# Guetzli: Perceptually Guided JPEG Encoder

arXiv: https://arxiv.org/abs/1703.04421 · HTML: https://mlsanity.com/paper/1703.04421/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2017.

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

- [Random-Access Neural Compression of Material Textures](https://mlsanity.com/paper/2305.17105/) (2023)
- [Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measures](https://mlsanity.com/paper/2407.10181/) (2024)
- [Measuring Perceptual Color Differences of Smartphone Photographs](https://mlsanity.com/paper/2205.13489/) (2022)
- [Learning a Deep Color Difference Metric for Photographic Images](https://mlsanity.com/paper/2303.14964/) (2023)
- [A Unified Image Preprocessing Framework For Image Compression](https://mlsanity.com/paper/2208.07110/) (2022)
- [Modeling Image Quantization Tradeoffs for Optimal Compression](https://mlsanity.com/paper/2112.07207/) (2021)

## Abstract (arXiv)

Guetzli is a new JPEG encoder that aims to produce visually indistinguishable images at a lower bit-rate than other common JPEG encoders. It optimizes both the JPEG global quantization tables and the DCT coefficient values in each JPEG block using a closed-loop optimizer. Guetzli uses Butteraugli, our perceptual distance metric, as the source of feedback in its optimization process. We reach a 29-45% reduction in data size for a given perceptual distance, according to Butteraugli, in comparison to other compressors we tried. Guetzli's computation is currently extremely slow, which limits its applicability to compressing static content and serving as a proof- of-concept that we can achieve significant reductions in size by combining advanced psychovisual models with lossy compression techniques.