Search

Your search keyword '"Munteanu, Adrian"' showing total 662 results
Start Over Author "Munteanu, Adrian"
662 results on '"Munteanu, Adrian"'

161. MPEG-I Depth Estimation Reference Software

Author

Rogge, Ségolène, Bonatto, Daniele, Sancho, Jaime, Salvador, Ruben, Juarez, Eduardo, Munteanu, Adrian, Lafruit, Gauthier, Rogge, Ségolène, Bonatto, Daniele, Sancho, Jaime, Salvador, Ruben, Juarez, Eduardo, Munteanu, Adrian, and Lafruit, Gauthier

Abstract

info:eu-repo/semantics/inPress

Published
2019

179. Scalable Multiple-Description Image Coding Based on Embedded Quantization

Author

Moerman Ingrid, Gavrilescu AugustinI, Verdicchio Fabio, Munteanu Adrian, Cornelis Jan, and Schelkens Peter

Subjects
Electronics, TK7800-8360
Abstract

Scalable multiple-description (MD) coding allows for fine-grain rate adaptation as well as robust coding of the input source. In this paper, we present a new approach for scalable MD coding of images, which couples the multiresolution nature of the wavelet transform with the robustness and scalability features provided by embedded multiple-description scalar quantization (EMDSQ). Two coding systems are proposed that rely on quadtree coding to compress the side descriptions produced by EMDSQ. The proposed systems are capable of dynamically adapting the bitrate to the available bandwidth while providing robustness to data losses. Experiments performed under different simulated network conditions demonstrate the effectiveness of the proposed scalable MD approach for image streaming over error-prone channels.

Published
2007

180. Multiview Video Processing -- Color Correction and View Synthesis

Author

Munteanu, Adrian, Lafruit, Gauthier, Van Biesen, Leo, Debeir, Olivier, Pintelon, Rik, Cornelis, Bruno, Deligiannis, Nikos, Bekaert, Philippe, Lu, Shao-Ping, Ceulemans, Beerend, Munteanu, Adrian, Lafruit, Gauthier, Van Biesen, Leo, Debeir, Olivier, Pintelon, Rik, Cornelis, Bruno, Deligiannis, Nikos, Bekaert, Philippe, Lu, Shao-Ping, and Ceulemans, Beerend

Abstract

With the increasing interest in high-quality 3D multimedia content over the last decade, a great deal of research is dedicated to further improvement of already existing technologies as well as the search for new ways to present 3D multimedia content to consumers. To this end, significant improvements have been made in the area of stereoscopic 3D video which is currently the most ubiquitous type of 3D.With the release of the movie Avatar in 2009 it was clear that stereoscopic 3D was within our reach and both industry and academia have made various contributions in the acquisition, storage, transmission, and visualization of such content.In contrast, others were not fully satisfied with the fact that stereoscopic 3D requires consumers to wear appropriate glasses in order to perceive depth differences.To this end, several types of visualization technologies were developed that can simultaneously display multiple different viewpoints for different viewing positions.These so-called autostereoscopic devices have the advantages that they can deliver content to multiple users without the need for special glasses. Additionally, these devices also offer different viewpoints to a moving user hereby enabling the motion parallax effect which is an important depth-cue for the human visual system but which is not present in classical stereoscopic 3D. The downside of existing autostereoscopic displays is that the current state-of-the-art in acquisition and processing is not sufficiently mature to push this technology to consumers.This thesis presents a new color correction method that has been developed to handle color differences between cameras, using a global energy minimization method to provide an appropriate color transfer while preserving important structural information. A second contribution is the development of a multiview synthesis framework that is able to render novel viewpoints from a sparse set of captured texture and depth pairs. A point cloud-based filter elimina, Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

Published
2018

182. Scalable feature-preserving irregular mesh coding

Author

El Sayeh Khalil, Jonas, Munteanu, Adrian, Denis, Leon, Lambert, Peter, Van De Walle, Rik, and Electronics and Informatics

Subjects
I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling–Hierarchy and geometric transformations G.1.2 [Numerical Analysis]: Approximation–Approximation of surfaces and contours, Computer Networks and Communications, scalable mesh compression, Wavelets and fractals E.4 [Coding and Information Theory]: Data compaction and compression, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, scalable rendering, level of detail algorithms, Computer Graphics and Computer-Aided Design, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

This paper presents a novel wavelet-based transform and coding scheme for irregular meshes. The transform preserves geometric features at lower resolutions by adaptive vertex sampling and retriangulation, resulting in more accurate subsampling and better avoidance of smoothing and aliasing artefacts. By employing octree-based coding techniques, the encoding of both connectivity and geometry information is decoupled from any mesh traversal order, and allows for exploiting the intra-band statistical dependencies between wavelet coefficients. Improvements over the state of the art obtained by our approach are three-fold: (1) improved rate–distortion performance over Wavemesh and IPR for both the Hausdorff and root mean square distances at low-to-mid-range bitrates, most obvious when clear geometric features are present while remaining competitive for smooth, feature-poor models; (2) improved rendering performance at any triangle budget, translating to a better quality for the same runtime memory footprint; (3) improved visual quality when applying similar limits to the bitrate or triangle budget, showing more pronounced improvements than rate–distortion curves.

Published
2017

183. CNN-Based Intra-Prediction for Lossless HEVC.

Author

Schiopu, Ionut, Huang, Hongyue, and Munteanu, Adrian

Subjects
VIDEO coding, VIDEO compression, CONVOLUTIONAL neural networks, ARTIFICIAL neural networks, FORECASTING, LOSS functions (Statistics)
Abstract

The paper proposes a novel block-wise prediction paradigm based on Convolutional Neural Networks (CNNs) for lossless video coding. A deep neural network model which follows a multi-resolution design is employed for block-wise prediction. Several contributions are proposed to improve neural network training. A first contribution proposes a novel loss function formulation for an efficient network training based on a new approach for patch selection. Another contribution consists in replacing all HEVC-based angular intra-prediction modes with a CNN-based intra-prediction method, where each angular prediction mode is complemented by a CNN-based prediction mode using a specifically trained model. Another contribution consists in an efficient adaptation of the CNN-based intra-prediction residual for lossless video coding. Experimental results on standard test sequences show that the proposed coding system outperforms the HEVC standard with an average bitrate improvement of around 5%. To our knowledge, the paper is the first to replace all the traditional HEVC-based angular intra-prediction modes with an intra-prediction method based on modern Machine Learning techniques for lossless video coding applications. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results