Most of tomorrow’s media formats tend to increase the sense of immersion into the content. Transforming the end-user/consumer’s experience by putting them into the middle of the action or information is the challenge, especially whenever narrative aspects are key.
The Advanced Media Content lab is building tools that enable the creation, storage, transportation, and sharing both for and by as many people as possible. It delivers expertise and technologies related to innovative dimensions of images (resolution, depth, color, light, etc.), sound (spatialized), and new rendering devices (screen, projection, AR/VR head-mounted displays with 3 or 6 degrees of freedom, etc.). It develops tools and solutions aimed at enabling the adoption of these future formats by industry professionals, and ultimately by end consumers. Holography and compression systems are key areas of expertise for the laboratory.
The challenges raised by new video and audio formats? Bringing even MORE realism and immersion to the experience, while still remaining easy to use and handle.
03.22.2023
Digital reconstructions of numerical holograms enable data visualization and serve a multitude of purposes ranging from microscopy to holographic displays. Over the years, many pipelines have been developed for specific hologram types. Within the standardization effort of JPEG Pleno holography, an open-source MATLAB toolbox was developed that reflects the best current consensus. It can process…
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02.20.2023
Automatic depth map retrieval from digital holograms using a deep learning approach
Information extraction from computer-generated holograms using learning-based methods is a topic that has not received much research attention. In this article, we propose and study two learning-based methods to extract the depth information from a hologram and compare their performance with that of classical depth from focus (DFF) methods. We discuss the main characteristics of a hologram and…
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06.08.2023
In this work, we introduce a novel approach for depth estimation in a computer-generated hologram by employing horizontal segmentation of the reconstruction volume instead of conventional vertical segmentation. The reconstruction volume is divided into horizontal slices and each slice is processed using a residual U-net architecture to identify in-focus lines, enabling determination of the slice…
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02.21.2023
Automatic depth map retrieval from digital holograms using a depth-from-focus approach
Recovering the scene depth map from a computer-generated hologram is a problem that remains unsolved, despite the growing interest in the subject. In this article, we propose to study the application of depth-from-focus (DFF) methods to retrieve the depth information from the hologram. We discuss the different hyperparameters that are required for the application of the method and their impact on…
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2019
Product of the Year at the NAB Show in Las Vegas
2017
Technology Innovation Award at the NAB Show in Las Vegas
