510 research outputs found
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
Low Complexity Mode Decision for 3D-HEVC
High efficiency video coding- (HEVC-) based 3D video coding (3D-HEVC) developed by joint collaborative team on 3D video coding (JCT-3V) for multiview video and depth map is an extension of HEVC standard. In the test model of 3D-HEVC, variable coding unit (CU) size decision and disparity estimation (DE) are introduced to achieve the highest coding efficiency with the cost of very high computational complexity. In this paper, a fast mode decision algorithm based on variable size CU and DE is proposed to reduce 3D-HEVC computational complexity. The basic idea of the method is to utilize the correlations between depth map and motion activity in prediction mode where variable size CU and DE are needed, and only in these regions variable size CU and DE are enabled. Experimental results show that the proposed algorithm can save about 43% average computational complexity of 3D-HEVC while maintaining almost the same rate-distortion (RD) performance
Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements
This paper addresses the problem of distributed coding of images whose
correlation is driven by the motion of objects or positioning of the vision
sensors. It concentrates on the problem where images are encoded with
compressed linear measurements. We propose a geometry-based correlation model
in order to describe the common information in pairs of images. We assume that
the constitutive components of natural images can be captured by visual
features that undergo local transformations (e.g., translation) in different
images. We first identify prominent visual features by computing a sparse
approximation of a reference image with a dictionary of geometric basis
functions. We then pose a regularized optimization problem to estimate the
corresponding features in correlated images given by quantized linear
measurements. The estimated features have to comply with the compressed
information and to represent consistent transformation between images. The
correlation model is given by the relative geometric transformations between
corresponding features. We then propose an efficient joint decoding algorithm
that estimates the compressed images such that they stay consistent with both
the quantized measurements and the correlation model. Experimental results show
that the proposed algorithm effectively estimates the correlation between
images in multi-view datasets. In addition, the proposed algorithm provides
effective decoding performance that compares advantageously to independent
coding solutions as well as state-of-the-art distributed coding schemes based
on disparity learning
Rate Distortion Analysis and Bit Allocation Scheme for Wavelet Lifting-Based Multiview Image Coding
This paper studies the distortion and the model-based bit allocation scheme of wavelet lifting-based multiview image coding. Redundancies among image views are removed by disparity-compensated wavelet lifting (DCWL). The distortion prediction of the low-pass and high-pass subbands of each image view from the DCWL process is analyzed. The derived distortion is used with different rate distortion models in the bit allocation of multiview images. Rate distortion models including power model, exponential model, and the proposed combining the power and exponential models are studied. The proposed rate distortion model exploits the accuracy of both power and exponential models in a wide range of target bit rates. Then, low-pass and high-pass subbands are compressed by SPIHT (Set Partitioning in Hierarchical Trees) with a bit allocation solution. We verify the derived distortion and the bit allocation with several sets of multiview images. The results show that the bit allocation solution based on the derived distortion and our bit allocation scheme provide closer results to those of the exhaustive search method in both allocated bits and peak-signal-to-noise ratio (PSNR). It also outperforms the uniform bit allocation and uniform bit allocation with normalized energy in the order of 1.7–2 and 0.3–1.4 dB, respectively
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