Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 Motivation
- 2 Book overview
- 3 Principles of lossless compression
- 4 Entropy coding techniques
- 5 Lossy compression of scalar sources
- 6 Coding of sources with memory
- 7 Mathematical transformations
- 8 Rate control in transform coding systems
- 9 Transform coding systems
- 10 Set partition coding
- 11 Subband/wavelet coding systems
- 12 Methods for lossless compression of images
- 13 Color and multi-component image and video coding
- 14 Distributed source coding
- Index
- References
13 - Color and multi-component image and video coding
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 Motivation
- 2 Book overview
- 3 Principles of lossless compression
- 4 Entropy coding techniques
- 5 Lossy compression of scalar sources
- 6 Coding of sources with memory
- 7 Mathematical transformations
- 8 Rate control in transform coding systems
- 9 Transform coding systems
- 10 Set partition coding
- 11 Subband/wavelet coding systems
- 12 Methods for lossless compression of images
- 13 Color and multi-component image and video coding
- 14 Distributed source coding
- Index
- References
Summary
Introduction
Due to advances in the technology of metrology and storage density, acquisition of images is now increasingly being practiced in more than two dimensions and in ever finer resolutions and in numerous scientific fields. Scientific data are being acquired, stored, and analyzed as images. Volume or three-dimensional images are generated in clinical medicine by CT or MRI scans of regions of the human body. Such an image can be regarded as a sequence of two-dimensional slice images of a bodily section. Tomographic methods in electron microscopy, for example, produce images in slices through the material being surveyed. The material can be a biological specimen or a materials microstructure. In remote sensing, a surface is illuminated with broad-spectrum radiation and the reflectance spectrum of each point on the surface is measured and recorded. In this way, a reflectance “image” of the surface is generated for a number of spectral bands. One uses the term multi-spectral imaging when the number of bands is relatively small, say less than 20, and the term hyper-spectral imaging when the number of bands is larger, usually hundreds. In either case, one can view the data either as a sequence of images or a single image of vector pixels. One particular kind of multi-spectral image is a color image, where the spectrum is in the range of visible wavelengths. Because of the properties of the human visual system, only three particular color images are needed to generate a visual response in the human viewer.
- Type
- Chapter
- Information
- Digital Signal CompressionPrinciples and Practice, pp. 373 - 397Publisher: Cambridge University PressPrint publication year: 2011