# Variable Rate Deep Image Compression with Modulated Autoencoder

arXiv: https://arxiv.org/abs/1912.05526 · HTML: https://mlsanity.com/paper/1912.05526/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 30 of 40)

- [Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform](https://mlsanity.com/paper/2108.09551/) (2021)
- [Slimmable Compressive Autoencoders for Practical Neural Image Compression](https://mlsanity.com/paper/2103.15726/) (2021)
- [DPICT: Deep Progressive Image Compression Using Trit-Planes](https://mlsanity.com/paper/2112.06334/) (2021)
- [Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving](https://mlsanity.com/paper/2310.00013/) (2023)
- [Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve](https://mlsanity.com/paper/2212.03905/) (2022)
- [Controlling Rate, Distortion, and Realism: Towards a Single Comprehensive Neural Image Compression Model](https://mlsanity.com/paper/2405.16817/) (2024)
- [VRVVC: Variable-Rate NeRF-Based Volumetric Video Compression](https://mlsanity.com/paper/2412.11362/) (2024)
- [ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Training](https://mlsanity.com/paper/2305.02145/) (2023)
- [Interpolation variable rate image compression](https://mlsanity.com/paper/2109.09280/) (2021)
- [QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression](https://mlsanity.com/paper/2303.05744/) (2023)
- [SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and Judger Mechanism](https://mlsanity.com/paper/2402.00321/) (2024)
- [Rate-Distortion-Cognition Controllable Versatile Neural Image Compression](https://mlsanity.com/paper/2407.11700/) (2024)
- [High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation](https://mlsanity.com/paper/2209.05054/) (2022)
- [UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach](https://mlsanity.com/paper/2503.18541/) (2025)
- [Joint Image Compression and Denoising via Latent-Space Scalability](https://mlsanity.com/paper/2205.01874/) (2022)
- [Prompt-ICM: A Unified Framework towards Image Coding for Machines with Task-driven Prompts](https://mlsanity.com/paper/2305.02578/) (2023)
- [Transformer-based Variable-rate Image Compression with Region-of-interest Control](https://mlsanity.com/paper/2305.10807/) (2023)
- [CBANet: Towards Complexity and Bitrate Adaptive Deep Image Compression using a Single Network](https://mlsanity.com/paper/2105.12386/) (2021)
- [Universal Efficient Variable-rate Neural Image Compression](https://mlsanity.com/paper/2111.11305/) (2021)
- [Deep Image Compression Using Scene Text Quality Assessment](https://mlsanity.com/paper/2305.11373/) (2023)
- [STanH : Parametric Quantization for Variable Rate Learned Image Compression](https://mlsanity.com/paper/2410.00557/) (2024)
- [Substitutional Neural Image Compression](https://mlsanity.com/paper/2105.07512/) (2021)
- [Online Meta Adaptation for Variable-Rate Learned Image Compression](https://mlsanity.com/paper/2111.08256/) (2021)
- [Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression](https://mlsanity.com/paper/2311.13846/) (2023)
- [Rate-Adaptive Quantization: A Multi-Rate Codebook Adaptation for Vector Quantization-based Generative Models](https://mlsanity.com/paper/2405.14222/) (2024)
- [Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization](https://mlsanity.com/paper/2501.02521/) (2025)
- [DANICE: Domain adaptation without forgetting in neural image compression](https://mlsanity.com/paper/2104.09370/) (2021)
- [Channel-wise Feature Decorrelation for Enhanced Learned Image Compression](https://mlsanity.com/paper/2403.10936/) (2024)
- [Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAV](https://mlsanity.com/paper/2410.04320/) (2024)
- [OMR-NET: a two-stage octave multi-scale residual network for screen content image compression](https://mlsanity.com/paper/2407.08545/) (2024)

## Abstract (arXiv)

Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods are optimized for a single fixed rate-distortion tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bit rates. Addressing these limitations, we formulate the problem of variable rate-distortion optimization for deep image compression, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific rate-distortion tradeoff via a modulation network. Jointly training this modulated autoencoder and modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters.