Improved Deep Distributed Light Field Coding - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Open Journal of Circuits and Systems Année : 2021

Improved Deep Distributed Light Field Coding

Résumé

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.
Fichier principal
Vignette du fichier
2021_OJCAS_Mukati_et_al.pdf (2.71 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03198096 , version 1 (14-04-2021)

Identifiants

Citer

Muhammad Umair Mukati, Milan Stepanov, Giuseppe Valenzise, Søren Forchhammer, Frédéric Dufaux. Improved Deep Distributed Light Field Coding. IEEE Open Journal of Circuits and Systems, 2021, 2, pp.325 - 337. ⟨10.1109/OJCAS.2021.3073252⟩. ⟨hal-03198096⟩
94 Consultations
85 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More