Relevant bibliographies by topics / Color space conversion

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  1. Journal articles

Academic literature on the topic 'Color space conversion'

Author: Grafiati
Published: 9 March 2023
Last updated: 11 June 2025

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Journal articles on the topic "Color space conversion"

1

Cao, Cong Jun, and Qiang Jun Liu. "Study on Color Space Conversion Based on RBF Neural Network." Advanced Materials Research 174 (December 2010): 28–31. http://dx.doi.org/10.4028/www.scientific.net/amr.174.28.

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The conversions of color spaces are core techniques of modern ICC color management and the study of color space conversion algorithm between L*a*b* and CMYK is valuable both in theory and in application. In this paper, firstly ECI2002 standard color target data are uniformly selected, including modeling data and testing data; secondly the models of color space conversions from CMYK to L*a*b* and from L*a*b* to CMYK are built based on Radial Basis Function (RBF) neural network; finally the precision of the models are evaluated. This research indicates that the RBF neural network is suitable for the color space conversions between CMYK and L*a*b*. The models’ building processes are simpler and more convenient; the network has fast training speed and good results. With the improvement of the modeling method, this method for color space conversion will have a broader application.
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Le, Hoang, Mahmoud Afifi, and Michael S. Brown. "Improving Color Space Conversion for Camera-Captured Images via Wide-Gamut Metadata." Color and Imaging Conference 2020, no. 28 (2020): 193–98. http://dx.doi.org/10.2352/issn.2169-2629.2020.28.30.

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Color space conversion is the process of converting color values in an image from one color space to another. Color space conversion is challenging because different color spaces have different sized gamuts. For example, when converting an image encoded in a medium-sized color gamut (e.g., AdobeRGB or Display-P3) to a small color gamut (e.g., sRGB), color values may need to be compressed in a many-to-one manner (i.e., multiple colors in the source gamut will map to a single color in the target gamut). If we try to convert this sRGB-encoded image back to a wider gamut color encoding, it can be challenging to recover the original colors due to the color fidelity loss. We propose a method to address this problem by embedding wide-gamut metadata inside saved images captured by a camera. Our key insight is that in the camera hardware, a captured image is converted to an intermediate wide-gamut color space (i.e., ProPhoto) as part of the processing pipeline. This wide-gamut image representation is then saved to a display color space and saved in an image format such as JPEG or HEIC. Our method proposes to include a small sub-sampling of the color values from the ProPhoto image state in the camera to the final saved JPEG/HEIC image. We demonstrate that having this additional wide-gamut metadata available during color space conversion greatly assists in constructing a color mapping function to convert between color spaces. Our experiments show our metadata-assisted color mapping method provides a notable improvement (up to 60% in terms of E) over conventional color space methods using perceptual rendering intent. In addition, we show how to extend our approach to perform adaptive color space conversion based spatially over the image for additional improvements.
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Pearlstein, Larry, Alexander Benasutti, Skyler Maxwell, Matthew Kilcher, Jake Bezold, and Warren Seto. "Retrieval of Color Space Conversion Matrix via Convolutional Neural Network." International Journal of Machine Learning and Computing 9, no. 4 (2019): 393–400. http://dx.doi.org/10.18178/ijmlc.2019.9.4.816.

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4

Jing, Liang. "Design and Realization of Animation Composition and Tone Space Conversion Algorithm." Complexity 2021 (April 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/5579547.

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In recent years, with the development of society and the rapid development of the animation industry, people are paying more and more attention to and requirements for animation production. As an indispensable part of animation production, picture composition plays a major role in animation production. It can give full play to the application of color matching and light and shadow design and enhance the depth and space of the animation screen. Tone space conversion refers to the conversion or representation of color data in one color space into corresponding data in another color space. Its purpose is to distinguish and process color components such as hue and saturation in an image. This article first introduces the domestic and foreign research status of digital image preprocessing and analyzes the basic principles of several color space conversions in detail. Then, several color space conversion algorithms are studied, and the performance of the algorithms is compared and analyzed. The paper focuses on the hardware implementation and optimization of the algorithm for converting RGB color space into HSI color space to meet the real-time requirements. This article focuses on the mutual conversion between the RGB tone space and the HSI tone space and describes in detail how each color component in the HSI tone space is converted from the three RGB color components from a geometric perspective, and then the conversion is derived, and several general conversion methods of RGB to HSI tone space are introduced; two conversion methods of geometric derivation method and standard modulus algorithm are implemented in the software, and the comparison verification is carried out, and the comparison is made from the perspective of hardware implementation. The pros and cons of the two methods are discussed. Finally, the paper summarizes the shortcomings in the design and proposes further research directions in the future.
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Alonso Pérez, M. A., and J. J. Báez Rojas. "Conversion from n bands color space to HSI n color space." Optical Review 16, no. 2 (2009): 91–98. http://dx.doi.org/10.1007/s10043-009-0016-5.

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Li, Xin Wu. "Research on Color Space Conversion Model between XYZ and RGB." Key Engineering Materials 428-429 (January 2010): 466–69. http://dx.doi.org/10.4028/www.scientific.net/kem.428-429.466.

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Color space conversion for color digital camera is a key and difficult technique in the color reproduction information optics. A new color space conversion model based on subsectional fitting to correct color conversion error camera image is presented. First, color error sources and color rendering mechanism are analyzed in theory; then the paper takes standard color target for experimental sample and substitutes color blocks in color shade district for complete color space to solve the difficulties of experimental color blocks selecting; third the model uses subsectional fitting algorithm to built three dimension color conversion curve to correct color conversion error; Finally the experimental results show that the model can color space conversion accuracy of color digital camera and can be used in color conversion for digital camera practically.
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7

Seo, Jong Wan, and Myung Chul Shin. "Fast and Accurate Color Space Conversion Matrix." Key Engineering Materials 321-323 (October 2006): 1297–300. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.1297.

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A color space used to create color on a computer monitor or a television screen is RGB color space. However, RGB color space is strongly related to each other, therefore RGB color space is inadequate for adjustment of brightness or contrast. Moreover RGB color space is not suitable for pattern recognition. For this reason, it is needed that color space conversion from RGB to YIQ, YUV or YCrCb. The color space conversion matrix consists of 3 by 3 matrix element that is represented by floating point numbers. However RGB or YUV color space is in integer domain. Therefore these transform lead to lose the least significant bit (LSB) of color space. We propose the simple and fast reversible transform matrix. No lose the least significant bit (LSB) and not required multiplication but shift and addition that provides for real time conversion of huge image.
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8

Hua, Liang, Zhen Tao Zhou, Ji Yang, Hao Feng, Li Jun Ding, and Ju Ping Gu. "Fuzzy Enhancement Method for Color Medical Images Based on Color Space Conversion." Applied Mechanics and Materials 380-384 (August 2013): 3706–9. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3706.

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A new fuzzy enhancement method is put forward in the paper combining with Young-Helmholtz (Y-H) color space and fuzzy set theory. Color images with RGB tri-channels are transformed into Y-H color space by using Greaves transformation method. The colors image could be decomposed into chromaticity numbers matrix and intensity numbers matrix. The intensity numbers matrix is processed by using fuzzy enhancement arithmetic, while chromaticity numbers matrix keeps invariant. The primary chromaticity numbers matrix and enhanced intensity numbers matrix are processed by using Y-H inverse transformation. The method put forward in the paper have characteristics of efficiency, convenience and high speed. The method can achieve enhancement for color medical images without changing hue and saturation.
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9

Bi, Zhicheng, and Peng Cao. "Color space conversion algorithm and comparison study." Journal of Physics: Conference Series 1976, no. 1 (2021): 012008. http://dx.doi.org/10.1088/1742-6596/1976/1/012008.

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10

Amara, Mohamed, Fabien Mandorlo, Romain Couderc, Félix Gerenton, and Mustapha Lemiti. "Temperature and color management of silicon solar cells for building integrated photovoltaic." EPJ Photovoltaics 9 (2018): 1. http://dx.doi.org/10.1051/epjpv/2017008.

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Color management of integrated photovoltaics must meet two criteria of performance: provide maximum conversion efficiency and allow getting the chosen colors with an appropriate brightness, more particularly when using side by side solar cells of different colors. As the cooling conditions are not necessarily optimal, we need to take into account the influence of the heat transfer and temperature. In this article, we focus on the color space and brightness achieved by varying the antireflective properties of flat silicon solar cells. We demonstrate that taking into account the thermal effects allows freely choosing the color and adapting the brightness with a small impact on the conversion efficiency, except for dark blue solar cells. This behavior is especially true when heat exchange by convection is low. Our optical simulations show that the perceived color, for single layer ARC, is not varying with the position of the observer, whatever the chosen color. The use of a double layer ARC adds flexibility to tune the wanted color since the color space is greatly increased in the green and yellow directions. Last, choosing the accurate material allows both bright colors and high conversion efficiency at the same time.
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