Let's use the PIL Image module's convert() function for color quantization, with the P mode and the color argument as the maximum number of possible colors. We'll also use the SciPy stats module's signaltonoise() function to find the Signal-to-Noise Ratio (SNR) of an image (parrot.jpg), which is defined as the mean divided by the standard deviation of the image array:
im = Image.open('../images/parrot.jpg')pylab.figure(figsize=(20,30))num_colors_list = [1 << n for n in range(8,0,-1)]snr_list = []i = 1for num_colors in num_colors_list: im1 = im.convert('P', palette=Image.ADAPTIVE, colors=num_colors) pylab.subplot(4,2,i), pylab.imshow(im1), pylab.axis('off') snr_list.append(signaltonoise(im1, axis=None)) pylab.title('Image ...
