High-Dynamic-Range Color Space

2013 ◽  
pp. 399-417
Keyword(s):  
Dynamic Range ◽  
Color Space ◽  
High Dynamic Range ◽  
High Dynamic

Gamut mapping in a high-dynamic-range color space

2014 ◽  
Author(s):  
Jens Preiss ◽  
Mark D. Fairchild ◽  
James A. Ferwerda ◽  
Philipp Urban
Keyword(s):  
Dynamic Range ◽  
Color Space ◽  
High Dynamic Range ◽  
Gamut Mapping ◽  
High Dynamic

High dynamic range algorithm based on HSI color space

2014 ◽  
Author(s):  
Jiancheng Zhang ◽  
Xiaohua Liu ◽  
Liquan Dong ◽  
Yuejin Zhao ◽  
Ming Liu
Keyword(s):  
Dynamic Range ◽  
Color Space ◽  
High Dynamic Range ◽  
Hsi Color Space ◽  
High Dynamic

Comparing common still image quality metrics in recent High Dynamic Range (HDR) and Wide Color Gamut (WCG) representations

2020 ◽  
Vol 2020 (9) ◽  
pp. 214-1-214-9
Author(s):  
Anustup Choudhury ◽  
Scott Daly
Keyword(s):  
Video Compression ◽  
Dynamic Range ◽  
Color Space ◽  
High Dynamic Range ◽  
Quality Metrics ◽  
Subjective Quality ◽  
Tone Mapping ◽  
Color Gamut ◽  
Wide Color Gamut ◽  
High Dynamic

There are an increasing number of databases describing subjective quality responses for HDR (high dynamic range) imagery with various distortions. The dominant distortions across the databases are those that arise from video compression, which are primarily perceived as achromatic, but there are some chromatic distortions due to 422 and other chromatic sub-sampling. Tone mapping from the source HDR levels to various levels of reduced capability SDR (standard dynamic range) are also included in these databases. While most of these distortions are achromatic, tone-mapping can cause changes in saturation and hue angle when saturated colors are in the upper hull of the of the color space. In addition, there is one database that specifically looked at color distortions in an HDR-WCG (wide color gamut) space. From these databases we can test the improvements to well-known quality metrics if they are applied in the newly developed color perceptual spaces (i.e., representations) specifically designed for HDR and WCG. We present results from testing these subjective quality databases to computed quality using the new color spaces of Jzazbz and ICTCP, as well as the commonly used SDR color space of CIELAB.

scholarly journals Perceptually uniform color space for image signals including high dynamic range and wide gamut

2017 ◽  
Vol 25 (13) ◽  
pp. 15131 ◽  
Author(s):  
Muhammad Safdar ◽  
Guihua Cui ◽  
Youn Jin Kim ◽  
Ming Ronnier Luo
Keyword(s):  
Dynamic Range ◽  
Color Space ◽  
High Dynamic Range ◽  
High Dynamic

Towards a Perceptually-Motivated Color Space for High Dynamic Range Imaging

2017 ◽  
Vol 2017 (18) ◽  
pp. 48-59
Author(s):  
Mekides Abebe ◽  
Tania Pouli ◽  
Mohamed-Chaker Larabi
Keyword(s):  
Dynamic Range ◽  
Color Space ◽  
High Dynamic Range ◽  
High Dynamic Range Imaging ◽  
Range Imaging ◽  
High Dynamic

scholarly journals High Dynamic Range Color Image Enhancement Using Fuzzy Logic and Bacterial Foraging

2011 ◽  
Vol 61 (5) ◽  
pp. 462 ◽  
Author(s):  
Om Prakash Verma ◽  
V. K. Madasu ◽  
V. Shantaram
Keyword(s):  
Dynamic Range ◽  
Color Image ◽  
Color Space ◽  
High Dynamic Range ◽  
Hyperbolic Function ◽  
Main Concern ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Degraded Image ◽  
High Dynamic

<p>High dynamic range images contain both the underexposed and the overexposed regions. The enhancement of the underexposed and the overexposed regions is the main concern of this paper. Two new transformation functions are proposed to modify the fuzzy membership values of under and the overexposed regions of an image respectively.For the overexposed regions, a rectangular hyperbolic function is used while for the underexposed regions, an S-function is applied. The shape and range of these functions can be controlled by the parameters involved, which are optimized using the bacterial foraging optimization algorithm so as to obtain the enhanced image. The hue, saturation, and intensity (HSV) color space is employed for the purpose of enhancement, where the hue component is preserved to keep the original color composition intact. This approach is applicable to a degraded image of mixed type. On comparison, the proposed transforms yield better results than the existing transformation functions17 for both the underexposed and the overexposed regions.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.462-472</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.1184</strong></strong></p>

scholarly journals Uniform Color Space-Based High Dynamic Range Video Compression

2019 ◽  
Vol 29 (7) ◽  
pp. 2055-2066 ◽  
Author(s):  
Ratnajit Mukherjee ◽  
Kurt Debattista ◽  
Thomas-Bashford Rogers ◽  
Maximino Bessa ◽  
Alan Chalmers
Keyword(s):  
Video Compression ◽  
Dynamic Range ◽  
Color Space ◽  
High Dynamic Range ◽  
High Dynamic ◽  
High Dynamic Range Video

scholarly journals A Practical Device for Measuring the Luminance Distribution

2017 ◽  
Vol 19 (1) ◽  
pp. 75-90 ◽  
Author(s):  
Thijs Kruisselbrink ◽  
Myriam Aries ◽  
Alexander Rosemann
Keyword(s):  
Credit Card ◽  
Dynamic Range ◽  
Color Space ◽  
High Dynamic Range ◽  
Measurement Device ◽  
Camera System ◽  
Worst Case ◽  
Distribution Measurement ◽  
Luminance Distribution ◽  
High Dynamic

Various applications in building lighting such as automated daylight systems, dynamic lighting control systems, lighting simulations, and glare analyzes can be optimized using information on the actual luminance distributions of the surroundings. Currently, commercially available luminance distribution measurement devices are often not suitable for these kind of applications or simply too expensive for broad application. This paper describes the development of a practical and autonomous luminance distribution measurement device based on a credit card-sized single-board computer and a camera system. The luminance distribution was determined by capturing High Dynamic Range images and translating the RGB information to the CIE XYZ color space. The High Dynamic Range technology was essential to accurately capture the data needed to calculate the luminance distribution because it allows to capture luminance ranges occurring in real scenarios. The measurement results were represented in accordance with established methods in the field of daylighting. Measurements showed that the accuracy of the luminance distribution measurement device ranged from 5% to 20% (worst case) which was deemed acceptable for practical measurements and broad applications in the building realm.

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