Scalable Image Coding for Humans and Machines

Image Coding for Machines

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This is the official implementation of the following papers.

・Image Coding for Machines with Edge Information Learning Using Segment Anything (arXiv)

・Scalable Image Coding for Humans and Machines Using Feature Fusion Network (arXiv)

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Installation

1. Clone this repository from GitHub.

git clone https://github.com/final-0/ICM-v1.git

2. Change the current directory to the "ICM-v1" folder.

cd ICM-v1/

3. Install the required dependencies in the current directory.

pip3 install -r requirements.txt 

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Recommended Specs

For testing only, a GPU with about 11GB of memory is sufficient. (e.g. 1080ti, 2080ti)

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Usage

Download model checkpoints. You can obtain "ica.pth.tar" and "icm.pth.tar" from this link. These checkpoints can be used by placing them in the "param" folder.

param ---- param_details.txt
       |-- icm.pth.tar (image compression model for Machines)
       |-- ica.pth.tar (additional information compression model)

If you want to compress images for "Machines", run the following command :

python3 coding_m.py --checkpoint param/icm.pth.tar --input image/input

Add “--real” to the command to obtain a bit-stream:

python3 coding_m.py --checkpoint param/icm.pth.tar --input image/input --real

If you want to compress images for "Humans" & "Machines", run the following command :

python3 coding_hm.py --checkpoint_m param/icm.pth.tar --checkpoint_a param/ica.pth.tar --input image/input

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Reconstructed Samples