| PROJECT: | End-to-End Deep learning based image Compression | |||||
| ACRONYM: | DRAGON | |||||
| MAIN OBJECTIVE: | The DRAGON (Deep leaRning bAsed imaGe cOmpressioN) project’s aim is to develop state-of-the-art deep learning methods that are able to determine a compact image representation model, obtained (learned) from a large amount of visual data, and are capable of representing the wide variety of visual content that is available today with a high value of compression ratio. This project will advance existing solutions in the learning-based image coding field, which already show encouraging results in terms of rate- distortion performance, especially in comparison with conventional image codecs (e.g. JPEG 2000 and HEVC Intra) that compress the visual information with hand-crafted transforms, entropy coding and quantization schemes. | |||||
| Reference: | 0001 | |||||
| Funding: | IT | |||||
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| Team: | João Miguel Duarte Ascenso, Catarina Isabel Carvalheiro Brites, Nuno Miguel Morais Rodrigues, Luís Filipe Barbosa de Almeida Alexandre | |||||
| Groups: |
Multimedia Signal Processing – Lx Multimedia Signal Processing – Lr Pattern and Image Analysis – Cv |
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| Partners: | IT | |||||
| Local Coordinator: | João Miguel Duarte Ascenso |
This project falls under the following United Nations Strategic Development Goals (SDGs):
No publications associated with this project.