# DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework

arXiv: https://arxiv.org/abs/1803.05788 · HTML: https://mlsanity.com/paper/1803.05788/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 23 of 23)

- [Lossy Compression for Lossless Prediction](https://mlsanity.com/paper/2106.10800/) (2021)
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- [3D-GSW: 3D Gaussian Splatting for Robust Watermarking](https://mlsanity.com/paper/2409.13222/) (2024)
- [Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AI](https://mlsanity.com/paper/2312.14229/) (2023)
- [Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks](https://mlsanity.com/paper/2201.10162/) (2022)
- [Memory-Efficient CNN Accelerator Based on Interlayer Feature Map Compression](https://mlsanity.com/paper/2110.06155/) (2021)
- [Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Prior](https://mlsanity.com/paper/2511.22549/) (2025)
- [Task Oriented Video Coding: A Survey](https://mlsanity.com/paper/2208.07313/) (2022)
- [JPEG Inspired Deep Learning](https://mlsanity.com/paper/2410.07081/) (2024)
- [Countering Adversarial Examples: Combining Input Transformation and Noisy Training](https://mlsanity.com/paper/2106.13394/) (2021)
- [Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions](https://mlsanity.com/paper/2108.13055/) (2021)
- [Picking Up Quantization Steps for Compressed Image Classification](https://mlsanity.com/paper/2304.10714/) (2023)
- [Dense Pruning of Pointwise Convolutions in the Frequency Domain](https://mlsanity.com/paper/2109.07707/) (2021)
- [Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems](https://mlsanity.com/paper/2201.00063/) (2021)
- [Learned Lossless Compression for JPEG via Frequency-Domain Prediction](https://mlsanity.com/paper/2303.02666/) (2023)
- [ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layer](https://mlsanity.com/paper/2105.02019/) (2021)
- [TACTIC: Joint Rate-Distortion-Accuracy Optimisation for Low Bitrate Compression](https://mlsanity.com/paper/2109.10658/) (2021)
- [Modeling Image Quantization Tradeoffs for Optimal Compression](https://mlsanity.com/paper/2112.07207/) (2021)
- [XAI based Performance Preserving Adaptive Image Compression for Efficient Satellite Communication](https://mlsanity.com/paper/2207.10885/) (2022)
- [Pruned Lightweight Encoders for Computer Vision](https://mlsanity.com/paper/2211.13137/) (2022)
- [Deep Selector-JPEG: Adaptive JPEG Image Compression for Computer Vision in Image classification with Human Vision Criteria](https://mlsanity.com/paper/2302.09560/) (2023)
- [JPEG Compliant Compression for Both Human and Machine, A Report](https://mlsanity.com/paper/2503.10912/) (2025)
- [Learned JPEG Compression for DNN Vision](https://mlsanity.com/paper/2606.16185/) (2026)

## Abstract (arXiv)

As one of most fascinating machine learning techniques, deep neural network (DNN) has demonstrated excellent performance in various intelligent tasks such as image classification. DNN achieves such performance, to a large extent, by performing expensive training over huge volumes of training data. To reduce the data storage and transfer overhead in smart resource-limited Internet-of-Thing (IoT) systems, effective data compression is a "must-have" feature before transferring real-time produced dataset for training or classification. While there have been many well-known image compression approaches (such as JPEG), we for the first time find that a human-visual based image compression approach such as JPEG compression is not an optimized solution for DNN systems, especially with high compression ratios. To this end, we develop an image compression framework tailored for DNN applications, named "DeepN-JPEG", to embrace the nature of deep cascaded information process mechanism of DNN architecture. Extensive experiments, based on "ImageNet" dataset with various state-of-the-art DNNs, show that "DeepN-JPEG" can achieve ~3.5x higher compression rate over the popular JPEG solution while maintaining the same accuracy level for image recognition, demonstrating its great potential of storage and power efficiency in DNN-based smart IoT system design.