# Differentiable bit-rate estimation for neural-based video codec enhancement

arXiv: https://arxiv.org/abs/2301.09776 · HTML: https://mlsanity.com/paper/2301.09776/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 24 Jan 2023.

## Builds on (most cited first, 6 of 6)

- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [Image and Video Compression with Neural Networks: A Review](https://mlsanity.com/paper/1904.03567/) (2019)
- [A Deep Journey into Super-resolution: A survey](https://mlsanity.com/paper/1904.07523/) (2019)
- [The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study](https://mlsanity.com/paper/2008.00605/) (2020)
- [Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies](https://mlsanity.com/paper/2101.06341/) (2021)

## Built on by (most cited first, 3 of 3)

- [Sandwiched Video Compression: Efficiently Extending the Reach of Standard Codecs with Neural Wrappers](https://mlsanity.com/paper/2303.11473/) (2023)
- [Versatile Recompression-Aware Perceptual Image Super-Resolution](https://mlsanity.com/paper/2511.18090/) (2025)
- [Differentiable Proxy Learning for Adaptive Quantization Control in H.264 Video Coding](https://mlsanity.com/paper/2607.10478/) (2026)

## Abstract (arXiv)

Neural networks (NN) can improve standard video compression by pre- and post-processing the encoded video. For optimal NN training, the standard codec needs to be replaced with a codec proxy that can provide derivatives of estimated bit-rate and distortion, which are used for gradient back-propagation. Since entropy coding of standard codecs is designed to take into account non-linear dependencies between transform coefficients, bit-rates cannot be well approximated with simple per-coefficient estimators. This paper presents a new approach for bit-rate estimation that is similar to the type employed in training end-to-end neural codecs, and able to efficiently take into account those statistical dependencies. It is defined from a mathematical model that provides closed-form formulas for the estimates and their gradients, reducing the computational complexity. Experimental results demonstrate the method's accuracy in estimating HEVC/H.265 codec bit-rates.