Summary
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.
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.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101153722 |
| Start date: | 01-01-2025 |
| End date: | 31-12-2027 |
| Total budget - Public funding: | - 279 227,00 Euro |
Cordis data
Original description
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.
Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
17-11-2024
Images
No images available.
Geographical location(s)
Search
