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Optimizing quantization tables for JPEG2000 codec with significant rate-accuracy performance.

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QuanNet: Joint Image Compression and Classification Over Channels with Limited Bandwidth

python project files for the paper - Quannet: Joint Image Compression and Classification Over Channels with Limited Bandwidth

  1. This project uses CIFAR-10 dataset and we provide the python code for the end to end training of QuanNet in keras.
  2. We use ResNet-20 as the classification network
  3. We use the following simple trainable layer for QuanNet. alt text

Installation

Download the python scripts from the repository. Install the following dependencies.

pip install PyWavelets

Training

python train.py

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{chamain2019quannet,
  title={Quannet: Joint Image Compression and Classification Over Channels with Limited Bandwidth},
  author={Chamain, Lahiru D and Cheung, Sen-ching Samson and Ding, Zhi},
  booktitle={2019 IEEE International Conference on Multimedia and Expo (ICME)},
  pages={338--343},
  year={2019},
  organization={IEEE}
}

Patent pending.

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Optimizing quantization tables for JPEG2000 codec with significant rate-accuracy performance.

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