Among major deep learning (DL) applications, distributed learning involving image classification require effective image compression codecs deployed on low-cost sensing devices for efficient transmission and storage. Traditional codecs such as JPEG designed for perceptual quality are not configured for DL tasks. This work introduces an integrative end-to-end trainable model for image compression and classification consisting of a JPEG image codec and a DL-based classifier. We demonstrate how this model can optimize the widely deployed JPEG codec settings to improve classification accuracy in consideration of bandwidth constraint. Our tests on CIFAR-100 and ImageNet also demonstrate improved validation accuracy over preset JPEG configuration.
@article{arxiv.2308.05840,
title = {End-to-End Optimization of JPEG-Based Deep Learning Process for Image Classification},
author = {Siyu Qi and Lahiru D. Chamain and Zhi Ding},
journal= {arXiv preprint arXiv:2308.05840},
year = {2023}
}