3,228 research outputs found
Error-resilient multi-view video plus depth based 3-D video coding
Three Dimensional (3-D) video, by definition, is a collection of signals that can provide depth perception of a 3-D scene. With the development of 3-D display
technologies and interactive multimedia systems, 3-D video has attracted significant interest from both industries and academia with a variety of applications. In order to provide desired services in various 3-D video applications, the multiview video plus depth (MVD) representation, which can facilitate the generation of virtual views, has been determined to be the best format for 3-D video data.
Similar to 2-D video, compressed 3-D video is highly sensitive to transmission errors due to errors propagated from the current frame to the future predicted frames. Moreover, since the virtual views required for auto-stereoscopic displays are rendered from the compressed texture videos and depth maps, transmission
errors of the distorted texture videos and depth maps can be further propagated to the virtual views. Besides, the distortions in texture and depth show different
effects on the rendering views. Therefore, compared to the reliability of the transmission of the 2-D video, error-resilient texture video and depth map coding
are facing major new challenges.
This research concentrates on improving the error resilience performance of MVD-based 3-D video in packet loss scenarios. Based on the analysis of the propagating behaviour of transmission errors, a Wyner-Ziv (WZ)-based error-resilient algorithm is first designed for coding of the multi-view video data or depth data. In this scheme, an auxiliary redundant stream encoded according to WZ principle
is employed to protect a primary stream encoded with standard multi-view video coding codec. Then, considering the fact that different combinations of texture and depth coding mode will exhibit varying robustness to transmission errors, a rate-distortion optimized mode switching scheme is proposed to strike the optimal trade-off between robustness and compression effciency. In this approach,
the texture and depth modes are jointly optimized by minimizing the overall distortion of both the coded and synthesized views subject to a given bit rate. Finally, this study extends the research on the reliable transmission of view synthesis prediction (VSP)-based 3-D video. In order to mitigate the prediction position error caused by packet losses in the depth map, a novel disparity vector correction algorithm is developed, where the corrected disparity vector is calculated from the depth error. To facilitate decoder error concealment, the depth
error is recursively estimated at the decoder.
The contributions of this dissertation are multifold. First, the proposed WZbased error-resilient algorithm can accurately characterize the effect of transmission
error on multi-view distortion at the transform domain in consideration of both temporal and inter-view error propagation, and based on the estimated distortion,
this algorithm can perform optimal WZ bit allocation at the encoder through explicitly developing a sophisticated rate allocation strategy. This proposed algorithm is able to provide a finer granularity in performing rate adaptivity
and unequal error protection for multi-view data, not only at the frame level, but also at the bit-plane level. Secondly, in the proposed mode switching scheme, a
new analytic model is formulated to optimally estimate the view synthesis distortion due to packet losses, in which the compound impact of the transmission distortions of both the texture video and the depth map on the quality of the
synthesized view is mathematically analysed. The accuracy of this view synthesis distortion model is demonstrated via simulation results and, further, the estimated distortion is integrated into a rate-distortion framework for optimal
mode switching to achieve substantial performance gains over state-of-the-art algorithms. Last, but not least, this dissertation provides a preliminary investigation
of VSP-based 3-D video over unreliable channel. In the proposed disparity vector correction algorithm, the pixel-level depth map error can be precisely estimated at the decoder without the deterministic knowledge of the error-free reconstructed depth. The approximation of the innovation term involved in depth error estimation is proved theoretically. This algorithm is very useful to conceal
the position-erroneous pixels whose disparity vectors are correctly received
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
New visual coding exploration in MPEG: Super-MultiView and free navigation in free viewpoint TV
ISO/IEC MPEG and ITU-T VCEG have recently jointly issued
a new multiview video compression standard, called 3D-HEVC,
which reaches unpreceded compression performances for linear,dense camera arrangements. In view of supporting future highquality,auto-stereoscopic 3D displays and Free Navigation virtual/augmented reality applications with sparse, arbitrarily arranged camera setups, innovative depth estimation and virtual view synthesis techniques with global optimizations over all camera views should be developed. Preliminary studies in response to the MPEG-FTV (Free viewpoint TV) Call for Evidence suggest these
targets are within reach, with at least 6% bitrate gains over 3DHEVC
technology
Learning to Predict Image-based Rendering Artifacts with Respect to a Hidden Reference Image
Image metrics predict the perceived per-pixel difference between a reference
image and its degraded (e. g., re-rendered) version. In several important
applications, the reference image is not available and image metrics cannot be
applied. We devise a neural network architecture and training procedure that
allows predicting the MSE, SSIM or VGG16 image difference from the distorted
image alone while the reference is not observed. This is enabled by two
insights: The first is to inject sufficiently many un-distorted natural image
patches, which can be found in arbitrary amounts and are known to have no
perceivable difference to themselves. This avoids false positives. The second
is to balance the learning, where it is carefully made sure that all image
errors are equally likely, avoiding false negatives. Surprisingly, we observe,
that the resulting no-reference metric, subjectively, can even perform better
than the reference-based one, as it had to become robust against
mis-alignments. We evaluate the effectiveness of our approach in an image-based
rendering context, both quantitatively and qualitatively. Finally, we
demonstrate two applications which reduce light field capture time and provide
guidance for interactive depth adjustment.Comment: 13 pages, 11 figure
Survey of image-based representations and compression techniques
In this paper, we survey the techniques for image-based rendering (IBR) and for compressing image-based representations. Unlike traditional three-dimensional (3-D) computer graphics, in which 3-D geometry of the scene is known, IBR techniques render novel views directly from input images. IBR techniques can be classified into three categories according to how much geometric information is used: rendering without geometry, rendering with implicit geometry (i.e., correspondence), and rendering with explicit geometry (either with approximate or accurate geometry). We discuss the characteristics of these categories and their representative techniques. IBR techniques demonstrate a surprising diverse range in their extent of use of images and geometry in representing 3-D scenes. We explore the issues in trading off the use of images and geometry by revisiting plenoptic-sampling analysis and the notions of view dependency and geometric proxies. Finally, we highlight compression techniques specifically designed for image-based representations. Such compression techniques are important in making IBR techniques practical.published_or_final_versio
Deep Depth Completion of a Single RGB-D Image
The goal of our work is to complete the depth channel of an RGB-D image.
Commodity-grade depth cameras often fail to sense depth for shiny, bright,
transparent, and distant surfaces. To address this problem, we train a deep
network that takes an RGB image as input and predicts dense surface normals and
occlusion boundaries. Those predictions are then combined with raw depth
observations provided by the RGB-D camera to solve for depths for all pixels,
including those missing in the original observation. This method was chosen
over others (e.g., inpainting depths directly) as the result of extensive
experiments with a new depth completion benchmark dataset, where holes are
filled in training data through the rendering of surface reconstructions
created from multiview RGB-D scans. Experiments with different network inputs,
depth representations, loss functions, optimization methods, inpainting
methods, and deep depth estimation networks show that our proposed approach
provides better depth completions than these alternatives.Comment: Accepted by CVPR2018 (Spotlight). Project webpage:
http://deepcompletion.cs.princeton.edu/ This version includes supplementary
materials which provide more implementation details, quantitative evaluation,
and qualitative results. Due to file size limit, please check project website
for high-res pape
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