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BrainCode

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Cerrado

Brain-inspired Intelligence for Semantic Video Compression

The BrainCode project proposes novel compression techniques for extended reality (XR) data which are energy efficient while ensuring a reconstruction quality that satisfies the human visual semantic perception. There are several c... ver más

31/12/2027

FOUNDATION FOR RES...

279K€

Presupuesto del proyecto: 279K€

Líder del proyecto

IDRYMA TECHNOLOGIAS KAI EREVNAS No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-03-28

HORIZON-MSCA-2023-PF-01-01: MSCA Postdoctoral Fellowships 2023 ExpectedOutcome:Project results are expected to contribute to the following outcomes:

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3 Participantes

FOUNDATION FOR RESEARCH AND...

279.23K€ | Lider

FACEBOOK TECHNOLOGIES LLC

N.D. | Participante

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Duración del proyecto: 45 meses Fecha Inicio: 2024-03-28
Fecha Fin: 2027-12-31

Líder del proyecto

IDRYMA TECHNOLOGIAS KAI EREVNAS No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 279K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The BrainCode project proposes novel compression techniques for extended reality (XR) data which are energy efficient while ensuring a reconstruction quality that satisfies the human visual semantic perception. There are several challenges concerning the complexity and the power consumption of the latest video compression standards which have not yet taken into account by the signal processing community. We propose that these challenges can be addressed by machine learning based architectures in order to avoid the exhaustive comparisons between sequential frames. We aim at releasing a semantic video compression algorithm that uses Convolutional Neural Networks (CNNs) and drives the bit allocation with respect to the content of the visual scene. Another goal of BrainCode is to mimic the visual system as an intelligent mechanism that processes the visual stimulus. This can be claimed as it consumes low power, it deals with high resolution dynamic signals and the dynamic way it transforms and encodes the visual stimulus is beyond the current compression standards. During the last decades, a lot of effort has been made to understand how the visual system works, what is the structure and role of each layer and individual cell that lies along the visual pathway, and how the huge visual information is propagated and compacted through the nerve cells before it reaches the visual cortex. There are very interesting mathematical models which approximate the neural behaviour and they have been widely used for image processing applications including compression. The BrainCode project searches the latest neuroscience models for the design of a groundbreaking XR video compression architecture. The efficiency of the above approaches is expected to improve several image processing applications like computer vision, virtual reality, and video compression among other, where the real-time processing of the visual scene plays a substantial role.

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