AIConfPaper
← Search

Marco Cagnazzo

8 accepted papers

2024

Find the Lady: Permutation and Re-synchronization of Deep Neural Networks

Carl De Sousa Trias, Mihai Petru Mitrea, Attilio Fiandrotti, Marco Cagnazzo, Sumanta Chaudhuri, Enzo Tartaglione
AAAI 2024technical

Deep neural networks are characterized by multiple symmetrical, equi-loss solutions that are redundant. Thus, the order of neurons in a layer and feature maps can be given arbitrary permutations, without affecting (or minimally affecting) their output. If we shuffle these neurons, or if we apply to…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2021

Patch Decoder-Side Depth Estimation In Mpeg Immersive Video

Marta Milovanovic, Félix Henry, Marco Cagnazzo, Joël Jung
ICASSP 2021accepted

This paper presents a new approach for achieving bitrate and pixel rate reduction in the MPEG immersive video coding setting. We demonstrate that it is possible to avoid the transmission of some depth information in the Test Model for Immersive Video (TMIV) by estimating it at the receiver's side. A…

Cited by 0Source ↗Scholar ↗
2019

Compression Improvement via Reference Organization for 2D-multiview Content

Pavel Nikitin, Marco Cagnazzo, Joël Jung
ICASSP 2019accepted

One of the most challenging goals of future immersive services is to enable the observation of a scene from any viewpoint, thus making free-navigation possible under certain constraints. In order to provide such kind of services with smooth navigation, a huge amount of views should be available on t…

Cited by 0Source ↗Scholar ↗
2019

Enhancing HEVC Spatial Prediction by Context-based Learning

Li Wang, Attilio Fiandrotti, Andrei I. Purica, Giuseppe Valenzise, Marco Cagnazzo
ICASSP 2019accepted

Deep generative models have been recently employed to compress images, image residuals or to predict image regions. Based on the observation that state-of-the-art spatial prediction is highly optimized from a rate-distortion point of view, in this work we study how learning-based approaches might be…

Cited by 7Source ↗Scholar ↗
2016

View synthesis based on temporal prediction via warped motion vector fields

Andrei I. Purica, Marco Cagnazzo, Béatrice Pesquet-Popescu, Frédéric Dufaux, Bogdan Ionescu
ICASSP 2016accepted

The demand for 3D content has increased over the last years as 3D displays are now widespread. View synthesis methods, such as depth-image-based-rendering, provide an efficient tool in 3D content creation or transmission, and are integrated in coding solutions for multiview video content such as 3D-…

Cited by 0Source ↗Scholar ↗
2015

Improved view synthesis by motion warping and temporal hole filling

Andrei I. Purica, Elie Gabriel Mora, Béatrice Pesquet-Popescu, Marco Cagnazzo, Bogdan Ionescu
ICASSP 2015accepted

View synthesis received increasing attention over the last years, as it offers a wide range of practical applications like Free Viewpoint Television, 3D video, video gaming, etc. The main issues in view synthesis are the filling of disoccluded areas and the warping of real views. In this paper we pr…

Cited by 0Source ↗Scholar ↗