
              
                # To install Skimage, delete the # (before the "!").
# !pip install scikit-image
 
## Loading libraries
    
import skimage # Image processing library
import skimage.io as io # Utility for reading and writing images in various formats (shortened to io).
import skimage.metrics # Utility allowing measurements between images.
import matplotlib.pyplot as plt # Library for plotting and visualizing data in graphical form (shortened to plt).
import numpy as np # Library intended to manipulate multidimensional matrices or tables.

## To find out the version of skimage

skimage.__version__ # warning to "." after skiimage


              
                ## Reading with the imread() function before displaying

HOW87TM4 = io.imread('data/how87tm4.tif')


              
                ## Displaying the number of rows, columns and number of channels (if applicable)
## Using the .shape function of the .io module

print(HOW87TM4.shape)

## If you want to dress up the output (text between '', then "," before the .shape function on the HOW87TM4 variable)

print('(number of rows, number of columns):', HOW87TM4.shape)


              
                ## Displaying information

print('type :', HOW87TM4.dtype )


              
                ## Displaying the image in gray level, using the imshow() function of the plt module:

plt.imshow(HOW87TM4, 'gray')

## To choose another color: https://matplotlib.org/stable/tutorials/colors/colormaps.html

## Write anything in place of 'gray', it generates an error message!
## At ValueError, in the list of colors, try others such as :
## viridis' (default color) or 'tab20c_r' to see the effect.
## Pay attention, it's case-sensitive (SHIFT/min)!


              
                ## Saving the compressed image in .jpg format

io.imsave('data/HOW87TM4.jpg', HOW87TM4, quality=50) # 50% compression


              
                ## Reading the image in .jpg format

JPEG4 = io.imread('data/HOW87TM4.jpg') # Reading the image assigned to the JPEG variable
plt.imshow(JPEG4, 'gray')


              
                ## Display of compressed image and difference image

plt.figure(figsize=(15, 5)) # display of images in two sub-figures (vary the size if necessary
plt.subplot(121) # display of multiple layouts in the same figure (121/122 layout on one line)
plt.title('JPEG4 (apparent luminances, 256 gray levels') # titling to identify the image
plt.imshow(JPEG4, 'gray') # display function in matplotlib.pyplot

## Add legend
plt.colorbar()

diff1 = np.absolute(HOW87TM4-JPEG4) # calculation (with numpy) the absolute value of the difference between the two images (origin - compressed)
plt.subplot(122) # display of multiple layouts in the same figure (121/122 layout on one line)
plt.imshow(diff1) # show the difference image between HOW87TM4 and JPEG4

## Add legend
plt.colorbar()

plt.title('TIFF - JPEG4 (deviation in val. absolute. of luminances app.)') # add a title to the sub-figure with the MSE value associated with the difference image
plt.show() # Not necessary here to display successive results stored in memory, but avoids unnecessary text above images (like: Text(0.5, 1.0, 'TIFF - JPEG4 Difference'))


              
                ## Calculation of the mean square error (MSE) between the two images


mse = skimage.metrics.mean_squared_error(HOW87TM4, JPEG4) # calculation of the difference between the two images with skimage.metrics via MSE

MSE = f'{mse:.2f}' # MSE value with two decimal places


print(f" Value of the MSE (mean square error) between the two images: {mse:.2f}")

#print('Value of the MSE (mean square error) between the two images:', MSE)


              
                ## Save compressed image in .png format

io.imsave('data/HOW87TM4.png', HOW87TM4) # no compression level is defined here


              
                ## Read image in .png format

PNG4 = io.imread('data/HOW87TM4.png') # read image assigned to PNG variable
plt.imshow(PNG4, 'gray')

## Add legend
plt.colorbar()


              
                ## Display of compressed image and difference image

plt.figure(figsize=(15, 5)) # displays images in two sub-figures (vary size if necessary)
plt.subplot(121) # display of multiple layouts in the same figure (121/122 layout on one line)
plt.title('PNG4 (apparent luminances, 256 grey levels') # titling to identify the image
plt.imshow(PNG4, 'gray') # display function in matplotlib.pyplot

## Add legend
plt.colorbar()

diff2 = np.absolute(HOW87TM4-PNG4) # calcul (avec numpy) la valeur absolue de la différence entre les deux images (origine - compressée)
plt.subplot(122) # display of several layouts in the same figure (121/122 = layout on a line)
plt.imshow(diff2) # displays image of differences between HOW87TM4 and PNG4

## Add legend
plt.colorbar()

plt.title('TIFF - PNG4 (difference in absolute luminance values).)') # add a title to the sub-figure with the MSE value associated with the difference image
plt.show() # Not necessary here to display successive results stored in memory, but avoids unnecessary text above images (like: Text(0.5, 1.0, 'Difference TIFF - PNG4').)


              
                ## Calculation of the mean square error (MSE) between the two images

mse = skimage.metrics.mean_squared_error(HOW87TM4, PNG4) # calculation of the difference between the two images with skimage.metrics via MSE

MSE = f'{mse:.2f}' # MSE value to two decimal places

#print(f" MSE (mean square error) value between the two images: {mse:.2f}")

print('MSE (mean square error) value between the two images:', MSE)

TD12: Image compression and loss of quality.¶

GIS and Remote Sensing - PhD curriculum¶

Debre Berhan University and University of Paris 8¶

Download required documents :

1. Loading data and libraries¶

1.1. Loading data¶

1.2. Loading libraries¶

2. Loading and displaying data¶

2.1. Loading data¶

2.2. Displaying image content¶

2.3. Displaying the HOW87TM4 image in grayscale¶

3. JPEG compression¶

3.1. Compressed image creation¶

3.2. Comparison of HOW87TM4 JPEG-compressed image with original TIFF image¶

3.2.1. Visual comparison¶

3.2.2. Statistical comparison¶

4. Comparison of the HOW87TM4 compressed PNG image with the original TIFF image¶

4.1. PNG compression¶

4.2. Comparison of the HOW87TM4 PNG-compressed image with the original TIFF image¶

4.2.1. Visual comparison¶

4.2.2. Statistical comparison¶

5. Would you be able to compress the HOW87TM3 image in JPEG and then in PNG and compare them successively with the original TIFF image?¶