It's called Guetzli, and it works with existing browsers and image processing tools.
Secondly, Guetzli would seem set to have a significant resource footprint compared to the long-established image compression libraries now in use (mostly over Apache or other Linux-based frameworks).
Google is shrinking JPEG image files by 35% to help boost online page load times.
Although Guetzli primarily aims at compressing the file size, Google also points out that it can also be used to increase the quality of the image without compromising on the size.
Uncompressed original image (left), libjpeg-encoded JPEG (middle), Guetzli-encoded JPEG (right). "It is our hope that webmasters and graphic designers will find Guetzli useful and apply it to their photographic content, making users' experience smoother on image-heavy websites in addition to reducing load times and bandwidth costs for mobile users", the researchers added.
Google has developed a new open-source JPEG compression algorithm, dubbed Guetzli (apparently that means "cookie" in Swiss German).
The new encoder is called Guetzli - Swiss German for "cookie", apparently - and according to Google, it can create "high quality JPEG images with file sizes 35 percent smaller than now available methods".
JPEG compression has several steps, including color space transformation, discrete cosine transformation, and quantization.
The JPEG encoder is open-source and available for you to download and implement in your own projects from this GitHub repository. Alternatively, it's now possible to significantly improve the image quality of a file without raising its size. But since the files are so much smaller and there's no real loss in image quality, Google says it's worth the tradeoff. Presumably Google are considering optimisations and rational solutions to speed up Guetzli's compression speed performance. However, Google said that images that use Guetzli are preferred visually over images that use libjpeg-based compression. In principle Google is able to make estimations of colour perception and visual masking in a more detailed way than current techniques allow.
The downside to this methodology is that compression takes significantly longer than now available methods.