# QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression

arXiv: https://arxiv.org/abs/2303.05744 · HTML: https://mlsanity.com/paper/2303.05744/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 20 of 20)

- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding](https://mlsanity.com/paper/2203.10886/) (2022)
- [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017)
- [CompressAI: a PyTorch library and evaluation platform for end-to-end compression research](https://mlsanity.com/paper/2011.03029/) (2020)
- [Channel-wise Autoregressive Entropy Models for Learned Image Compression](https://mlsanity.com/paper/2007.08739/) (2020)
- [Checkerboard Context Model for Efficient Learned Image Compression](https://mlsanity.com/paper/2103.15306/) (2021)
- [The Devil Is in the Details: Window-based Attention for Image Compression](https://mlsanity.com/paper/2203.08450/) (2022)
- [Variable Rate Deep Image Compression With a Conditional Autoencoder](https://mlsanity.com/paper/1909.04802/) (2019)
- [Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation](https://mlsanity.com/paper/2003.02012/) (2020)
- [Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform](https://mlsanity.com/paper/2108.09551/) (2021)
- [Joint Global and Local Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/2112.04487/) (2021)
- [Soft then Hard: Rethinking the Quantization in Neural Image Compression](https://mlsanity.com/paper/2104.05168/) (2021)
- [Learning Accurate Entropy Model with Global Reference for Image Compression](https://mlsanity.com/paper/2010.08321/) (2020)
- [Variable Rate Deep Image Compression with Modulated Autoencoder](https://mlsanity.com/paper/1912.05526/) (2019)
- [Interpolation variable rate image compression](https://mlsanity.com/paper/2109.09280/) (2021)
- [Rate Distortion Characteristic Modeling for Neural Image Compression](https://mlsanity.com/paper/2106.12954/) (2021)
- [Universal Efficient Variable-rate Neural Image Compression](https://mlsanity.com/paper/2111.11305/) (2021)
- [SADN: Learned Light Field Image Compression with Spatial-Angular Decorrelation](https://mlsanity.com/paper/2202.10837/) (2022)

## Built on by (most cited first, 11 of 11)

- [Variable-Rate Learned Image Compression with Multi-Objective Optimization and Quantization-Reconstruction Offsets](https://mlsanity.com/paper/2402.18930/) (2024)
- [Improving 3D Gaussian Splatting Compression by Scene-Adaptive Lattice Vector Quantization](https://mlsanity.com/paper/2509.13482/) (2025)
- [Compression Tells Intelligence: Visual Coding, Visual Token Technology, and the Unification](https://mlsanity.com/paper/2601.20742/) (2026)
- [Learned Compression of Encoding Distributions](https://mlsanity.com/paper/2406.13059/) (2024)
- [Training-Free Adaptive Quantization for Variable Rate Image Coding for Machines](https://mlsanity.com/paper/2511.05836/) (2025)
- [Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Framework](https://mlsanity.com/paper/2511.08915/) (2025)
- [Uni-LVC: A Unified Method for Intra- and Inter-Mode Learned Video Compression](https://mlsanity.com/paper/2603.05756/) (2026)
- [Variable Rate Image Compression via N-Gram Context based Swin-transformer](https://mlsanity.com/paper/2510.00058/) (2025)
- [TerraCodec: Compressing Optical Earth Observation Data](https://mlsanity.com/paper/2510.12670/) (2025)
- [MixCompress: Mixture of Experts for Variable Rate Learned Image Compression](https://mlsanity.com/paper/2607.14334/) (2026)
- [EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database](https://mlsanity.com/paper/2607.18187/) (2026)

## Abstract (arXiv)

Learned image compression has exhibited promising compression performance, but variable bitrates over a wide range remain a challenge. State-of-the-art variable rate methods compromise the loss of model performance and require numerous additional parameters. In this paper, we present a Quantization-error-aware Variable Rate Framework (QVRF) that utilizes a univariate quantization regulator a to achieve wide-range variable rates within a single model. Specifically, QVRF defines a quantization regulator vector coupled with predefined Lagrange multipliers to control quantization error of all latent representation for discrete variable rates. Additionally, the reparameterization method makes QVRF compatible with a round quantizer. Exhaustive experiments demonstrate that existing fixed-rate VAE-based methods equipped with QVRF can achieve wide-range continuous variable rates within a single model without significant performance degradation. Furthermore, QVRF outperforms contemporary variable-rate methods in rate-distortion performance with minimal additional parameters.