Deep Learning Image Compression

A curated collection of research papers and resources on learned/neural image compression.

Foundational Papers

These papers established the core architectures and techniques used in modern learned image compression.

Paper	Authors	Venue	Links
End-to-end Optimized Image Compression	Ballé, Laparra, Simoncelli	ICLR 2017	arXiv
Variational Image Compression with a Scale Hyperprior	Ballé et al.	ICLR 2018	arXiv
Joint Autoregressive and Hierarchical Priors for Learned Image Compression	Minnen, Ballé, Toderici	NeurIPS 2018	arXiv | GitHub
Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules	Cheng et al.	CVPR 2020	arXiv | GitHub

Transformer-Based Methods

Paper	Authors	Venue	Links
Transformer-based Transform Coding (SwinT-ChARM)	Zhu et al.	ICLR 2022	OpenReview | GitHub
Entroformer: A Transformer-based Entropy Model for Learned Image Compression	Qian et al.	ICLR 2022	OpenReview
Transformer-based Image Compression	Lu et al.	DCC 2022	arXiv | GitHub
Learned Image Compression with Mixed Transformer-CNN Architectures	Liu et al.	CVPR 2023	CVF | GitHub

Generative Methods (GAN / Diffusion)

Paper	Authors	Venue	Links
High-Fidelity Generative Image Compression (HiFiC)	Mentzer et al.	NeurIPS 2020	Project | PDF
Lossy Image Compression with Conditional Diffusion Models (CDC)	Yang et al.	NeurIPS 2023	arXiv | GitHub

Recent Advances (2024-2025)

Paper	Venue	Links
Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity	2024	arXiv
On Efficient Neural Network Architectures for Image Compression	2024	arXiv
Causal Context Adjustment Loss for Learned Image Compression	NeurIPS 2024	GitHub
EVC: Towards Real-Time Neural Image Compression with Mask Decay	2023	arXiv
WeConvene: Learned Image Compression with Wavelet-Domain Convolution and Entropy Model	ECCV 2024	GitHub
Enhanced Invertible Encoding for Learned Image Compression (InvCompress)	ACM MM 2021	GitHub

Key Libraries & Frameworks

Library	Description	Links
CompressAI	PyTorch library and evaluation platform for end-to-end compression research (InterDigital)	GitHub | Docs
tensorflow/compression	TensorFlow library for learned compression	GitHub
CompressAI-Trainer	Training platform for end-to-end compression models	GitHub

Paper Collections & Resources

Repository	Description
Awesome-Deep-Compression	Paper list of deep learning based image/video compression
Deep-Learning-Based-Image-Compression	Paper list about deep learning based image compression
Learned-Image-Video-Compression	Collection of papers related to data compression
Image-Compression-Papers-and-Code	Papers with code implementations

Review Papers

Paper	Links
Image and Video Compression with Neural Networks: A Review	arXiv
Deep Architectures for Image Compression: A Critical Review	ScienceDirect
Information Compression in the AI Era: Recent Advances and Future Challenges	arXiv

Implementation Note: Fabric vs. PyTorch Lightning

We have chosen to use Lightning Fabric over PyTorch Lightning for this project.

• Why Fabric? Fabric allows for a "build-your-own-loop" approach, which is essential for the custom training requirements of neural compression (e.g., separate auxiliary loss optimization steps, custom rate-distortion loss handling). It provides the necessary control without the "magic" or restrictive structure of a full LightningModule.
• Trade-offs: Fabric does not have a built-in callback system (like EarlyStopping or ModelCheckpoint). These features must be implemented manually in the training loop, as seen in tinify/cli/train.py.
• Decision: Stick with Fabric for the transparency and granular control it offers, which outweighs the convenience of pre-built callbacks for this specific use case.