# TACTIC: Joint Rate-Distortion-Accuracy Optimisation for Low Bitrate Compression

arXiv: https://arxiv.org/abs/2109.10658 · HTML: https://mlsanity.com/paper/2109.10658/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 24 references that are in the map.
- First posted on arXiv 22 Sep 2021.

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

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [High-Fidelity Generative Image Compression](https://mlsanity.com/paper/2006.09965/) (2020)
- [Generative Adversarial Networks for Extreme Learned Image Compression](https://mlsanity.com/paper/1804.02958/) (2018)
- [Real-Time Adaptive Image Compression](https://mlsanity.com/paper/1705.05823/) (2017)
- [Deep Generative Models for Distribution-Preserving Lossy Compression](https://mlsanity.com/paper/1805.11057/) (2018)
- [Quantization Guided JPEG Artifact Correction](https://mlsanity.com/paper/2004.09320/) (2020)
- [Towards Image Understanding from Deep Compression without Decoding](https://mlsanity.com/paper/1803.06131/) (2018)
- [Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks](https://mlsanity.com/paper/2008.10314/) (2020)
- [DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework](https://mlsanity.com/paper/1803.05788/) (2018)
- [The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study](https://mlsanity.com/paper/2008.00605/) (2020)
- [Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision](https://mlsanity.com/paper/2009.12088/) (2020)
- [A GAN-based Tunable Image Compression System](https://mlsanity.com/paper/2001.06580/) (2020)
- [On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures](https://mlsanity.com/paper/2007.14314/) (2020)
- [Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal](https://mlsanity.com/paper/2009.06912/) (2020)
- [Optimizing JPEG Quantization for Classification Networks](https://mlsanity.com/paper/2003.02874/) (2020)
- [Adapting JPEG XS gains and priorities to tasks and contents](https://mlsanity.com/paper/2005.08768/) (2020)

## Abstract (arXiv)

We present TACTIC: Task-Aware Compression Through Intelligent Coding. Our lossy compression model learns based on the rate-distortion-accuracy trade-off for a specific task. By considering what information is important for the follow-on problem, the system trades off visual fidelity for good task performance at a low bitrate. When compared against JPEG at the same bitrate, our approach is able to improve the accuracy of ImageNet subset classification by 4.5%. We also demonstrate the applicability of our approach to other problems, providing a 3.4% accuracy and 4.9% mean IoU improvements in performance over task-agnostic compression for semantic segmentation.