Samuel Cheng

Distributed source coding for sensor networks

In recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf previous milestones in the information revolution. Realizing the great promise of sensor networks requires more than a mere advance in individual technologies. It relies on many components working together in an efficient, unattended, comprehensible, and trustworthy manner. One of the enabling technologies in sensor networks is the distributed source coding (DSC), which refers to the compression of the multiple correlated sensor outputs that does not communicate with each other. DSC allows a many-to-one video coding paradigm that effectively swaps encoder-decoder complexity with respect to conventional video coding, thereby representing a fundamental concept shift in video processing. This article has presented an intensive discussion on two DSC techniques, namely Slepian-Wolf coding and Wyner-Ziv coding. The Slepian and Wolf coding have theoretically shown that separate encoding is as efficient as joint coding for lossless compression in channel coding.

Lossy-to-lossless compression of medical volumetric data using three-dimensional integer wavelet transforms

We study lossy-to-lossless compression of medical volumetric data using three-dimensional (3-D) integer wavelet transforms. To achieve good lossy coding performance, it is important to have transforms that are unitary. In addition to the lifting approach, we first introduce a general 3-D integer wavelet packet transform structure that allows implicit bit shifting of wavelet coefficients to approximate a 3-D unitary transformation. We then focus on context modeling for efficient arithmetic coding of wavelet coefficients. Two state-of-the-art 3-D wavelet video coding techniques, namely, 3-D set partitioning in hierarchical trees (Kim et al., 2000) and 3-D embedded subband coding with optimal truncation (Xu et al., 2001), are modified and applied to compression of medical volumetric data, achieving the best performance published so far in the literature-both in terms of lossy and lossless compression.

Successive refinement for the Wyner-Ziv problem and layered code design

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.

Slepian-Wolf Coded Nested Lattice Quantization for Wyner-Ziv Coding: High-Rate Performance Analysis and Code Design

Nested lattice quantization provides a practical scheme for Wyner-Ziv coding. This paper examines the high-rate performance of nested lattice quantizers and gives the theoretical performance for general continuous sources. In the quadratic Gaussian case, as the rate increases, we observe an increasing gap between the performance of finite-dimensional nested lattice quantizers and the Wyner-Ziv distortion-rate function. We argue that this is because the boundary gain decreases as the rate of the nested lattice quantizers increases. To increase the boundary gain and ultimately boost the overall performance, a new practical Wyner-Ziv coding scheme called Slepian-Wolf coded nested lattice quantization (SWC-NQ) is proposed, where Slepian-Wolf coding is applied to the quantization indices of the source for the purpose of compression with side information at the decoder. Theoretical analysis shows that for the quadratic Gaussian case and at high rate, SWC-NQ performs the same as conventional entropy-coded lattice quantization with the side information available at both the encoder and the decoder. Furthermore, a nonlinear minimum mean-square error (MSE) estimator is introduced at the decoder, which is theoretically proven to degenerate to the linear minimum MSE estimator at high rate and experimentally shown to outperform the linear estimator at low rate. Practical designs of one- and two-dimensional nested lattice quantizers together with multilevel low-density parity-check (LDPC) codes for Slepian-Wolf coding give performance close to the theoretical limits of SWC-NQ

On packetization of embedded multimedia bitstreams

We study the problem of packetizing embedded multimedia bitstreams to improve the error resilience of source (compression) codes. This problem is important because of the increasing popularity of embedded compression methodology and its suitability for scalable streaming media over IP or/and mobile IP. We study various packetization schemes against packet erasure at both low and high bit rates. Maximizing packetization efficiency for embedded bitstreams is formulated as a discrete optimization problem and globally optimal packetization (OP) algorithms are proposed under different settings. Suboptimal packetization algorithms are also devised to reduce the complexity of the OP algorithms. In order to assess their effectiveness, the proposed packetization algorithms are used to packetize embedded image and video bitstreams with simulated packet loss. Experimental results show that our OP algorithms slightly outperforms suboptimal ones. In addition to confirming the superiority of the OP algorithms, these results also provide justification of heuristic packetization methods published in the literature.

Slepian-Wolf coded nested quantization (SWC-NQ) for Wyner-Ziv coding: performance analysis and code design

This paper examines the high-rate performance of low-dimensional nested lattice quantizers for the quadratic Gaussian Wyner-Ziv problem, using a pair of nested lattices with the same dimensionality. As the rate increases, the gap increases between the performances of low dimensional nested lattice quantizers and the Wyner-Ziv rate-distortion function. This gap is due to the relatively weak channel coding component (or coarse lattice) in the nested lattice pair. To enhance the lattice channel code and boost the overall performance, Slepian-Wolf coding is applied to the quantization indices to achieve further compression. Thereby a Wyner-Ziv coding paradigm is introduced using Slepian-Wolf coded nested lattice quantization (SWC-NQ). Theoretical analysis and simulation results show that, for the quadratic Gaussian source and at high rate, SWC-NQ performs the same as traditional entropy-constrained lattice quantization with side information available at both the encoder and decoder.

Wyner-Ziv coding based on TCQ and LDPC codes

This paper considers trellis coded quantization (TCQ) and low-density parity-check (LDPC) codes for the quadratic Gaussian Wyner-Ziv coding problem. After TCQ of the source X, LDPC codes are used to implement Slepian-Wolf coding of the quantized source Q(X) with side information Y at the decoder. Assuming 256-state TCQ and ideal Slepian-Wolf coding in the sense of achieving the theoretical limit H(Q(X)|Y ), we experimentally show that Slepian-Wolf coded TCQ performs 0.2 dB away from the Wyner-Ziv distortion-rate function DWZ(R) at high rate. This result mirrors that of entropy-constrained TCQ in classic source coding of Gaussian sources. Furthermore, using 8,192-state TCQ and assuming ideal Slepian-Wolf coding, our simulations show that Slepian-Wolf coded TCQ performs only 0.1 dB away from DWZ(R) at high rate. These results establish the practical performance limit of Slepian-Wolf coded TCQ for quadratic Gaussian Wyner-Ziv coding. Practical designs give performance very close to the theoretical limit. For example, with 8,192-state TCQ, irregular LDPC codes for Slepian-Wolf coding and optimal non-linear estimation at the decoder, our performance gap to DWZ(R) is 0.20 dB, 0.22 dB, 0.30 dB, and 0.93 dB at 3.83 bit per sample (b/s), 1.83 b/s, 1.53 b/s, and 1.05 b/s, respectively. When 256-state 4-D trellis-coded vector quantization instead of TCQ is employed, the performance gap to DWZ(R) is 0.51 dB, 0.51 dB, 0.54 dB, and 0.80 dB at 2.04 b/s, 1.38 b/s, 1.0 b/s, and 0.5 b/s, respectively.

3-D Face Recognition Based on Warped Example Faces

In this paper, we describe a novel 3-D face recognition scheme for 3-D face recognition that can automatically identify faces from range images, and is insensitive to holes, facial expression, and hair. In our scheme, a number of carefully selected range images constitute a set of example faces, and another range image is chosen as a ldquogeneric face.rdquo The generic face is then warped to match each of the example faces in the least mean square sense. Each such warp is specified by a vector of displacement values. In feature extraction operation, when a target face image comes in, the generic face is warped to match it. The geometric transformation used in the warping is a linear combination of the example face warping vectors. The coefficients in the linear combination are adjusted to minimize the root mean square error. After the matching process is complete, the coefficients of the composite warp are used as features and passed to a Mahalanobis-distance-based classifier for face recognition. Our technique is tested on a data set containing more than 600 range images. Experimental results in the access-control scenario show the effectiveness of the extracted features.

Non-ubiquitous digital watermarking for record indexing and integrity protection of medical images

Two novel non-ubiquitous digital watermarking schemes are proposed and evaluated in parallel (one in the spatial domain and one in the wavelet domain) for the purpose of record indexing and integrity protection of medical images. A fingerprint of the regions of interest (ROI) in a medical image is generated using the MD5 message-digest algorithm [R. Rivest, April 1992], and it is embedded along with the medical record information into the non-ROI parts of the image. Our experiments show that with both schemes, a payload of 420 bits of information can be achieved watermarking clinical cytogenetic images, without affecting image content in the ROI nor incurring perceptible differences between original and watermarked images. Furthermore, the resulting watermarked images can survive common image processing operations such as cropping, JPEG compression, sharpening, and their combinations.

Computing the channel capacity and rate-distortion function with two-sided state information

In this correspondence, we present iterative algorithms that numerically compute the capacity-power and rate-distortion functions for coding with two-sided state information. Numerical examples are provided to demonstrate efficiency of our algorithms.