| Project/Area Number |
16500101
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Chiba University |
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Principal Investigator |
KOTERA Hiroaki Chiba University, Faculty of Engineering, Professor, 工学部, 教授 (70282449)
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| Co-Investigator(Kenkyū-buntansha) |
HORIUCHI Takahiko Chiba University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (30272181)
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| Project Period (FY) |
2004 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2005: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 2004: ¥2,000,000 (Direct Cost: ¥2,000,000)
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| Keywords | Gamut mapping / HDR image / Gamut compression / Gamut expansion / Gamut boundary descriptor / Gamut comparison / Bi-directional mapping / Pleasant color mapping / 高レンジ画像 / 色見え |
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| Research Abstract |
We have been developing a bi-directional versatile gamut mapping algorithm with gamut compression from wide to narrow or gamut expansion from narrow to wide for obtaining a pleasant image by adapting to each device gamut effectively. The following is a summary of our results.
1.Construction of bi-directional gamut mapping model and quantitative gamut comparison method
We represented gamut shells of an image and devices such as inkjet printers and LBPs by r-image, and performed the following analysis : (a)numerical gamut comparison by r-image and (b)feature analysis of outside gamut by lightness segmentation. Then we found the following general characteristics of the gamut mapping. (1)When the most of r-image pixel values for an image are greater than those for device in entire region and around middle lightness region, a gamut compression is necessary. (2)If they are smaller than those for device in middle region and mainly located at lower region of lightness, a gamut expansion is desir
… More
able. Finally, we constructed a basic algorithm for bi-directional gamut mapping.
2.Development of a high accurate description method of a device gamut
In order to describe the gamut surface with a small number of color chips precisely, we developed a mapping algorithm to highly precise r-image which did not produce any empty segments by the following ideas : (1)color space was non-uniformly divided into a segment for each to include the constant color samples, (2)the gamut surface was shaped as polygon meshes, (3)the polygon meshes were divided again by a constant discrete polar angle segment (Δθ,Δψ).
3.Development of a gamut compression/expansion algorithm
We developed an image-dependent gamut expansion algorithm so called "histogram stretch method" and tested for typical image samples. We pointed out that decision of an expansion breaking point in a lightness axis was important, and decided the point statistically based on the gamut boundary of a device and the standard deviation of color distribution in an image. Furthermore, the method developed into "histogram rescaling method" which could perform continuous bi-directional mapping without any gamut comparison procedures. We verified that the adaptive gamut expansion and compression could be performed between an input image and a devise automatically through experiments with actual images.
4.Examination about an application to an HDR image
Dynamic range compression is indispensability to display an HDR image. We developed an improved Retinex model using an integrated surround field, and largely improved the color appearance at the dark place in the image by compressing a 32 bit HDR image into an 8 bit LDR image. Moreover, we developed an LCRT(Local Contrast Range Transform) algorithm and succeeded to display high quality images by applying the LCRT to HDR images captured by a video camera.
5.Development of gamut mapping algorithm to a pleasant image by scene reference
By the proposed gamut mapping based on the histogram stretch/rescaling, color reproduction process is finally entrusted to CMS. In another point of view, we developed "color transmission/color exchange algorithm between different scenes" as a new concept to produce a pleasant color image. We found possibility to get a pleasant image by gamut mapping between different scenes without any conventional color chips and any standard images. Less
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