Project description
Deep learning technology to decarbonise video streaming
Video streaming is bad for the environment. Studies show that 60 minutes of streaming in Europe has a carbon footprint equivalent to driving 250 metres. Video compression technologies may help reverse this trend by reducing the data used to encode digital video content without loss of quality. While major media are investing in methods to revolutionise image/video compression, these methods are difficult to implement in consumer devices. The EU-funded FALCON project will investigate a novel framework for developing fast and energy-efficient deep learning-based image and video compression to reduce their carbon footprint. The findings will benefit important EU policies such as the Paris Agreement and the EU Green Deal.
Objective
The emerging Learned Compression (LC) methods show great potential to revolutionize image/video compression, and major media industries are investing heavily in this field. However, the high computational complexity of these methods makes it difficult to employ them in consumer devices, and this obstacle discourages using them in future compression standards, such as JPEG and MPEG, despite their superior performance compared to traditional methods. This project will investigate a novel framework for developing fast and energy-efficient Deep Learning-based compression. We will develop methods that (1) greatly improve the compression efficiency of LC, and (2) significantly reduce its computational complexity and energy consumption. Given the huge share of video industry in global Greenhouse gas emission, this will be a big step towards important EU policies such as the Paris agreement and the EU Green Deal. The objectives of the project are achieved via: (i) splitting the coding into smaller tasks, (ii) investigating efficient learning methods (including Operational Neural Networks, an invention of the supervisor of the project), and (iii) integrating human perception into image/video coding.
The experienced researcher holds a PhD in computer engineering, during which he worked on accelerating the encoding process of compression standards. He has a background and skill-set in hardware engineering, signal processing, media technology, and machine learning, which is necessary for this interdisciplinary project. The project will be carried out under the supervision of an internationally famous scientist who has extensive experience in both machine learning and video compression. The host institution in Finland has a long experience in EU funding and collaborations with industries. The results and findings will be published in top international journals and conferences. Moreover, some findings will be considered for possible exploitation in future MPEG/JPEG standards.
Fields of science (EuroSciVoc)
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.
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.
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering signal processing
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering computer hardware
- social sciences economics and business economics sustainable economy
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
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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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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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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.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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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) H2020-MSCA-IF-2020
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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.
33100 Tampere
Finland
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.