Compression of binary sources with side information at the decoder using LDPC codes

Liveris, A.D.; Xiong, Zixiang; Georghiades, C.N.

doi:10.1109/lcomm.2002.804244

Cited by 546 publications

(405 citation statements)

References 12 publications

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“…The decoder must estimatex from s x , y, and the correlation factor p:x is the closest sequence to y with the syndrome s x . When the source has no memory and when the channel code is an LDPC code, this search can be efficiently performed with a modified Message-Passing (MP) [5].…”

Section: Estimation-decoding Em Algorithmmentioning

confidence: 99%

“…P θ (Xn = 0|σn, y, sx) = e Pn 1 + e Pn P θ (Xn = 1|σn, y, sx) = 1 − P θ (Xn = 0|σn, y, sx) (5) In this LDPC decoding, we have decided to propagate LLRs, which implies their conversion to probabilities in (5), for use at the maximization (3.2) and with the BCJR (3.3).…”

Section: Ldpc Decoding Sum-product Algorithmmentioning

confidence: 99%

“…A way to achieve this compression rate is to use channel codes, this solution is shown to be optimal in [3] in the sense that a capacity achieving channel code can be turned into an optimal DSC code. The first practical DSC solutions used syndrome-based channel codes [4], as LowDensity Parity-Check (LDPC) codes [5].…”

Section: Introductionmentioning

confidence: 99%

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Hidden Markov Model for distributed video coding

Toto-Zarasoa

Roumy

Guillemot

2010

2010 IEEE International Conference on Image Processing

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This paper addresses the problem of asymmetric distributed coding of correlated binary Hidden Markov Sources, modeled as a Gilbert-Elliott process. The model parameters are estimated with an estimation-decoding ExpectationMaximization algorithm. The rate gain obtained by accounting for the memory of the sources is first assessed theoretically. The method is then shown to improve the PSNR versus rate performance of a Distributed Video Coding system, based on Low-Density Parity-Check codes.

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Section: Estimation-decoding Em Algorithmmentioning

confidence: 99%

Section: Ldpc Decoding Sum-product Algorithmmentioning

confidence: 99%

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Hidden Markov Model for distributed video coding

Toto-Zarasoa

Roumy

Guillemot

2010

2010 IEEE International Conference on Image Processing

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“…A Slepian-Wolf decoder reconstructs X from the Slepian-Wolf bitstream S(X) using Y as side information. Decoding is via LDPC belief propagation [9] initialized according to the statistics of the legitimate channel state at the worst permissible quality for the given original image. Finally, the image digest of X is computed and compared to the image digest, decrypted from the digital signature D(X, K s ) using a public key.…”

Section: Introductionmentioning

confidence: 99%

Image Authentication and Tampering Localization using Distributed Source Coding

Varodayan

Girod

2007

2007 IEEE 9th Workshop on Multimedia Signal Processing

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Abstract-Media authentication is important in content delivery via untrusted intermediaries, such as peer-to-peer (P2P) file sharing. Many differently encoded versions of a media file might exist. Our previous work applied distributed source coding to distinguish the legitimate diversity of encoded images from tampering. An authentication decoder was supplied with a Slepian-Wolf encoded lossy version of the image as authentication data. Distributed source coding provided the desired robustness against legitimate encoding variations, while detecting illegitimate modification.We augment the decoder to localize tampering in an image already deemed to be inauthentic. The localization decoder requires only incremental localization data beyond the authentication data since we use rate-adaptive distributed source codes. Both decoders perform joint bitplane decoding, rather than conditional bitplane decoding. Our results demonstrate that tampered image blocks can be identified with high probability using authentication plus localization data of only a few hundred bytes for a 512x512 image.

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“…defined in the late seventies, the first constructive design of a DSC scheme using channel codes [3] has been formulated only recently. It has further motivated several researchers to build DSC solutions using different channel codes based on Turbo or LDPC codes [7]. Recently, distributed video encoders have been proposed using the Wyner-Ziv theorem [4]- [6], in an effort to reduce the complexity of the encoding process by shifting the motion estimation procedure to the decoder.…”

Section: Introductionmentioning

confidence: 99%

Distributed Coding of Multiresolution Omnidirectional Images

Thirumalai

Tošić

Frossard

2007

2007 IEEE International Conference on Image Processing

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This paper addresses the problem of compact representation of a 3D scene, captured by distributed omnidirectional cameras. As the images from the sensors are likely to be correlated in most practical scenarios, we build a distributed algorithm based on coding with side information. A reference image is processed with a wavelet transform and progressively encoded. The Wyner-Ziv images undergo a multiresolution representation, and the generated bitplanes are channel encoded with LDPC codes. The central decoder eventually reconstructs the Wyner-Ziv images given by the syndrome bits from the channel codes using the reference omnidirectional image. It also iteratively implements motion estimation on the 2-sphere in order to improve the side information. Experimental results demonstrate that distributed coding improves the rate-distortion performance for coding a set of omnidirectional images when compared to independent coding solutions. The proposed method can further be extended to the decoding of multiple Wyner-Ziv images using one single reference omnidirectional image. Hence, it achieves a reduced overall coding rate compared to disparity-based schemes. In addition, it does not require explicit knowledge of the camera parameters nor precise calibration, which is certainly interesting in camera networks.

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Compression of binary sources with side information at the decoder using LDPC codes

Cited by 546 publications

References 12 publications

Hidden Markov Model for distributed video coding

Hidden Markov Model for distributed video coding

Image Authentication and Tampering Localization using Distributed Source Coding

Distributed Coding of Multiresolution Omnidirectional Images

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