# Deep Convolutional AutoEncoder-based Lossy Image Compression

arXiv: https://arxiv.org/abs/1804.09535 · HTML: https://mlsanity.com/paper/1804.09535/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 30 of 42)

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- [Learning-Driven Lossy Image Compression; A Comprehensive Survey](https://mlsanity.com/paper/2201.09240/) (2022)
- [A practical existence theorem for reduced order models based on convolutional autoencoders](https://mlsanity.com/paper/2402.00435/) (2024)
- [Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression](https://mlsanity.com/paper/2207.12348/) (2022)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Reducing Redundancy in the Bottleneck Representation of the Autoencoders](https://mlsanity.com/paper/2202.04629/) (2022)
- [A Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN Computing](https://mlsanity.com/paper/2208.11596/) (2022)
- [Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising](https://mlsanity.com/paper/2305.03485/) (2023)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Video Coding Using Learned Latent GAN Compression](https://mlsanity.com/paper/2207.04324/) (2022)
- [Sparse L1-Autoencoders for Scientific Data Compression](https://mlsanity.com/paper/2405.14270/) (2024)
- [A New Image Codec Paradigm for Human and Machine Uses](https://mlsanity.com/paper/2112.10071/) (2021)
- [Learning sparse auto-encoders for green AI image coding](https://mlsanity.com/paper/2209.04448/) (2022)
- [Image Generative Semantic Communication with Multi-Modal Similarity Estimation for Resource-Limited Networks](https://mlsanity.com/paper/2404.11280/) (2024)
- [Subjective Visual Quality Assessment for High-Fidelity Learning-Based Image Compression](https://mlsanity.com/paper/2504.06301/) (2025)
- [Streaming-capable High-performance Architecture of Learned Image Compression Codecs](https://mlsanity.com/paper/2208.01641/) (2022)
- [An autoencoder compression approach for accelerating large-scale inverse problems](https://mlsanity.com/paper/2304.04781/) (2023)
- [PatchSVD: A Non-uniform SVD-based Image Compression Algorithm](https://mlsanity.com/paper/2406.05129/) (2024)
- [Region of Interest Loss for Anonymizing Learned Image Compression](https://mlsanity.com/paper/2406.05726/) (2024)
- [Physics-Informed GCN-LSTM Framework for Long-Term Forecasting of 2D and 3D Microstructure Evolution](https://mlsanity.com/paper/2509.15029/) (2025)
- [OpenViGA: Video Generation for Automotive Driving Scenes by Streamlining and Fine-Tuning Open Source Models with Public Data](https://mlsanity.com/paper/2509.15479/) (2025)
- [Fitbeat: COVID-19 Estimation based on Wristband Heart Rate](https://mlsanity.com/paper/2104.09263/) (2021)
- [Data-driven modeling of time-domain induced polarization](https://mlsanity.com/paper/2107.14796/) (2021)
- [Semantic-assisted image compression](https://mlsanity.com/paper/2201.12599/) (2022)
- [Image Compression and Actionable Intelligence With Deep Neural Networks](https://mlsanity.com/paper/2203.13686/) (2022)

## Abstract (arXiv)

Image compression has been investigated as a fundamental research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compression. In this paper, we present a lossy image compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to achieve a high coding efficiency. First, we design a novel CAE architecture to replace the conventional transforms and train this CAE using a rate-distortion loss function. Second, to generate a more energy-compact representation, we utilize the principal components analysis (PCA) to rotate the feature maps produced by the CAE, and then apply the quantization and entropy coder to generate the codes. Experimental results demonstrate that our method outperforms traditional image coding algorithms, by achieving a 13.7% BD-rate decrement on the Kodak database images compared to JPEG2000. Besides, our method maintains a moderate complexity similar to JPEG2000.