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A new wavelet algorithm for on-line improvement of an existing polynomial model of nonlinearity in a Hammerstein system is proposed and its properties are examined. The algorithm employs wavelet bases on interval. Convergence of the resulting assembly, comprising the parametric polynomial model and a nonparametric wavelet add-on, to the system nonlinearity is shown. Rates of convergence for uniformly smooth and piecewise smooth nonlinearities with discontinuities are both established. Index Terms--Hammerstein system, nonlinear system identification, order statistics, polynomial models, semiparametric approach, wavelet bypass, wavelet regression estimate.

In this letter, we investigate the error resilience properties of JPEG2000. We identify the dependencies among coding passes of a codeblock codestream, and determine the sections of the codestream that can be salvaged in the presence of errors. In our analysis, we consider the effects of mode variations provided by the standard. The proposed methods are derived using the existing dependency structure of coding passes and do not require a substantial increase in computational complexity of the decoder. Experimental results indicate that the proposed methods can improve the error resilience performance substantially. Index Terms--Decoding dependency, error resilience, JPEG2000.

The fast rate-optimal rate allocation algorithm used selects a subchannel and channel code rate based on signal-to-noise ratios of the subchannels and gives unequal error protection.

A joint source-channel decoding scheme is proposed that is based on JPEG2000 codestreams and LDPC channel codes. The results have shown that this method needs very less iterations and has improved overall system performance.

An important issue in wireless sensor networks is the limited energy and bandwidth resources. A novel theory is developed here for maximizing the lifetime of unicast multihop wireless sensor networks. An optimal centralized solution is presented in the form of an iterative algorithm. The algorithm attempts to find a Pareto-optimal (PO) solution. In the first iteration, the minimum lifetime of the network is maximized. If the solution is not PO, a second iteration is performed which maximizes the second minimum lifetime, subject to the minimum lifetime being maximum. At the nth iteration, the algorithm maximizes the nth minimum lifetime subject to the (n - 1)th minimum lifetime being maximum, subject to the (n - 2)th minimum lifetime being maximum, etc. The algorithm can be stopped at any iteration n. The presented solution assumes static network conditions. Index Terms--Communication system routing, routing, sensor networks.

We consider a network of imaging sensors. We address the problem of energy-efficient communication of the measurements of the sensors. A novel algorithm is presented for the purpose of exploiting intersensor and intrasensor correlation, which is inherent in a network of imaging sensors. The collaborative algorithm is used in conjunction with a cooperative multihop routing strategy to maximize the lifetime of the network. The algorithm is demonstrated to achieve an average gain in the lifetime as high as 3.2 over previous methods. OCIS codes: 100.0100, 200.3050.

One of the goals of telemedicine is to enable remote visualization and browsing of medical volumes. There is a need to employ scalable compression schemes and efficient client-server models to obtain interactivity and an enhanced viewing experience. First, we present a scheme that uses JPEG2000 and JPIP (JPEG2000 Interactive Protocol) to transmit data in a multi-resolution and progressive fashion. The server exploits the spatial locality offered by the wavelet transform and packet indexing information to transmit, in so far as possible, compressed volume data relevant to the clients query. Once the client identifies its volume of interest (VOI), the volume is refined progressively within the VOI from an initial lossy to a final lossless representation. Contextual background information can also be made available having quality fading away from the VOI. Second, we present a prioritization that enables the client to progressively visualize scene content from a compressed file. In our specific example, the client is able to make requests to progressively receive data corresponding to any tissue type. The server is now capable of reordering the same compressed data file on the fly to serve data packets prioritized as per the client's request. Lastly, we describe the effect of compression parameters on compression ratio, decoding times and interactivity. We also present suggestions for optimizing JPEG2000 for remote volume visualization and volume browsing applications. The resulting system is ideally suited for client-server applications with the server maintaining the compressed volume data, to be browsed by a client with a low bandwidth constraint. Index Terms--Code-block, discrete wavelet transform (DWT), embedded coding, JPEG2000, JPIP, precinct, scalable compression.

An algorithm is presented that efficiently exploits inter- and intrasensor correlation for the purpose of power-constrained distributed transmission of sensor-network imagery. Results demonstrate that gains in network lifetime up to 114e obtained when using the suggested algorithm with lossless compression, and much larger gains are achieved when lossy compression is employed.

A joint source/channel coding algorithm is proposed for the transmission of multiple image sources over memoryless channels. The proposed algorithm uses a quality scalable image coder to optimally allocate a limited bit budget among all the sources to achieve the optimal overall distortion reduction for the multiple reconstructed images. In addition to the conventional un gain, it provides channel multiplexing gain, which can be much more significant. Furthermore, an extended scheme is proposed to provide flexibility between the optimization performance and complexity. Index Terms--Joint source/channel coding, scalable source coding, un (UEP).

We develop novel methods for compressing volumetric imagery that has been generated by single-platform (mobile) range sensors. We exploit the correlation structure inherent in multiple views in order to improve compression efficiency. We show that, for lossless compression, three-dimensional volumes compress more efficiently than two-dimensional (2D) images by a factor of 60%. Furthermore, our error metric for lossy compression suggests that accumulating more than nine range images in one volume before compression yields as much as a 99% improvement in compression performance over 2D compression. OCIS codes: 100.6890, 100.200, 110.6880, 200.3050, 200.4560.

Two leaky- bucket rate allocation methods that provide constant quality video under buffer constraint are proposed. The goal is to allocate the capacity of the channel between sequences to achieve constant quality across all sequences and the results demonstrate substantial benefits from the use of Motion JPEG2000.

A fully scalable image coder is presented and the lossless and lossy performances are investigated.

Research describing an improved image restoration algorithm is presented. Using original and diffraction-limited image pairs test results show that the nonlinear algorithm produces super-resolution.

An improved version of an algorithm designed to perform image restoration through use of nonlinear interpolative vector quantization is discussed. Using lapped bocks in the decoding process is helpful. The algorithm has been trained on original and diffraction-limited image paris. The nonlinearity of the algorithm gives super-resolution in restored images.

We present a new image-restoration algorithm for binary-valued imagery. A trellis-based search method is described that exploits the finite alphabet of the target imagery. This algorithm seeks the maximum-likelihood solution to the image-restoration problem and is motivated by the Viterbi algorithm for traditional binary data detection in the presence of intersymbol interference and noise. We describe a blockwise method to restore two-dimensional imagery on a row-by-row basis and in which a priori knowledge of image pixel correlation structure can be included through a modification to the trellis transition probabilities. The performance of the new Viterbi-based algorithm is shown to be superior to Wiener filtering in terms of both bit error rate and visual quality. Algorithmic choices related to trellis state configuration, complexity reduction, and transition probability selection are investigated, and various trade-offs are discussed.

Building from uniform quantization thresholds, the authors present a new method of trellis coded quantization named universal trellis coded quantization (UTCQ). The UTCQ's symmetry to trellis supersets, and its use to improve the picture performance of particular images at low birates are discussed.

Techniques are presented for allowing progressive transmission for situations involving the application of trellis coded quantization to wavelet coefficients, along with a process for approximately inverting trellis coded quantization if least significant bits are absent.

A data compression method is presented based on specific bit allocations within synthetic aperture radar systems. The process includes applying inverse Fourier transform and aperture weighting operations to radar system phase history data, then attaching a gain factor to each sample, which directs bit allocation.

Synthetic aperture radar (SAR) is a remote-sensing technology that uses the motion of the radar transmitter to synthesize an antenna aperture much larger than the actual antenna aperture to yield high-spatial resolution radar images. In this paper, trellis-coded quantization (TCQ) techniques are shown to provide a high-performance, low bit-error sensitivity solution to the problem of downlink data rate reduction for SAR systems. Trellis-coded vector quantization (TCVQ) and universal TCQ coding systems are discussed, implemented, and compared with other data compression schemes [block adaptive quantization (BAQ) and VQ] that can be used to compress SAR phase history data. Index Terms - Compression, phase history, quantization, radar, SAR.

This paper proposes a new error concealment algorithm for packet video, effectively eliminating error propagation effects. Most standard video CODEC's use motion compensation to remove temporal redundancy. With such motion-compensated interframe processing, any packet loss may generate serious error propagation over more than ten consecutive frames. This kind of error propagation leads to perceptually annoying artifacts. Thus, proper error concealment algorithms need to be used to reduce this effect. The proposed algorithm adopts a one-pixel block overlap structure. With the redundancy information from the damaged frame and the following frames, the proposed POCS-based method can have consistently high performance in error concealment. According to experimental results, the proposed algorithm can successfully eliminate visible error propagation. In addition, the proposed algorithm is very robust. Experimental results show that it can have good error concealment results, even when the damaged frame loses all DCT coefficients. Index Terms - Error concealment, packet video, projection video compression.

Image compression research has established a near-lossless imaging system which centers around differential pulse code modulation techniques (DPCM). It reduces quantized prediction error sequences to a minimum and sets up contexts for a conditional prediction error model. The DPCM trellis is illustrated, together with encoding rate tables.

An investigation of various classification schemes applied to the subband coding of images found that the dependence between classification maps was used in encoded by constraining the classification maps. The complexities of the iterative algorithms used in the schemes made analysis of their functionalities very difficult. Simulation, however, demonstrated the value of the schemes and their algorithms. The results of the simulation indicate that the fourth scheme generally performed better than the other schemes.

A hyperspectral image coding approach that involves the use of the two-dimensional discrete cosine transform (2-D DCT) and entropy-constrained predictive trellis coded quantization (ECPTCQ) is developed. The 2-D DCT is employed for the transformation of all non-overlapping eight-by-eight blocks of each band. ECPTCQ is then utilized for encoding the transform coefficients in the spectral dimension. Iterative optimization algorithms are applied to codebook design and rate allocation.

A predictive image coder uses adaptive recursive interpolative DPCM along with entropy-constrained trellis-coded quantization and optimal rate allocation to reduce decoder complexity. It is able to provide the same range of signal-to-noise ratios as advanced transform coders making it a high-performance adaptive RIDPCM-ECTCQ image coder.

Modulation coding, to limit the number of consecutive zeros in a data stream, is essential in digital magnetic recording/playback systems. Additionally, such systems require error-correction coding to ensure that the decoded output matches the recorder input, even if noise is present. Typically, these two coding steps have been performed independently, although various methods of combining them into one step have recently appeared. Another recent development is two-dimensional modulation codes, which meet runlength constraints using several parallel recording tracks, significantly increasing channel capacity. This correspondence combines these two ideas. Previous techniques (both block and trellis structures) for combining error correction and modulation coding are surveyed, with discussion of their applicability in the two-dimensional case. One approach, based on trellis-coded modulation, is explored in detail, and a class of codes is developed which exploits the increased capacity to achieve good error-correcting ability at the same rate as common non-error-correcting one-dimensional codes. Index Terms - Constrained channels, constrained codes, (d, k) codes, recording codes.

A new construction for n-track (d, k) codes with redundancy r, referred to as (d, k; n, r) codes, is presented. This construction applies single-track (d, k + [Delta]k) codes (with certain extra constraints and appropriate amounts of delay) on each of the n tracks. This construction achieves a large part of the capacity increases possible when using (d, k; n, r) codes, has simple encoders and decoders, and exhibits considerable robustness to faulty tracks. It is shown that under this construction, (d, k; n, r) codes can achieve at least (n - r - 1)/n * 100% of the gap in capacity between conventional (d, k) and (d, [infinity]) codes. Several practical examples of (d, k; n, r) codes under this construction are presented. Index Terms - (d, k) codes, modulation codes, run-length-limited codes, synchronization, capacity.

The use of entropy-constrained trellis-coded quantization for encoding monochrome and color images was studied. The technique, which achieves efficient data compression, was implemented on images decomposed into frequency subbands by the discrete wavelet transform. A performance evaluation, using quadrature mirror filters and nine-tap spline filters, was carried out. It reproduced images without fuzziness or high-frequency background noise.

Two systems are presented for compression of hyper-spectral imagery which utilize trellis coded quantization (TCQ). Specifically, the first system uses TCQ to encode transform coefficients resulting from the application of an 8 X 8 X 8 discrete cosine transform (DCT). The second systems uses DPCM to spectrally decorrelate the data, while a 2-D DCT coding scheme is used for spatial decorrelation. Side information and rate allocation strategies are discussed. Entropy-constrained code-books are designed using a modified version of the generalized Lloyd algorithm. These entropy constrained systems achieve compression ratios of greater than 70:1 with average PSNR's of the coded hyperspectral sequences exceeding 40.0 dB.

This paper investigates the performance of different adaptive prediction algorithms for use in a low delay, high-quality speech coder at 16 kbit/s. The system to be studied is based on trellis coded quantization (TCQ). In the work presented, we demonstrate that when trellises based on feedback-free convolutional encoding circuits are employed, error propagation in TCQ systems is not a serious problem. Performance comparisons are made using a fixed predictor and four different adaptive predictors. Informal listening tests reveal that the encoded speech is of good communications quality at an error rate of 10 to the -3. In an ideal channel, a system employing an all-pole least squares lattice predictor with pitch prediction produces reconstructed speech that is nearly indistinguishable from the original.

Multitrack run-length-limited (d, k) modulation codes were recently introduced as a method to increase storage densities in magnetic and optical recording systems. These codes are a generalization of the usual run-length-limited (d, k) codes and provide for increased storage density by relaxing the k-constraint and encoding multiple tracks in parallel. Previously, n-track (d, k) codes used all n tracks to satisfy the k constraint causing synchronization to be completely intolerant of faulty tracks. In this paper, we present multitrack (d, k) codes which allow for r faulty tracks by mandating that all subsets of n - r tracks satisfy the k constraint.

Multitrack run-length-limited (d, k) modulation codes were recently introduced as a method to increase storage densities in magnetic and optical recording systems. These codes are a generalization of the usual run-length-limited (d, k) codes and provide for increased storage density by relaxing the k-constraint and encoding multiple tracks in parallel. This paper introduces a method to construct multitrack (d, k) block codes which are implemented via an enumeration scheme based on the trellis description of the (d, k) constraints. This implementation results in memory requirements which increase only linearly with block length as opposed to the exponential increases arising from look-up table implementations of block codes.

The application of transform coding using trellis coded quantization (TCQ) to encode monochrome and color images is investigated. Specifically, TCQ is used to encode transform coefficients resulting from applying a 16 X 16 discrete cosine transform (DCT) to 8-b gray level and 24-b color images. For the color images, the red, green, and blue (RGB) planes are transformed into the NTSC transmission primaries (Y, I, and Q) before the DCT is applied. Rate allocation schemes for encoding the transform coefficients and side information are presented and discussed. The generalized Lloyd algorithm and the entropy-constrained vector quantizer design algorithm are used to design fixed-rate and entropy-constrained codebooks. The entropy-constrained designs result in a low-complexity system with objective performance rivaling that of many coding schemes from the literature.

An entropy-constrained Trellis Coded Quantization (TCQ) scheme is presented for encoding memoryless sources. A simple 8-state trellis is used to encode the memoryless Gaussian source with mean-squared-error (MSE) performance within about 0.5 dB of the rate-distortion function. This performance is achieved at all non-negative encoding rates.

A three-dimensional (3-D) image-compression algorithm based on integer wavelet transforms and zero-tree coding is presented. The embedded coding of zerotrees of wavelet coefficients (EZW) algorithm is extended to three dimensions, and context-based adaptive arithmetic coding is used to improve its performance. The resultant algorithm, 3-D CB-EZW, efficiently encodes 3-D image data by the exploitation of the dependencies in all dimensions, while enabling lossy and lossless decompression from the same bit stream. Compared with the best available two-dimensional lossless compression techniques, the 3-D CB-EZW algorithm produced averages of 22%, 25%, and 20% decreases in compressed file sizes for computed tomography, magnetic resonance, and Airborne Visible Infrared Imaging Spectrometer images, respectively. The progressive performance of the algorithm is also compared with other lossy progressive-coding algorithms. [C] 2000 Optical Society of America OCIS codes: 100.6890, 100.7410.

A technique for identifying image blur using vector quantizer encoder distortion is presented. This technique needs a set of training images blurred by each of the candidate blur functions to design a corresponding vector quantizer codebook.