Project description
Machine Learning boosts video compression
Video compression is essential for every internet video streaming platform. However, the signal processing community has not yet fully addressed the challenges related to the complexity and power consumption of the latest video compression standards. Supported by the Marie Skłodowska-Curie Actions programme, the BrainCode project will develop energy-efficient compression techniques for extended reality (XR) data. It will apply machine learning-based architectures to create a semantic video compression algorithm that uses Convolutional Neural Networks (CNNs). The goal is to mimic the visual system’s intelligent mechanisms for processing visual stimuli and leverage neuroscience models to design a groundbreaking XR video compression architecture. The results could enhance various image processing applications where real-time processing of visual scenes is crucial.
Objective
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.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2023-PF-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
700 13 IRAKLEIO
Greece
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.