Google announced the release of Jpegli, a new JPEG coding library that includes some impressive performance and compression benefits.

JPEG is one of the most widely used image formats, especially on the internet, but it doesn’t provide the same lossless quality as other formats. In fact, when a JPEG is overcompressed, the quality can be absolutely terrible.

Google is working to address the issue with Jpegli. The new coding library “high backward compatibility while offering enhanced capabilities and a 35% compression ratio improvement at high quality compression settings.” Google attributes the advancements to a number of techniques working together:

Jpegli works by using a number of new techniques to reduce noise and improve image quality; mainly adaptive quantization heuristics from the JPEG XL reference implementation, improved quantization matrix selection, calculating intermediate results precisely, and having the possibility to use a more advanced colorspace. All the new methods have been carefully crafted to use the traditional 8-bit JPEG formalism, so newly compressed images are compatible with existing JPEG viewers such as browsers, image processing software, and others.

Adaptive quantization heuristics

Jpegli uses adaptive quantization to reduce noise and improve image quality. This is done by spatially modulating the dead zone in quantization based on psychovisual modeling. Using adaptive quantization heuristics that we originally developed for JPEG XL, the result is improved image quality and reduced file size. These heuristics are much faster than a similar approach originally used in guetzli.

Improved quantization matrix selection

Jpegli also uses a set of quantization matrices that were selected by optimizing for a mix of psychovisual quality metrics. Precise intermediate results in Jpegli improve image quality, and both encoding and decoding produce higher quality results. Jpegli can use JPEG XL’s XYB colorspace for further quality and density improvements.

Jpegli is an impressive option that will hopefully see widespread adoption, improving the quality of images while simultaneously reducing their size.