|Original author(s)||Google Research|
1.0.1 / 2017-03-21
Guetzli is a freely licensed JPEG encoder developed at Google Research Europe in Zürich, Switzerland by Jyrki Alakuijala, Robert Obryk, and Zoltán Szabadka. It specializes in high-end image quality where it is claimed to produce significantly smaller files than prior encoders at equivalent quality, albeit at very low speed. It is named after the Swiss German expression for Biscuits, in line with the names of other compression technology from Google.
Guetzli is known to be very resource intensive, requiring several orders of magnitude more processing time than other JPEG encoders, and a lot of memory. Guetzli supports only the top of JPEG's quality range (quantizer settings 84–100) and supports only sequential (non-"progressive") encoding. The performance of the Butteraugli quality metric it optimizes for breaks down below that range and starves some areas too much. Guetzli is more effective with bigger files.
It is often placed in line with other bits of Google technology that are used to cut down web page load times, while Google itself describes it also as a demonstration of the potential of psychovisual optimizations to motivate further research to benefit future JPEG encoders, and acknowledges the often impractically low speed of their code.
It is implemented as a command-line tool in C++ and the source code published on GitHub as free software under the terms of version 2 of the Apache license. Several important Linux distributions have packages available in their official repositories. For Arch Linux there are user repositories available. There is a macOS version packaged in the Homebrew repository. There are graphical user interfaces and prebuilt Windows binaries available elsewhere on the Internet. For web development with node.js, there is a Guetzli package available in the Node Package Manager (npm).
The first public version was released on October 21, 2016, without any speed optimizations, and only announced on a specialist forum. Version 1.0 followed five months later on March 15, 2017, accompanied by an announcement to a broader public and two scientific papers.
Guetzli uses innovative methods to optimize compression efficiency that target mainly the quantization step. It constructs custom quantization tables for each file, decides on color subsampling, and quantizes some adjacent DCT coefficients to zero, balancing benefits in the run-length encoding of coefficients and preservation of perceived image fidelity. Zeroing the right coefficients is the most effective tool in Guetzli, which is used as a makeshift means of spatially adaptive quantization. The optimizations are guided by Butteraugli.
Butteraugli is a newly introduced objective image quality assessment metric. It assigns a differential mean opinion score (DMOS) value to the difference between an original image and a degraded version. It is significantly more complex than traditional metrics like PSNR and SSIM, but claimed to perform better with high-end quality, where degradations are not or barely noticeable. It models color perception and visual masking in the human visual system, taking into account that the eye is imaging different colors with different precision. It uses a heat map of changes. How the hundreds of parameters that model the properties of the human visual system were derived remains unexplained. An in-house performance evaluation with 614 ratings from 23 people on their own test set of 31 images yielded 75% of ratings favouring of JPEGs encoded for Butteraugli scores over libjpeg-turbo encodes, which usually score higher on SSIM and PSNRHVS-M.
Translating to "butter eye", the Swiss-German name originally signifies a dimple on top of some sweet pastry that has been filled with butter and sugar before baking.
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