Improved Deep Distributed Light Field Coding

Muhammad Umair Mukati, Milan Stepanov*, Giuseppe Valenzise, Søren Forchhammer, Frederic Dufaux

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Light fields enable increasing the degree of realism and immersion of visual experience by capturing a scene with a higher number of dimensions than conventional 2D imaging. On another side, higher dimensionality entails significant storage and transmission overhead compared to traditional video. Conventional coding schemes achieve high coding gains by employing an asymmetric codec design, where the encoder is significantly more complex than the decoder. However, in the case of light fields, the communication and processing among different cameras could be expensive, and the possibility of trading the complexity between the encoder and the decoder becomes a desirable feature. We leverage the distributed source coding paradigm to effectively reduce the encoder’s complexity at the cost of increased computation at the decoder side. Specifically, we train two deep neural networks to improve the two most critical parts of a distributed source coding scheme: the prediction of side information and the estimation of the uncertainty in the prediction. Experiments show considerable BD-rate gains, above 59% over HEVC-Intra and 17.45% over our previous method DLFC-I.
Original languageEnglish
JournalIEEE Open Journal of Circuits and Systems
Volume2
Pages (from-to)325 - 337
ISSN2644-1225
DOIs
Publication statusPublished - 2021

Keywords

  • Deep learning
  • Distributed source coding
  • Light field
  • Uncertainty estimation
  • View synthesis

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