Introduction to Image Processing Using R pp 21-29 | Cite as
Image Data Formats and Color Representation
Abstract
This chapter starts with a brief introduction about color representation. The nature of the light reflected by an object, along with its optical characteristics and the human perception, is responsible for its appearance. The light is characterized by the attributes of intensity, radiance, luminance, and brightness. Colors are electromagnetic waves described by their wavelength and they are considered to be formed from different combinations of the primary colors red, green, and blue. The color depth measures the amount of color information available to display or print each pixel of a digital image. The higher the color depth, the more accurate the color representation. The most used color models are RGB, CMYK, and HSV. The first one is usually used for representing colors in electronic devices as TV and computer monitors, scanners, and digital cameras. The CMYK space is usually used by printers and photocopiers, while the HSV model is used in artificial vision systems. The two main classes used to store images, vector, and raster, are also presented in this chapter. In a vector format the image is described by geometrical primitives, while in a raster format it is described by a matrix of values. Some common vector formats are PDF, PostScript and SVG, and the most used raster formats are TIFF, JPEG, PNG, BMP, and PBM. Procedures on how to read and write graphics in the formats presented using R are presented in the next chapter.
Keywords
Color Space Joint Photographic Expert Group Color Representation Portable Document Format Color DepthReferences
- Adobe Systems Inc. (1999). Postscript language reference manual (3rd ed.). Reading, MA: Addison Wesley.Google Scholar
- Adobe Systems Inc. (2004). PDF reference version 1.6 (5th ed.). Berkeley, CA: Adobe Press.Google Scholar
- Fairchild, M. D. (1997). Color appearance models. Reading, MA: Addison-Wesley-Longman.Google Scholar
- Ferraiolo, D. J. J. (2003). Scalable vector graphics (svg) 1.1 specification. W3C Recommendation. http://www.w3.org/TR/SVG/.
- Fortner, B. & Meyer, T. E. (1996). Number by colors—A guide to using color to understand technical data. Heidelberg: Springer.Google Scholar
- Gonzalez, R. C., & Woods, R. E. (1992). Digital image processing. Reading, MA: Addison-Wesley.Google Scholar
- Henderson, B. (1993). Netpbm home page. http://netpbm.sourceforge.net/.
- Malacara, D. (2011). Color vision and colorimetry: Theory and applications. Press monograph, SPIE. http://books.google.com.br/books?id=xDU4YgEACAAJ.
- Murray, J. E., & Ryper, W. V. (1994). Encyclopedia of graphic file formats. Cambridge: O’Reilly.Google Scholar
- Murrell, P. (2011). Raster images in R graphics. The R Journal, 3(1), 48–54. http://journal.r-project.org/archive/2011-1/RJournal_2011-1_Murrell.pdf.Google Scholar
- Nelson, M. R. (1989). LZW data compression. Dr Dobbs Journal, 14(10), 29–36.Google Scholar
- Smith, A. R. (1978). Color gamut transform pairs. SIGGRAPH Computer Graphics, 12(3), 12–19. http://doi.acm.org/10.1145/965139.807361.
- Velho, Z., Frery, A. C. & Miranda, J. (2008). Image processing for computer graphics and vision (2nd ed.). London: Springer.Google Scholar