6.Color Image processing

6.1 Color fundamentals

6.2 Color models

6.2.1 The RGB color model -RGB color pixel is said to have a depth of 24 bits ( 3 image planes times the number of bits per plane )

6.2.2 The CMY and CMYK color models -Most devices require CMY data -CMYK is “full color printing”

6.2.3 The HSI color model -Intensity (gray level) is most useful descriptor of monochromatic images -HSI(hue, saturation,intensity) color model decouples the intensity component from the color carrying information( hue and saturation) in image

6.3 Pseudocolor Image processing

6.3.1 Intensity slicing -The technique of intensity slicing and color coding is one of the simplest examples of pseudocolor image processing

6.3.2 Intensity to color transformations - It perform three independent transformations on the intensity of any input pixel. -The three results are then fed separately into the red,green and blue channels of a color television monitor.

6.4 Basics of full color image processing

6.5 Color transformations

6.5.2 Color Complements -The color complements are useful for enhancing detail that is embedded in dark regions of color image

6.5.3 Color Slicing -Highlighting a specific range of colors in an image is useful for separating objects from their surrounding. -One of the simplest ways to slice a color image is to map the colors outside some range of interest to a nonprominent neutral color

6.5.4 Tone and Color correction -It is useful in photo enhancement and color reproduction

6.5.5 Histogram processing -Histogram equalization is common when workinig with the intensity component in HSI space because changes in intensity affect the relatve appearance of color in an image

6.6 Smoothing and Sharpening

6.6.1 Color Image Smoothing -Each pixel is replaced by average of the pixels in the neighborhood defined by the mask

6.6.2 Color Image Sharpening -It use Laplacian that vector is defined as a vector whose components are equal to the Laplacian of the individual scalar components of the input vector

6.7 Image segmentation based on color

6.7.1 Segmentation in HSI Color space -Saturation is used as a masking image in order to isolate further regions of interest in the hue image

6.7.2 Segmentation in RGB vector space -Segmentation is one area in which better results are obtained by using RGB color vectors -The objective is to segment objects of a specified color range in an RGB image

6.7.3 Color edge detection -It is a tool for image segmentation in color vector space by computing edges on an individual image basis versus computing edges

6.8 Noise in color images

-The noise content of a color image has the same characteristics in each color channel

6.9 Color image compression

-Data compression plays a central role in the storage and transmission of color images because the number of bits required to represent color is three to four times grater than the number employed in the represenation of gray level