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Color fundamentals for digital imaging
Published in Sharma Gaurav, Digital Color Imaging Handbook, 2017
In our daily lives, color images surround us in print, television, computer displays, photographs, and movies. While these color images are taken for granted by a majority of readers and viewers, their production engages an entire industry of scientists, engineers, and practitioners. A knowledge of fundamental color principles is central to the work of this industry. The purpose of this chapter is to provide a concise introduction to some of these fundamentals of color science, colorimetry, color technology, and color systems. The presentation in the chapter is organized as a progressive introduction of principles from a logical rather than historical perspective. While suitable references and background material are included, the purpose is not to exhaustively document historical development of the principles or necessarily trace concepts to primary originators.
The Future
Published in Glenn Kennel, Charles S. Swartz, Color and Mastering for Digital Cinema, 2012
Glenn Kennel, Charles S. Swartz
Digital technology is being adopted throughout the whole film-making process. Many of these applications are evolutionary, replacing the traditional analog methods in one part of the process, while complementing the traditional workflow. Complementary applications that were adopted rapidly include non-linear editing, visual F/X and digital sound. Other changes, such as the digital distribution and exhibition of movies have much broader impacts, changing multiple parts of the process simultaneously, and impacting the whole industry. It is easy to view digital cinema as disruptive and risky. But it is also an opportunity to streamline the process and improve the quality of the cinema experience. The creative use of color is a powerful emotive tool. But to be used effectively, this color must be reproduced in a calibrated and consistent fashion on each and every screen. And this is where color science can be applied to support the art of movie-making.
Digital Image Formats
Published in Cliff Wootton, A Practical Guide to Video and Audio Compression, 2005
There is also a correlation among the range of colors that can be represented, what is available at the source, and what the human eye is able to resolve. In Figure 6-18 the luminance of a scene is mapped to the behavior of the human eye, and the range of available brightness values for 8-bit and HDR are both shown on the same axis. In this case the word luminance is the lighting and color science value and must not be confused with luma values discussed elsewhere.
Tutorial: Background and Guidance for Using the ANSI/IES TM-30 Method for Evaluating Light Source Color Rendition
Published in LEUKOS, 2022
Two classes of deficiencies with CRI and the broader CIE 13.3 method have been identified over decades of investigation. First, the underlying calculation framework – never updated despite advances in color science – relies on an inaccurate model of color vision that is officially considered obsolete by the CIE (2018) and a small set of color samples that are not capable of capturing the full effect of a light source. Second, CRI and the broader CIE 13.3 method only attempt to address color fidelity (see Section 3.8). Measures of average color fidelity do not convey how different colors change in appearance or distinguish between desirable and undesirable changes to color appearance, as was recognized even when CIE 13.2 was published in 1974. Combined, these technical and breadth deficiencies became an impediment to the development and specification of more efficient and higher quality solid-state lighting devices. When performance ratings do not match visual assessments, and when performance is not accurately distinguished by specification criteria, it creates unnecessary challenges for lighting producers and specifiers. TM-30 was developed to address this situation and provide a practical solution to a decades-old issue, enabling higher-quality lighting and reducing energy use.
Impact of Color-Matching Primaries on Observer Matching: Part I – Accuracy
Published in LEUKOS, 2022
Jiaye Li, Peter Hanselaer, Kevin A. G. Smet
Based on the CIE physiological observer CIEPO06, an extended individual colorimetric observer model was proposed by Asano et al. (Asano et al. 2016a) to simulate the CMFs of individual observers. In addition to observer age and field size, eight additional physiological parameters were included to account for individual differences with an average observer of a given age and for a specified field size. The eight parameters are: difference in lens pigment density, macular pigment density and optical densities, and peak wavelength shifts of the L, M, S cone photopigments. To simulate typical individual observers Asano (Asano et al. 2016a) identified the variability of each of the physiological parameters based on an extensive literature study. The model has been implemented in Matlab (Asano et al. 2016a) and in the LuxPy python toolbox for lighting and color science (Smet 2018). An advantage of the Individual Colorimetric Observer model of Asano is that it allows to estimate the expected observer variability in predicted visual matches by calculating the XYZ covariance matrix as proposed by Nimeroff (Nimeroff et al. 1961). It also allows to investigate the impact of observer CMF variability on all sorts of measures, such as the IES color fidelity index values (Murdoch and Fairchild 2019).