The emerging Learning based image and video 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 novel solutions for developing fast and energy-efficient Deep Learning-based compression and multimedia systems. 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 an important step towards important EU policies such as the Paris agreement and the EU Green Deal. The objectives of the project are achieved via:
- Designing low complexity DNN-based methods
- Investigating efficient learning methods, and
- Optimizing compression and multimedia systems based on human visual system
Funding
Funding source
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101022466.
