371 research outputs found
3D video compression based on high efficiency video coding
With the advent of autostereoscopic displays, questions rise on how to efficiently compress the video information needed by such displays. Additionally, for gradual market acceptance of this new technology it is valuable to have a solution offering forward compatibility with stereo 3D video as it is used nowadays. In this paper, a multiview compression scheme making use of the efficient single-view coding tools used in High Efficiency Video Coding (HEVC) is provided. Although efficient single view compression can be obtained with HEVC, a multiview adaptation of this standard under development is proposed, offering additional coding gains. On average, for the texture information, the total bitrate can be reduced by 37.2% compared to simulcast HEVC. For depth map compression, gains largely depend on the quality of the captured content. Additionally, a forward compatible solution is proposed offering the possibility for a gradual upgrade from H.264/AVC based stereoscopic 3D systems to an HEVC-based autostereoscopic environment. With the proposed system, significant rate savings compared to Multiview Video Coding (MVC) are presented(1)
Loss-resilient Coding of Texture and Depth for Free-viewpoint Video Conferencing
Free-viewpoint video conferencing allows a participant to observe the remote
3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint
image is commonly synthesized using two pairs of transmitted texture and depth
maps from two neighboring captured viewpoints via depth-image-based rendering
(DIBR). To maintain high quality of synthesized images, it is imperative to
contain the adverse effects of network packet losses that may arise during
texture and depth video transmission. Towards this end, we develop an
integrated approach that exploits the representation redundancy inherent in the
multiple streamed videos a voxel in the 3D scene visible to two captured views
is sampled and coded twice in the two views. In particular, at the receiver we
first develop an error concealment strategy that adaptively blends
corresponding pixels in the two captured views during DIBR, so that pixels from
the more reliable transmitted view are weighted more heavily. We then couple it
with a sender-side optimization of reference picture selection (RPS) during
real-time video coding, so that blocks containing samples of voxels that are
visible in both views are more error-resiliently coded in one view only, given
adaptive blending will erase errors in the other view. Further, synthesized
view distortion sensitivities to texture versus depth errors are analyzed, so
that relative importance of texture and depth code blocks can be computed for
system-wide RPS optimization. Experimental results show that the proposed
scheme can outperform the use of a traditional feedback channel by up to 0.82
dB on average at 8% packet loss rate, and by as much as 3 dB for particular
frames
Livrable D5.2 of the PERSEE project : 2D/3D Codec architecture
Livrable D5.2 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 D5.2 du projet. Son titre : 2D/3D Codec architectur
Acquisition, compression and rendering of depth and texture for multi-view video
Three-dimensional (3D) video and imaging technologies is an emerging trend in the development of digital video systems, as we presently witness the appearance of 3D displays, coding systems, and 3D camera setups. Three-dimensional multi-view video is typically obtained from a set of synchronized cameras, which are capturing the same scene from different viewpoints. This technique especially enables applications such as freeviewpoint video or 3D-TV. Free-viewpoint video applications provide the feature to interactively select and render a virtual viewpoint of the scene. A 3D experience such as for example in 3D-TV is obtained if the data representation and display enable to distinguish the relief of the scene, i.e., the depth within the scene. With 3D-TV, the depth of the scene can be perceived using a multi-view display that renders simultaneously several views of the same scene. To render these multiple views on a remote display, an efficient transmission, and thus compression of the multi-view video is necessary. However, a major problem when dealing with multiview video is the intrinsically large amount of data to be compressed, decompressed and rendered. We aim at an efficient and flexible multi-view video system, and explore three different aspects. First, we develop an algorithm for acquiring a depth signal from a multi-view setup. Second, we present efficient 3D rendering algorithms for a multi-view signal. Third, we propose coding techniques for 3D multi-view signals, based on the use of an explicit depth signal. This motivates that the thesis is divided in three parts. The first part (Chapter 3) addresses the problem of 3D multi-view video acquisition. Multi-view video acquisition refers to the task of estimating and recording a 3D geometric description of the scene. A 3D description of the scene can be represented by a so-called depth image, which can be estimated by triangulation of the corresponding pixels in the multiple views. Initially, we focus on the problem of depth estimation using two views, and present the basic geometric model that enables the triangulation of corresponding pixels across the views. Next, we review two calculation/optimization strategies for determining corresponding pixels: a local and a one-dimensional optimization strategy. Second, to generalize from the two-view case, we introduce a simple geometric model for estimating the depth using multiple views simultaneously. Based on this geometric model, we propose a new multi-view depth-estimation technique, employing a one-dimensional optimization strategy that (1) reduces the noise level in the estimated depth images and (2) enforces consistent depth images across the views. The second part (Chapter 4) details the problem of multi-view image rendering. Multi-view image rendering refers to the process of generating synthetic images using multiple views. Two different rendering techniques are initially explored: a 3D image warping and a mesh-based rendering technique. Each of these methods has its limitations and suffers from either high computational complexity or low image rendering quality. As a consequence, we present two image-based rendering algorithms that improves the balance on the aforementioned issues. First, we derive an alternative formulation of the relief texture algorithm which was extented to the geometry of multiple views. The proposed technique features two advantages: it avoids rendering artifacts ("holes") in the synthetic image and it is suitable for execution on a standard Graphics Processor Unit (GPU). Second, we propose an inverse mapping rendering technique that allows a simple and accurate re-sampling of synthetic pixels. Experimental comparisons with 3D image warping show an improvement of rendering quality of 3.8 dB for the relief texture mapping and 3.0 dB for the inverse mapping rendering technique. The third part concentrates on the compression problem of multi-view texture and depth video (Chapters 5–7). In Chapter 5, we extend the standard H.264/MPEG-4 AVC video compression algorithm for handling the compression of multi-view video. As opposed to the Multi-view Video Coding (MVC) standard that encodes only the multi-view texture data, the proposed encoder peforms the compression of both the texture and the depth multi-view sequences. The proposed extension is based on exploiting the correlation between the multiple camera views. To this end, two different approaches for predictive coding of views have been investigated: a block-based disparity-compensated prediction technique and a View Synthesis Prediction (VSP) scheme. Whereas VSP relies on an accurate depth image, the block-based disparity-compensated prediction scheme can be performed without any geometry information. Our encoder adaptively selects the most appropriate prediction scheme using a rate-distortion criterion for an optimal prediction-mode selection. We present experimental results for several texture and depth multi-view sequences, yielding a quality improvement of up to 0.6 dB for the texture and 3.2 dB for the depth, when compared to solely performing H.264/MPEG-4AVC disparitycompensated prediction. Additionally, we discuss the trade-off between the random-access to a user-selected view and the coding efficiency. Experimental results illustrating and quantifying this trade-off are provided. In Chapter 6, we focus on the compression of a depth signal. We present a novel depth image coding algorithm which concentrates on the special characteristics of depth images: smooth regions delineated by sharp edges. The algorithm models these smooth regions using parameterized piecewiselinear functions and sharp edges by a straight line, so that it is more efficient than a conventional transform-based encoder. To optimize the quality of the coding system for a given bit rate, a special global rate-distortion optimization balances the rate against the accuracy of the signal representation. For typical bit rates, i.e., between 0.01 and 0.25 bit/pixel, experiments have revealed that the coder outperforms a standard JPEG-2000 encoder by 0.6-3.0 dB. Preliminary results were published in the Proceedings of 26th Symposium on Information Theory in the Benelux. In Chapter 7, we propose a novel joint depth-texture bit-allocation algorithm for the joint compression of texture and depth images. The described algorithm combines the depth and texture Rate-Distortion (R-D) curves, to obtain a single R-D surface that allows the optimization of the joint bit-allocation in relation to the obtained rendering quality. Experimental results show an estimated gain of 1 dB compared to a compression performed without joint bit-allocation optimization. Besides this, our joint R-D model can be readily integrated into an multi-view H.264/MPEG-4 AVC coder because it yields the optimal compression setting with a limited computation effort
3D video coding and transmission
The capture, transmission, and display of
3D content has gained a lot of attention in the last few
years. 3D multimedia content is no longer con fined to
cinema theatres but is being transmitted using stereoscopic
video over satellite, shared on Blu-RayTMdisks,
or sent over Internet technologies. Stereoscopic displays
are needed at the receiving end and the viewer needs to
wear special glasses to present the two versions of the
video to the human vision system that then generates
the 3D illusion. To be more e ffective and improve the
immersive experience, more views are acquired from a
larger number of cameras and presented on di fferent displays,
such as autostereoscopic and light field displays.
These multiple views, combined with depth data, also
allow enhanced user experiences and new forms of interaction
with the 3D content from virtual viewpoints.
This type of audiovisual information is represented by a
huge amount of data that needs to be compressed and
transmitted over bandwidth-limited channels. Part of
the COST Action IC1105 \3D Content Creation, Coding
and Transmission over Future Media Networks" (3DConTourNet)
focuses on this research challenge.peer-reviewe
Providing 3D video services: the challenge from 2D to 3DTV quality of experience
Recently, three-dimensional (3D) video has decisively burst onto the entertainment industry scene, and has arrived in households even before the standardization process has been completed. 3D television (3DTV) adoption and deployment can be seen as a major leap in television history, similar to previous transitions from black and white (B&W) to color, from analog to digital television (TV), and from standard definition to high definition. In this paper, we analyze current 3D video technology trends in order to define a taxonomy of the availability and possible introduction of 3D-based services. We also propose an audiovisual network services architecture which provides a smooth transition from two-dimensional (2D) to 3DTV in an Internet Protocol (IP)-based scenario. Based on subjective assessment tests, we also analyze those factors which will influence the quality of experience in those 3D video services, focusing on effects of both coding and transmission errors. In addition, examples of the application of the architecture and results of assessment tests are provided
Optimization of Occlusion-Inducing Depth Pixels in 3-D Video Coding
The optimization of occlusion-inducing depth pixels in depth map coding has
received little attention in the literature, since their associated texture
pixels are occluded in the synthesized view and their effect on the synthesized
view is considered negligible. However, the occlusion-inducing depth pixels
still need to consume the bits to be transmitted, and will induce geometry
distortion that inherently exists in the synthesized view. In this paper, we
propose an efficient depth map coding scheme specifically for the
occlusion-inducing depth pixels by using allowable depth distortions. Firstly,
we formulate a problem of minimizing the overall geometry distortion in the
occlusion subject to the bit rate constraint, for which the depth distortion is
properly adjusted within the set of allowable depth distortions that introduce
the same disparity error as the initial depth distortion. Then, we propose a
dynamic programming solution to find the optimal depth distortion vector for
the occlusion. The proposed algorithm can improve the coding efficiency without
alteration of the occlusion order. Simulation results confirm the performance
improvement compared to other existing algorithms
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