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

Source: [End-to-End Optimization of JPEG-Based Deep Learning Process for Image Classification](<https://www.sota2.com/research/paper/68fa7fed1e209131cea9b7fa>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:29.880Z

Authors: Siyu Qi, Lahiru D. Chamain, Zhi Ding

Published: 2023-08-10T19:38:41.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2308.05840>)

[PDF](<https://arxiv.org/pdf/2308.05840.pdf>)