Optimize any image using techniques like chroma subsampling and optimized huffman coding etc.

Images we capture today, contains so much extra information that is not needed.
And also our human eye have some limitations.
So, removing what our eyes can't see is the basic idea.
Our eye is high sensitive to 'luma' than 'chroma'. So, according to that, image can be optimized.

The biggest advantage is image resolution is not changed during this optimization process.
Means if at first image is of size 1458 x 2592, then after optimization process, image resolution will be same 1458 x 2592.
But image size will be decresed or will remain same (if already optimized).

And also, image format is also kept same. png images will be kept png, jpeg images will be kept jpeg.

Make sure you have the following is installed:

• Python 3
• NumPy
• SciPy
• PIL (Python Image Library)

Give image path by command line argument.

python optimizer.py IMAGE_PATH

• Before size : 135 KB. Resolution : 1200 x 675
  

• Before size : 3358 KB. Resolution : 4208 x 2368
  

• Before size : 1482 KB. Resolution : 2592 x 1458
  

• Before size : 566 KB. Resolution : 2000 x 1125
  

Average time for optimizing a 1280 x 720 image is around 2 minutes. That is very long.
I will try to reduce that time.