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March 17, 2017

Google’s new JPEG encoder shrinks file sizes by up to 35 percent

by John_A

Why it matters to you

Google’s new method of encoding JPEG files could help your web browser load pages significantly faster, without sacrificing image quality.

Google has unveiled a new open source algorithm that can dramatically reduce the size of JPEG images. Guetzil — named for the Swiss German term for a cookie — is a JPEG encoder that can reportedly produce high quality images with file sizes that are 35 percent smaller than the norm.

Like Google’s Zopfli algorithm for PNG and gzip files, Guetzil offers smaller file sizes without sacrificing compatibility with existing web browsers, image processing applications, or the JPEG standard. This sets the algorithm apart from other methods of reducing JPEG file sizes, like RNN-based image compression, RAISR, and WebP.

Guetzil works by focusing on the quantization stage of the compression process. It uses an advanced psychovisual model that attempts to strike a balance between small file sizes and image fidelity by honing in on the kind of details that the human eye is drawn to, according to a post published to the Google Research Blog.

More: Google’s new Brotli algorithm is about to supercharge web browsing

The downside to this methodology is that compression takes significantly longer than currently available methods. However, tests have found that people preferred images compressed using Guetzil to those encoded with libjpeg, even when the latter images were slightly larger. Google describes the slower compression process as a “worthy tradeoff.”

If Guetzil is broadly implemented, users could be able to enjoy a smoother, more responsive experience while browsing the internet. The smaller image sizes would help pages load faster, and could even allow users on a data plan to use less of their allocation loading images.

The researchers behind the project hope to see webmasters and graphic designers adopt the open-source algorithm. The team also suggests that the psychovisual approach adopted by the project will inspire further research into the way our eyes perceive compressed images and video.

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