Introduction to Image Processing — Color Spaces
By: Kris Tabong
Tags: Color Space, Image Processing

Python is a fun programming language for exploring around with color spaces in images. I hope this story will give you some ideas on the basic types of color spaces. In this story, we will use Yuyuko as our sample.

Image: Touhouwiki.net

Let’s get started first with some important libraries in Python.

import numpy as np

import matplotlib.pyplot as plt

from skimage import img_as_uint

from skimage.color import rgb2hsv

from skimage.io import imshow, imread

from skimage.color import rgb2gray

from skimage.color import rgb2lab

RGB Model

The RGB Model is a color model that can produce various colors by “additive” combinations of the primary colors, red, green, and blue. The more you add colors, the lighter the color you get. This color mixing is used in everyday devices such as TV, camera, and phones. We can represent the image above as a combination of the RGB space model.

yuyuko = imread(“yuyuko.png”) fig, ax = plt.subplots(1, 3, figsize=(12,4))

ax[0].imshow(yuyuko[:,:,0], cmap=’Reds’)

ax[0].set_title(‘Red’)

ax[1].imshow(yuyuko[:,:,1], cmap=’Greens’)

ax[1].set_title(‘Green’)

ax[2].imshow(yuyuko[:,:,2], cmap=’Blues’)

ax[2].set_title(‘Blue’);

Images from Author

What I noticed at a glance about this is her blue ribbon. It was visible on the blue image, but not on the red and green images. The white background color on the original image is an equal combination of the three primary colors.

2. HSV Color Model

The HSV (Hue, Saturation, Value) model is conceptualized based on human vision. The hue represents the base pigment, the saturation represents the intensity of the color, and the value represents the brightness.

yuyuko_hsv = rgb2hsv(yuyuko)

fig, ax = plt.subplots(1, 4, figsize=(16,4))

ax[0].imshow(yuyuko_hsv[:,:,0], cmap=’hsv’)

ax[0].set_title(‘Hue’)

ax[0].axis(“off”)

ax[1].imshow(yuyuko_hsv[:,:,1], cmap=’gray’)

ax[1].set_title(‘Saturation’)

ax[1].axis(“off”)

ax[2].imshow(yuyuko_hsv[:,:,2], cmap=’gray’)

ax[2].set_title(‘Value’);

ax[2].axis(“off”)

ax[3].imshow(yuyuko)

ax[3].set_title(‘Original’)

ax[3].axis(“off”)

Images from Author

From this image, the background has a red hue, low saturation(black), and bright value. This combination produced a pure white background. The ribbon has a blue hue, a high value of saturation(white), and a somewhat dark value. This combination produced a deep blue ribbon.

3. CIEL*a*b* Color Space

The CIELAB model represents color based on three values: the perceptual luminosity(L*), color range from green to red (a*), and color range from blue to yellow (b*)

yuyuko_lab = rgb2lab(yuyuko)

fig, ax = plt.subplots(1, 4, figsize=(16,4))

ax[0].imshow(yuyuko_lab[:,:,0], cmap=’gray’)

ax[0].set_title(‘L*’)

ax[0].axis(“off”)

ax[1].imshow(yuyuko_lab[:,:,1], cmap=’RdYlGn_r’)

ax[1].set_title(‘a*’)

ax[1].axis(“off”)

ax[2].imshow(yuyuko_lab[:,:,2], cmap=’YlGnBu_r’)

ax[2].set_title(‘b*’);

ax[2].axis(“off”)

ax[3].imshow(yuyuko)

ax[3].set_title(‘Original’)

ax[3].axis(“off”)

Images from Author

Other Colors Space

Some types of color space are the YUV and the CMYK. The CMYK model (Cyan, Magenta, Yellow, Key) uses color subtraction in creating colors and usually applied to printers. The YUV is a color encoding system that is usually used on analog televisions. [1]

References:

[1]Zimet, L. (2002). Digital Processing of Analog Television. Department of Electrical Engineering, Stanford University