FAst and energy efficient Learned image and video CompresiON

FAst and energy efficient Learned image and video CompresiON




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.
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Beviljade finansiering

190 680 €

Beviljat belopp

190 681 €


Europeiska unionen

Typ av finansiering

Standard EF


Horizon 2020 Framework Programme


Nurturing excellence by means of cross-border and cross-sector mobility (H2020-EU.1.3.2.)
Individual Fellowships (MSCA-IF-2020)
Utlysnings ID

Övriga uppgifter

Finansieringsbeslutets nummer