The Power of Open Source

At the day 1 keynote of Open Source Summit North America, Timothy Jordan, Director of Developer Relations and Open Source at Google, will talk about the landscape of open source and AI, the importance of a responsible approach, and the transformative impact of community collaboration. In anticipation of this talk, let’s break down the AI open source ecosystem, and how Google approaches it.

Google believes in the power of open technology to drive innovation and benefit everyone. It fosters creativity and collaboration, while ensuring technology access for developers and allowing customization to fit unique use cases. Open source licenses give developers full creative autonomy without restriction. It is this ecosystem of open source and open technology, shaped by ML frameworks like TensorFlow, Keras, and JAX, that has enabled so many incredible advances in AI in recent years.

The open source community has been in discussion on how to apply the Open Source Definition to carry forward the open principles of the OSD while addressing concepts like derived work and author attribution in AI. During Timothy’s keynote, he’ll speak to his own philosophy on Open Source and AI, and share how his assumptions about how we apply open source to AI have evolved. The immediate availability of AI models, powered by the open source ecosystem of ML frameworks, means it’s more important than ever that we establish a shared definition for open source and AI.

While that definition is in development, at Google we’re using precise language to describe our openly available models like Gemma. The definition and license is only one part of this open ML/AI future; advancements in safety tooling, policies, and developer knowledge are all part of creating a responsible and open future for AI. Those advancements are all fueled by a dedication to collaboration. Whether sharing innovations and improvements with the community, or having conversations with policymakers and open source leaders, collaboration is key to a responsible approach to AI in the open ecosystem. AI can only be safe and responsible if everyone’s experiences and perspectives are brought to the forefront as it’s built.

To demonstrate how open source has made AI readily available, Timothy will also take the audience through a “low code” demo of how to run large language models in-browser for web applications. Using MediaPipe, the LLM Inference API, and Gemma, users can quickly add genAI capabilities like document summarization and text generation.

Join us at Open Source Summit North America for this keynote, and visit opensource.google to learn more.

By the Google Open Source team

Google Season of Docs announces participating organizations for 2024

Google Season of Docs provides support for open source projects to improve their documentation and gives professional technical writers an opportunity to gain experience in open source. Together we improve developer experience through better documentation, increase our understanding of best practices in open source documentation, and raise the profile of technical writers in open source.

For 2024, Google Season of Docs is pleased to announce that eleven organizations will be participating in the program! The list of participating organizations can be viewed on the website.

The project development phase now begins. Organizations and the technical writers they hire will work on their documentation projects from now until November 22nd. For organizations still looking to hire a technical writer, the hiring deadline is May 22nd.

How do I take part in Google Season of Docs as a technical writer?

Start by reading the technical writer guide and FAQs which give information about eligibility and choosing a project. Next, technical writers interested in working with accepted open source organizations can share their contact information via the Season of Docs GitHub repository; or they may submit a statement of interest directly to the organizations. We recommend technical writers reach out to organizations before submitting a statement of interest to discuss the project they’ll be working on and gain a better understanding of the organization. Technical writers do not need to submit a formal application through Google Season of Docs, so reach out to the organizations as soon as possible!

Will technical writers be paid while working with organizations accepted into Google Season of Docs?

Yes. Participating organizations will transfer funds directly to the technical writer via OpenCollective. Technical writers should review the organization's proposed project budgets and discuss their compensation and payment schedule with the organization before hiring. Check out our technical writer payment process guide for more details.

General Timeline

May 22, 2024	Technical writer hiring deadline
June 5, 2024	Organization administrators start reporting on their project status via monthly evaluations
December 10, 2024	Final date for Organization administrators submit their case study and final project evaluation
December 13, 2024	Google publishes the 2024 Season of Docs case studies and aggregate project data
May 1, 2025	Organizations begin to participate in post-program followup surveys

See the full timeline for details.

Care to join us?

Explore the Google Season of Docs website at g.co/seasonofdocs to learn more about the program. Use our logo and other promotional resources to spread the word. Review the timeline, check out the FAQ, and reach out to organizations now!

If you have any questions about the program, please email us at season-of-docs@google.com.

By Erin McKean, Google Open Source Programs Office

Introducing Jpegli: A New JPEG Coding Library

The internet has changed the way we live, work, and communicate. However, it can turn into a source of frustration when pages load slowly. At the heart of this issue lies the encoding of images. To improve on this, we are introducing Jpegli, an advanced JPEG coding library that maintains high backward compatibility while offering enhanced capabilities and a 35% compression ratio improvement at high quality compression settings.

Jpegli is a new JPEG coding library that is designed to be faster, more efficient, and more visually pleasing than traditional JPEG. It uses a number of new techniques to achieve these goals, including:

• It provides both a fully interoperable encoder and decoder complying with the original JPEG standard and its most conventional 8-bit formalism, and API/ABI compatibility with libjpeg-turbo and MozJPEG.

• High quality results. When images are compressed or decompressed through Jpegli, more precise and psychovisually effective computations are performed and images will look clearer and have fewer observable artifacts.

• Fast. While improving on image quality/compression density ratio, Jpegli's coding speed is comparable to traditional approaches, such as libjpeg-turbo and MozJPEG. This means that web developers can effortlessly integrate Jpegli into their existing workflows without sacrificing coding speed performance or memory use.

• 10+ bits. Jpegli can be encoded with 10+ bits per component. Traditional JPEG coding solutions offer only 8 bit per component dynamics causing visible banding artifacts in slow gradients. Jpegli's 10+ bits coding happens in the original 8-bit formalism and the resulting images are fully interoperable with 8-bit viewers. 10+ bit dynamics are available as an API extension and application code changes are needed to benefit from it.

• More dense: Jpegli compresses images more efficiently than traditional JPEG codecs, which can save bandwidth and storage space, and speed up web pages.

How Jpegli works

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.

Testing Jpegli

In order to quantify Jpegli's image quality improvement we enlisted the help of crowdsourcing raters to compare pairs of images from Cloudinary Image Dataset '22, encoded using three codecs: Jpegli, libjpeg-turbo and MozJPEG, at several bitrates.

In this comparison we limited ourselves to comparing the encoding only, decoding was always performed using libjpeg-turbo. We conducted the study with the XYB ICC color profile disabled since that is how we anticipate most users would initially use Jpegli. To simplify comparing the results across the codecs and settings, we aggregated all the rater decisions using chess rankings inspired ELO scoring.

A higher ELO score indicates a better aggregate performance in the rater study. We can observe that jpegli at 2.8 BPP received a higher ELO rating than libjpeg-turbo at 3.7 BPP, a bitrate 32 % higher than Jpegli's.

Results

Our results show that Jpegli can compress high quality images 35% more than traditional JPEG codecs.

Jpegli is a promising new technology that has the potential to make the internet faster and more beautiful.

By Zoltan Szabadka, Martin Bruse and Jyrki Alakuijala – Paradigms of Intelligence, Google Research