M
Marco Cagnazzo
Researcher at Télécom ParisTech
Publications - 157
Citations - 1610
Marco Cagnazzo is an academic researcher from Télécom ParisTech. The author has contributed to research in topics: Motion compensation & Motion estimation. The author has an hindex of 22, co-authored 153 publications receiving 1505 citations. Previous affiliations of Marco Cagnazzo include Université Paris-Saclay & Institut Mines-Télécom.
Papers
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Journal ArticleDOI
Initialization, Limitation, and Predictive Coding of the Depth and Texture Quadtree in 3D-HEVC
TL;DR: An intercomponent tool is proposed in which this link is exploited to save both runtime and bits through a joint coding of the quadtrees.
Book
Emerging Technologies for 3D Video: Creation, Coding, Transmission and Rendering
TL;DR: Emerging Technologies for 3D Video will deal with all aspects involved in 3D video systems and services, including content acquisition and creation, data representation and coding, transmission, view synthesis, rendering, display technologies, human perception of depth and quality assessment.
Journal ArticleDOI
Multiview Plus Depth Video Coding With Temporal Prediction View Synthesis
TL;DR: This work proposes a new coding scheme for 3-D High Efficiency Video Coding (HEVC) that allows it to take full advantage of temporal correlations in the intermediate view and improve the existing synthesis from adjacent views.
Proceedings ArticleDOI
Region-oriented compression of multispectral images by shape-adaptive wavelet transform and SPIHT
TL;DR: A new technique for the compression of remote-sensing hyperspectral images based on wavelet transform and zerotree coding of coefficients and thanks to the segmentation map region boundaries are faithfully preserved and selective encoding strategies can be easily implemented.
Journal ArticleDOI
Subjective evaluation of Super Multi-View compressed contents on high-end light-field 3D displays
Antoine Dricot,Joel Jung,Marco Cagnazzo,Béatrice Pesquet,Frederic Dufaux,Péter Tamás Kovács,Vamsi Kiran Adhikarla +6 more
TL;DR: First results are provided showing that improvement of compression efficiency is required, as well as depth estimation and view synthesis algorithms improvement, but that the use of SMV appears realistic according to next generation compression technology requirements.