# RAWtoBit: A Fully End-to-end Camera ISP Network

arXiv: https://arxiv.org/abs/2208.07639 · HTML: https://mlsanity.com/paper/2208.07639/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2022.

## Built on by (most cited first, 6 of 6)

- [Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs](https://mlsanity.com/paper/2404.10700/) (2024)
- [Goal Conditioned Reinforcement Learning for Photo Finishing Tuning](https://mlsanity.com/paper/2503.07300/) (2025)
- [Make Lossy Compression Meaningful for Low-Light Images](https://mlsanity.com/paper/2305.15030/) (2023)
- [An End-to-End Real-World Camera Imaging Pipeline](https://mlsanity.com/paper/2411.10773/) (2024)
- [Experts-Guided Unbalanced Optimal Transport for ISP Learning from Unpaired and/or Paired Data](https://mlsanity.com/paper/2512.05635/) (2025)
- [Learning to Sense for Driving: Joint Optics-Sensor-Model Co-Design for Semantic Segmentation](https://mlsanity.com/paper/2512.20815/) (2025)

## Abstract (arXiv)

Image compression is an essential and last processing unit in the camera image signal processing (ISP) pipeline. While many studies have been made to replace the conventional ISP pipeline with a single end-to-end optimized deep learning model, image compression is barely considered as a part of the model. In this paper, we investigate the designing of a fully end-to-end optimized camera ISP incorporating image compression. To this end, we propose RAWtoBit network (RBN) that can effectively perform both tasks simultaneously. RBN is further improved with a novel knowledge distillation scheme by introducing two teacher networks specialized in each task. Extensive experiments demonstrate that our proposed method significantly outperforms alternative approaches in terms of rate-distortion trade-off.