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

2023/08/10 by Siyu Qi, Qi, Siyu, Lahiru D. Chamain +3
Computer Science · #Advanced Data Compression Techniques #Advanced Image and Video Retrieval Techniques #Image and Signal Denoising Methods

paper · pdf · doi:10.48550/arxiv.2308.05840

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.

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