You can use newer OpenCV python interface (if I'm not mistaken it is available since OpenCV 2.2). It natively uses numpy arrays:

import cv2
im = cv2.imread("abc.tiff")
print(type(im))

result:

<type 'numpy.ndarray'>

I created a 2x2 JPEG image to test this. The image has white, red, green and purple pixels. I used cv2.imdecode and numpy.frombuffer

import cv2
import numpy as np

f = open('image.jpg', 'rb')
image_bytes = f.read()  # b'\xff\xd8\xff\xe0\x00\x10...'

decoded = cv2.imdecode(np.frombuffer(image_bytes, np.uint8), -1)

print('OpenCV:\n', decoded)

# your Pillow code
import io
from PIL import Image
image = np.array(Image.open(io.BytesIO(image_bytes))) 
print('PIL:\n', image)

This seems to work, although the channel order is BGR and not RGB as in PIL.Image. There are probably some flags you might use to tune this. Test results:

OpenCV:
 [[[255 254 255]
  [  0   0 254]]

 [[  1 255   0]
  [254   0 255]]]
PIL:
 [[[255 254 255]
  [254   0   0]]

 [[  0 255   1]
  [255   0 254]]]

Use the multiprocessing pool to load data parallely. In my PC the cpus count is 16. I tried loading 100 images and below you could see the time taken.

import multiprocessing
import cv2
import glob
from time import time 

def load_image(image_path):
    return cv2.imread(image_path)

if __name__ == '__main__':
    image_path_list = glob.glob('*.png')
        
        
    try:
        cpus = multiprocessing.cpu_count()
    except NotImplementedError:
        cpus = 2   # arbitrary default
    
    pool = multiprocessing.Pool(processes=cpus)
    
    start = time()
    images = pool.map(load_image, image_path_list)
    print("Total time taken using multiprocessing pool {} seconds".format (time() - start))
    
    images = []
    start = time()
    for image_path in image_path_list:
        images.append(load_image(image_path))
    print("Total time taken using for loop {} seconds".format (time() - start))
    
    
    start = time()
    images = [load_image(image_path) for image_path in image_path_list]        
    print("Total time taken using list comprehension {} seconds".format (time() - start))

Output:

Total time taken using multiprocessing pool 0.2922379970550537 seconds
Total time taken using for loop 1.4935636520385742 seconds
Total time taken using list comprehension 1.4925990104675293 seconds