# TinyLIC-High efficiency lossy image compression method

arXiv: https://arxiv.org/abs/2402.11164 · HTML: https://mlsanity.com/paper/2402.11164/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression](https://mlsanity.com/paper/2403.16677/) (2024)
- [MoECodec: Image Compression for joint human and machine perception via Mixture-of-Experts](https://mlsanity.com/paper/2606.21033/) (2026)

## Abstract (arXiv)

Image compression has been the subject of extensive research for several decades, resulting in the development of well-known standards such as JPEG, JPEG2000, and H.264/AVC. However, recent advancements in deep learning have led to the emergence of learned image compression methods that offer significant improvements in coding efficiency compared to traditional codecs. These learned compression techniques have shown noticeable gains and even outperformed traditional schemes