We examine successive refinement for the Wyner-Ziv problem described in a recent paper by Steinberg and Merhav, where the authors showed that if the side information for all stages is identical, then the jointly Gauss...
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We examine successive refinement for the Wyner-Ziv problem described in a recent paper by Steinberg and Merhav, where the authors showed that if the side information for all stages is identical, then the jointly Gaussian source with squared error distortion measure is successively refinable. We first extend this result to the case where the difference between the source and the side information is Gaussian and independent of the side information. As a byproduct, we give an alternative proof that the Wyner-Ziv problem for these sources has no rate loss-a result that was recently shown by Pradhan et al. through invoking the duality between the Gaussian Wyner-Ziv problem and the Costa problem. We then perform layered Wyner-Ziv code design for this general type of source based on nested scalar quantization, bit-plane coding, and low-density parity check (LDPC) code-based Slepian-Wolf coding (source coding with side information). We show that density evolution can be used to analyze the Slepian-Wolf code performance, provided that certain symmetry conditions, which have been dubbed dual symmetry, are satisfied by the hypothetical channel between the source and the side information. We also show that the dual symmetry condition is indeed satisfied by the hypothetical channel in our Slepian-Wolf coding setup. This justifies the use of density evolution in our LDPC code-based Slepian-Wolf code design for Wyner-Ziv coding. For the jointly Gaussian source, our layered coder performs 1.29 to 3.45 dB from the Wyner-Ziv bound for rates ranging from 0.47 to 4.68 bits per sample. When the side information is Laplacian and the source equals the side information plus an independent Gaussian noise term, our layered coder performs 1.33 to 3.90 dB from the Wyner-Ziv bound for rates ranging from 0.48 to 4.64 bits per sample.
We propose a data-driven approach to explicitly learn the progressive encoding of a continuous source, which is successively decoded with increasing levels of quality and with the aid of correlated side information. T...
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ISBN:
(纸本)9798350344868;9798350344851
We propose a data-driven approach to explicitly learn the progressive encoding of a continuous source, which is successively decoded with increasing levels of quality and with the aid of correlated side information. This setup refers to the successive refinement of the Wyner-Ziv coding problem. Assuming ideal Slepian-Wolf coding, our approach employs recurrent neural networks (RNNs) to learn layered encoders and decoders for the quadratic Gaussian case. The models are trained by minimizing a variational bound on the rate-distortion function of the successively refined Wyner-Ziv coding problem. We demonstrate that RNNs can explicitly retrieve layered binning solutions akin to scalable nestedquantization. Moreover, the rate-distortion performance of the scheme is on par with the corresponding monolithic Wyner-Ziv coding approach and is close to the ratedistortion bound.
Existing 3-D dynamic mesh compression methods directly explore temporal redundancy by predictive coding and the coded bitstreams are sensitive to transmission errors. In this paper, an efficient and error-resilient co...
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ISBN:
(纸本)9780819469946
Existing 3-D dynamic mesh compression methods directly explore temporal redundancy by predictive coding and the coded bitstreams are sensitive to transmission errors. In this paper, an efficient and error-resilient compression paradigm based on Wyner-Ziv coding (WZC) is proposed. We first apply an anisotropic wavelet transform (AWT) on each frame to explore their spatial redundancy. Then the wavelet coefficients of every frame are compressed by a Wyner-Ziv codec which is composed of a nestedscalar quantizer and a turbo codes based Slepian-Wolf codec. Benefiting from the inherent robustness of WZC, the proposed coding scheme can alleviates the problem of error-propagation associated with conventional predictive coding scheme. Furthermore, based on wavelet transform, our method can be extended to support progressive coding which is desirable for the streaming of 3D meshes. Experimental results show that our scheme is competitive with other compression methods in compression performance. Moreover, our method is more robust when transmission error occurs.
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