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pythonjpeg

Why size of jpg file is bigger than expected?

I generate a grayscale image and save it in jpg format.

SCENE_WIDTH = 28
SCENE_HEIGHT = 28

# draw random noice
p, n = 0.5, SCENE_WIDTH*SCENE_HEIGHT
scene_noise = np.random.binomial(1, p, n).reshape((SCENE_WIDTH, SCENE_HEIGHT))*255
scene_noise = scene_noise.astype(np.uint8)

n = scene_noise
print('%d bytes' % (n.size * n.itemsize)) # 784 bytes

cv2.imwrite('scene_noise.jpg', scene_noise)
print('noise: ', os.path.getsize("scene_noise.jpg")) # 1549 bytes

from PIL import Image
im = Image.fromarray(scene_noise)
im.save('scene_noise2.jpg')
print('noise2: ', os.path.getsize("scene_noise2.jpg")) # 1017 bytes

when I change from:

scene_noise = np.random.binomial(1, p, n).reshape((SCENE_WIDTH, SCENE_HEIGHT))*255

to:

scene_noise = np.random.binomial(255, p, n).reshape((SCENE_WIDTH, SCENE_HEIGHT))

The size of file decrease almost 2 times: ~ 775 bytes.

Can you please explain why JPG file is bigger than the raw version and why the size decreases when I change colors from black and white to full grayscale spectrum?

cv2.__version__.split(".") # ['4', '1', '2']
Solution

  • Two things here:

    • can you explain why the JPEG file is bigger than the raw version?

    The size differs because you are not comparing the same things. The first object is a NumPy array, and the second one is a JPEG file. The JPEG file is bigger than the NumPy array (ie. after creating it with OpenCV) because JPEG encoding includes information in the overhead that a NumPy array does not store nor need.

    • can you explain why the size decreases when I change colours from black and white to a full grayscale spectrum?

    This is due to JPEG encoding. If you truly want to understand all of what happens, I highly suggest to understand how JPEG encoding works as I will not go into much detail about this (I am in no way a specialist in this topic). Information on this is well documented on the Wikipedia JPEG article. The general idea is that the more contrast you have in your picture, the bigger it will be in terms of size. Here, having a picture in black and white only will force you to always go between 0 and 255, whereas a grayscale picture will not usually see as big a change between adjacent pixels.