Multiview video in "texture-plus-depth" format enables decoder to synthesize freely chosen intermediate views
for enhanced visual experience. Nevertheless, transmission of multiple texture and depth maps over bandwidthconstrained
and loss-prone networks is challenging, especially for conferencing applications with stringent deadlines.
In this paper, we examine the problem of loss-resilient coding of depth maps by exploiting two observations.
First, different depth macroblocks have significantly different error sensitivities with respect to the reconstructed
images. Second, unlike texture, the relative overhead of using reference pictures with large prediction distance is
low for depth maps. This motivates our approach of assigning a weight to represent the varying error sensitivity
of each macroblock and using such weights to guide selection of reference frames. Results show that (1) errors in
depth maps in sequence with high motion yields significant drop in quality in reconstructed images, and (2) that
the proposed scheme can efficiently maintain the quality of reconstructed images even at relatively high packet
loss rates of 3-5%.
KEYWORDS: Computer programming, Cameras, Motion estimation, Video, Video surveillance, Quantization, Video coding, Super resolution, Surveillance systems, Standards development
This work presents a new distributed multiview coding framework, based on the H.264/AVC standard operating
with mixed resolution frames. It allows for a scalable complexity transfer from the encoder to the decoder, which
is particularly suited for low-power video applications, such as multiview surveillance systems. Greater quality
sequences are generated by exploiting the spatial and temporal correlation between views at the decoder. The
results show a good potential for objective quality improvement over simulcast coding, with no extra rate cost.
A spatial-resolution reduction based framework for incorporation of a Wyner-Ziv frame coding mode in existing video
codecs is presented, to enable a mode of operation with low encoding complexity. The core Wyner-Ziv frame coder
works on the Laplacian residual of a lower-resolution frame encoded by a regular codec at reduced resolution. The
quantized transform coefficients of the residual frame are mapped to cosets to reduce the bit-rate. A detailed rate-distortion
analysis and procedure for obtaining the optimal parameters based on a realistic statistical model for the
transform coefficients and the side information is also presented. The decoder iteratively conducts motion-based side-information
generation and coset decoding, to gradually refine the estimate of the frame. Preliminary results are presented
for application to the H.263+ video codec.
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