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Image Processing Lab (IPL)

Implicit Neural Representations for Video Compression

Parole chiave COMPRESSIONE, DEEP NEURAL NETWORKS

Riferimenti ENRICO MAGLI

Gruppi di ricerca CCNE - COMMUNICATIONS AND COMPUTER NETWORKS ENGINEERING, ICT4SS - ICT FOR SMART SOCIETIES, Image Processing Lab (IPL)

Tipo tesi RESEARCH

Descrizione A new research line in computer vision replaces traditional discrete representations of signals (e.g. pixel grids in images and video) with continuous functions parameterized by deep neural networks. These architectures, called implicit neural representations, take as input the spatio-temporal coordinates and are trained to output a representation of the signal at each input location. Recent studies showed that these representations are a powerful tool, allowing accurate representations of natural signals and offering many possible benefits over conventional representations. In particular, implicit neural representations have been used to represent video, showing promising results. The purpose of this thesis is to investigate the potential of such signal representations in the context of video compression. The student will develop a video encoder based on implicit neural representations, assesing the rate-distortion performance of the proposed encoder with respect to state-of-the-art techniques.


References:
Sitzmann, Vincent, et al. "Implicit Neural Representations with Periodic Activation Functions." arXiv preprint arXiv:2006.09661 (2020). - https://vsitzmann.github.io/siren/

Tancik, Matthew, et al. "Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains." arXiv preprint arXiv:2006.10739 (2020).

Conoscenze richieste Candidate students should have some background on neural networks. Some experience of TensorFlow environment and Python programming are desirable, along with good programming skills.


Scadenza validita proposta 17/07/2021      PROPONI LA TUA CANDIDATURA




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