selected color samples, reference source, and uniform color space such that a tradeoff between them can be explicitly demonstrated. The goal of this study was to explore these tradeoffs by modeling participant responses---various subjective ratings and FM100 hue test performance---under systematically varied light spectra.
520 $a Methodology The IES TM-30-15 Rf- Rg space was partitioned into 12 bins whose centers were the target for spectral optimization. The nominal target Rf values were 65, 75, 85, and 95. The nominal target Rg values were 80, 90, 100, 110, and 120. Two SPDs were designated at each Rf-R g combination to have conceptually orthogonal gamut shapes; one CVG generally oriented in the direction of hue angle bin 1 ('CB1') and one generally oriented in the direction of hue angle Bin 7 ('CB7'). All spectra were created to be a metameric match to 3500 K blackbody radiation and calibrated to an illuminance of 600 lux.
520 $a A single viewing booth was filled with 12 familiar objects with strong memory associations and which span the hue circle, as much as practically possible. Objects were chosen to fit nominally into 6 color groups: "Red," "Orange," "Yellow," "Green," "Blue," and "Purple." Objects were split into two categories: 1. Consumer Goods (6 objects); and 2. Natural Food (6 objects).
520 $a Each of the 24 light spectra were evaluated by 20 participants. Experimentation was blocked such that 20 participants saw a randomly selected 12 of the 24 light spectra, and 20 different participants saw the other 12 light spectra. A total of 40 individuals participated in this experiment. There were 23 males and 17 females with ages ranging from 20 to 41 years and with a mean age of 26 years.
520 $a The independent variables for this experiment were R f, Rg, and gamut shape (specified with variable CB). The dependent variables were subjective ratings of naturalness, vividness, preference, and skin preference and objective measures of color discrimination (error scores from the Farnsworth-Munsell 100 hue test).
520 $a Analysis Subjective ratings It was discovered that the CB variable (the nominal orientation of the CVG) did not provide the granularity needed for model prediction. The visual observation that most of the experimental CVGs can be approximated by an ellipse suggested that a best-fit ellipse approach may be suitable to quantify the shape of the CVG. A direct least-squares fitting of an ellipse is proposed to approximate the CVGs of the 24 experimental SPDs. The resulting best-fit ellipses can be defined by the length of their semi-major axis (a), the length of their semi-minor axis (b), and their angular rotation (psi).
520 $a Overall, the best-fit models demonstrate strong predictive power of the subjective responses.
520 $a Color discrimination The standard scoring software for the FM100 hue test assumes the order of colored caps to be their order under CIE Illuminant C. Direct application of the standard scoring software, therefore, assumes that the test light source does not transpose caps relative to their order under illuminant C. By directly applying the scoring software to an experiment which purposefully varies the illuminant, errors could be miscounted and the results distorted.
520 $a To decouple the error calculation from the standard illuminant, an adjusted Total Error Score (TESadj) was computed which compared participants' responses to the correct order of colored caps under the experimental SPD (not their order under Illuminant C).
520 $a No single metric---of the main TM-30 metrics and best-fit ellipse parameters---has a higher r2 than 0.57. Rg was a fairly poor predictor of TES adj (r2 = 0.47). The poor predictive power of the considered metrics prompted a post-hoc development of custom measures to predict TESadj based on two assumptions: (1) A light source which transposes the colored caps is more likely to cause difficulty discriminating between those caps (measured by the custom metric Rdt), and (2) A light source which compresses caps in color space will make it more difficult to discern between adjacent caps (i.e. smaller average hue angle difference, Deltahi). (Abstract shortened by ProQuest.).
590 $a School code: 0176.
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