This repo contains two compression algorithms in python3:
- Color coding using huffman codes
- A custom jpeg implementation
To perform color coding compression use color_code.py
usage: color_code.py [-h] [--verbose] {encode,decode} path_in path_out
positional arguments:
{encode,decode} encode or decode an image
path_in path to input
path_out path for saving output
optional arguments:
-h, --help show this help message and exit
--verbose print encoding statistics
To perform jpeg compression use jpeg_code.py
usage: jpeg_code.py [-h] [--verbose] {encode,decode} ... path_in path_out
positional arguments:
{encode,decode} encode or decode an image
path_in path to input
path_out path for saving output
optional arguments:
-h, --help show this help message and exit
--verbose print encoding statistics
The JPEG encoding also allows for some optional parameters that control the level of compression. Higher compression results in a smaller file size but may lead to noticable artifacts.
usage: jpeg_code.py encode [-h] [--quant QUANT] [--sub_rows SUB_ROWS] [--sub_columns SUB_COLUMNS]
optional arguments:
-h, --help show this help message and exit
--quant QUANT level of quantization applied to the image. Lower values indicate less quantization.
Use integers ranging from -60 to 30 for good results
--sub_rows SUB_ROWS chrominance subsampling in the direction of the rows
--sub_columns SUB_COLUMNS
chrominance subsampling in the direction of the columns
quant is the most important one. The default value is -45. You might want to try out a few quantization levels to see how much quantizaton affects your image.
Besides the compression algorithms, there are also a couple of interesting things connected to the blog post. For example:
- Testing compression performance with picsum images
- Decomposing an image into its cosine transform components
- Projecting an image into a random orthogonal basis (and making a nice animation with it).