At Fluendo Lab, we’re advancing technologies for digital experiences. We work on video processing, interactive content creation, and audio enhancement. Our research covers AI-based computer vision, next-generation codecs, and multimedia frameworks for edge devices. We dialogue with stakeholders and aim to transform how people enjoy, communicate, and work with digital content. Join us as we redefine the boundaries of digital media experiences.
At Fluendo Lab, we value open dialogue and collaboration with our team and beyond. We welcome insights and ideas from our stakeholders, such as users, customers, and the tech community. They help shape the future of multimedia technology with their experiences, needs, and ideas. They guide our research and ensure our solutions are user-focused and effective. Our research focuses on these key areas:
We are using AI-based computer vision to enhance user experiences. This research line is about expanding the possibilities of multimedia, enabling users to interact with digital content in new and exciting ways.
The focus is on improving the encoding and decoding processes to deliver better quality at lower bitrates and computational requirements. This is our commitment to providing high-quality multimedia experiences, even in resource-limited scenarios, and evolving with our partner’s needs.
We’re innovating in robust multimedia frameworks for edge devices. Our focus is on performance and efficiency, enabling seamless multimedia experiences on devices with limited resources.
In-house developed multi platform SDK to speed up our research to find new solutions for common problems.
We've distilled our expertise in multimedia technologies into a tool, designed to substantially expedite the process of new developments.
Our adeptness at hardware acceleration opens up possibilities for tackling novel use cases.
Merging off-the-shelf and custom models we are able to reduce our time to market while keeping state-of-the-art performance.
All those tools are thought as optional parts in a bigger architecture so we can create an optimal toolset for each problem.