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Among major deep learning (DL) applications, distributed learning involvingimage classification require effective image compression codecs deployed onlow-cost sensing devices for efficient transmission and storage. Traditionalcodecs such as JPEG designed for perceptual quality are not configured for DLtasks. This work introduces an integrative end-to-end trainable model for imagecompression and classification consisting of a JPEG image codec and a DL-basedclassifier. We demonstrate how this model can optimize the widely deployed JPEGcodec settings to improve classification accuracy in consideration of bandwidthconstraint. Our tests on CIFAR-100 and ImageNet also demonstrate improvedvalidation accuracy over preset JPEG configuration.

End-to-End Optimization of JPEG-Based Deep Learning Process for Image Classification