10,693 research outputs found
Navigation domain representation for interactive multiview imaging
Enabling users to interactively navigate through different viewpoints of a
static scene is a new interesting functionality in 3D streaming systems. While
it opens exciting perspectives towards rich multimedia applications, it
requires the design of novel representations and coding techniques in order to
solve the new challenges imposed by interactive navigation. Interactivity
clearly brings new design constraints: the encoder is unaware of the exact
decoding process, while the decoder has to reconstruct information from
incomplete subsets of data since the server can generally not transmit images
for all possible viewpoints due to resource constrains. In this paper, we
propose a novel multiview data representation that permits to satisfy bandwidth
and storage constraints in an interactive multiview streaming system. In
particular, we partition the multiview navigation domain into segments, each of
which is described by a reference image and some auxiliary information. The
auxiliary information enables the client to recreate any viewpoint in the
navigation segment via view synthesis. The decoder is then able to navigate
freely in the segment without further data request to the server; it requests
additional data only when it moves to a different segment. We discuss the
benefits of this novel representation in interactive navigation systems and
further propose a method to optimize the partitioning of the navigation domain
into independent segments, under bandwidth and storage constraints.
Experimental results confirm the potential of the proposed representation;
namely, our system leads to similar compression performance as classical
inter-view coding, while it provides the high level of flexibility that is
required for interactive streaming. Hence, our new framework represents a
promising solution for 3D data representation in novel interactive multimedia
services
Optimized Data Representation for Interactive Multiview Navigation
In contrary to traditional media streaming services where a unique media
content is delivered to different users, interactive multiview navigation
applications enable users to choose their own viewpoints and freely navigate in
a 3-D scene. The interactivity brings new challenges in addition to the
classical rate-distortion trade-off, which considers only the compression
performance and viewing quality. On the one hand, interactivity necessitates
sufficient viewpoints for richer navigation; on the other hand, it requires to
provide low bandwidth and delay costs for smooth navigation during view
transitions. In this paper, we formally describe the novel trade-offs posed by
the navigation interactivity and classical rate-distortion criterion. Based on
an original formulation, we look for the optimal design of the data
representation by introducing novel rate and distortion models and practical
solving algorithms. Experiments show that the proposed data representation
method outperforms the baseline solution by providing lower resource
consumptions and higher visual quality in all navigation configurations, which
certainly confirms the potential of the proposed data representation in
practical interactive navigation systems
Representation and coding of 3D video data
Livrable D4.1 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D4.1 du projet
Joint Reconstruction of Multi-view Compressed Images
The distributed representation of correlated multi-view images is an
important problem that arise in vision sensor networks. This paper concentrates
on the joint reconstruction problem where the distributively compressed
correlated images are jointly decoded in order to improve the reconstruction
quality of all the compressed images. We consider a scenario where the images
captured at different viewpoints are encoded independently using common coding
solutions (e.g., JPEG, H.264 intra) with a balanced rate distribution among
different cameras. A central decoder first estimates the underlying correlation
model from the independently compressed images which will be used for the joint
signal recovery. The joint reconstruction is then cast as a constrained convex
optimization problem that reconstructs total-variation (TV) smooth images that
comply with the estimated correlation model. At the same time, we add
constraints that force the reconstructed images to be consistent with their
compressed versions. We show by experiments that the proposed joint
reconstruction scheme outperforms independent reconstruction in terms of image
quality, for a given target bit rate. In addition, the decoding performance of
our proposed algorithm compares advantageously to state-of-the-art distributed
coding schemes based on disparity learning and on the DISCOVER
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