Most existing nonlinear regression filtering techniques for image denoising are claimed to be edge preserving without considering the pixel position information. This will cause speckling effects on the denoised image and inconsistent smoothing in the vicinity of texture-rich areas. This paper proposes a novel denoising method to address this problem. The proposed method removes the low to intermediate noise using edge-preserving range filtering, thereby removing short, false edges. The updated edge map is used for subsequent filtering in which pixel intensities are smoothed according to their minimum distance to the closest edge point. This procedure is carried out in an iterative scheme until the edge map stabilizes. We compare existing denoising algorithms with the proposed method. Experimental results validate the effectiveness and efficiency of the proposed method.

Automated tracking of targets within outdoor infrared (IR) video sequences poses a host of challenges. These include automatic gain adjustment in the IR camera, extreme granularity, large luminance changes, and uncontrolled environmental factors such as moving foliage, animals, and birds, among others. To address these problems, we present an IR video target tracking system for stationary cameras that learns and divides the video frames into reliable and unreliable regions. A difference-frame-based method can recognize moving regions with high sensitivity and reliably discern background clutter from target motion. A low-complexity target validation process is presented, which in conjunction with the reliable region masking, dramatically reduces the number of false alarms. We demonstrate the outstanding performance of the proposed system using real-world IR video sequences with difficult background motion clutter, as well as with small and blurred moving targets.

An interval recursive least-squares (RLS) filter is developed to produce state estimation and prediction by narrow intervals, in which true values are contained with high confidence. The interval filter is robust to variations of the filter parameters and state observations. Using this filter, a video target tracking algorithm is proposed to estimate the target position in each frame. The tracking algorithm is robust to both noise in the video sequence and estimation error of the affine model. The experiments show that the tracking algorithm using the interval RLS filter outperforms that using an RLS filter.

A no-reference perceptual sharpness quality metric, inspired by visual attention information, is presented for a better simulation of the Human Visual System (HVS) response to blur distortions. Saliency information about a scene is used to accentuate blur distortions around edges present in conspicuous areas and attenuate those distortions present in the rest of the image. Simulation results are presented to illustrate the performance of the proposed metric.

Orthogonal rotation-invariant moments (ORIMs), such as Zernike moments, are introduced and defined on a continuous unit disk and have been proven powerful tools in optics applications. These moments have also been digitized for applications in digital image processing. Unfortunately, digitization compromises the orthogonality of the moments and, therefore, digital ORIMs are incapable of representing subtle details in images and cannot accurately reconstruct images. Typical approaches to alleviate the digitization artifact can be divided into two categories: 1) careful selection of a set of pixels as close approximation to the unit disk and using numerical integration to determine the ORIM values, and 2) representing pixels using circular shapes such that they resemble that of the unit disk and then calculating ORIMs in polar space. These improvements still fall short of preserving the orthogonality of the ORIMs. In this paper, in contrast to the previous methods, we propose a different approach of using numerical optimization techniques to improve the orthogonality. We prove that with the improved orthogonality, image reconstruction becomes more accurate. Our simulation results also show that the optimized digital ORIMs can accurately reconstruct images and can represent subtle image details.

This paper presents a transform-domain distributed video compression (DVC) system with block-adaptive Wyner-Ziv coding. In the proposed system, the source symbols are reformatted into several blocks. The parity bits are requested only for the blocks with decoding errors. More accurate placement of the parity bits results in an improved system performance that is much closer to the performance of H.263 interframe coding as compared to existing DCT-based DVC schemes. Coding results and comparison with existing DVC schemes and with H.263 are presented to illustrate the performance of the proposed system.

The paper presents a distributed video compression (DVC) system with improved rate-distortion performance that is much closer to the performance of H.263 interframe coding as compared to existing DCT-based DVC schemes. The system performance, in terms of compression rate and video quality, is affected by the difference between the source information and the generated side information at the decoder. To improve the accuracy of the side information, a modified three-dimensional recursive search block matching algorithm is proposed. The performance of the proposed DVC system is also investigated when the side information is estimated from lossy video frames that are compressed at different bit rates. Coding results and comparison with existing DVC schemes and with H.263 are presented to illustrate the performance of the proposed system.

This paper presents a new method for image registration for real natural scenes. The method is based on the observation that most natural scenes are actually 3-D. If the assumption of weak perspective is violated, the error in image registration induced by parallax is increased, leading artifacts or blurs in image mosaic. Our method first applies the affine-invariant point detector in scale space. After clustering feature point pairs, an initial global transformation is formed based on majority correspondence. The global transformation is evaluated in each region at certain scale determined by inliers. The global model is optimized for local registration by minimizing least square error. This method is more robust than standard image registration algorithms on images subject to uncalibrated camera motion

This paper presents a selective error detection (SED) scheme that is based on a similarity check function designed for the JPEG2000 compression algorithm. The presented scheme takes advantage of the error-resilience properties of JPEG-2000 to significantly reduce the number of corrupted data packets that need to be retransmitted in a typical ARQ scheme. The degree of corruption induced by a particular codeblock is measured by a similarity check function at the receiver, without using any explicit knowledge regarding the original source data. Accordingly, if the data is found to be badly corrupted based on a specified similarity criteria, it is considered unusable and retransmitted. On the other hand, if the corrupted data does not significantly affect the quality of the decoded image, it is simply processed by the decoder without retransmission. Simulation results comparing the proposed selective retransmission scheme to full retransmission of corrupted packets are provided to illustrate the performance of the proposed method.

This paper presents a novel video compression system with a low complexity encoder. Based on the Slepian-Wolf theorem and the Wyner-Ziv theorem, a video is intraframe coded and interframe decoded in this system. The system performance, the compression rate and the video quality, is affected by the difference between the source information and the side information. To increase the accuracy of the side information, a modified three dimensional recursive search block matching is proposed. The resulting motion vectors preserve the true motion of objects because of the spatial and temporal consistency. The results obtained show a significant improvement over full search block matching algorithm in the term of the rate-distortion measure. The different quantizer design and puncturing table reduce the necessary parity bits for the decoding, and also increase the PSNR. The system performance is also much closer to H.263 interframe coding.

High compression ratios introduce visible compression artifacts and blurring. To enhance the quality of the compressed video, a super-resolution (SR) scheme is employed. However, the effectiveness of the SR process depends heavily on the amount of subpixel motion that exists between frames in a video sequence, and on the accuracy of the motion vectors (MV). To increase the effectiveness of the SR process, we introduce the concept of added motion prior to video compression. The parameters of this added motion are sent to the receiver with the compressed bit-stream. The proposed SR scheme also exploits existing motion between frames, by estimating MV for each pixel using the original (uncompressed) video. However, since transmitting the pixel-based MV is not practical due to the resulting significant increase in side information, the proposed scheme selects and sends only a very small set of MV based upon their significance in the super-resolution enhancement. The results obtained show a significant improvement in the visual quality of the video sequences, as well as improvement in PSNR.

The paper presents a reduced-delay selective-ARQ scheme based on a similarity check function that measures the degree of corruption in transmitted multimedia data packets. Accordingly, if the packet is found to be badly corrupted based on a specified similarity criterion, it can be considered as a lost packet and retransmitted. The degree of corruption contributed by a particular packet is measured by the proposed similarity check function at the receiver without explicit knowledge of the original source data. Simulation results and comparisons with a conventional ARQ scheme are provided to illustrate the performance of the proposed method.

Orthogonality and rotation invariance are important feature properties in digital signal processing. Orthogonality enables a target to be represented by a compact number of features, while rotation invariance results in unique features for a target with different orientations. The orthogonal, rotation-invariant moments (ORIMs), such as Zernike, pseudo-Zernike, and orthogonal Fourier-Melling moments, are defined in continuous space. These ORIMs have been digitized and have been demonstrated effectively for some digital imagery applications. However, digitization compromises the orthogonality of the moments, and hence, reduces their precision. Therefore, digital ORIMs are incapable of representing the fine details of images. In this paper, we propose a numerical optimization technique to improve the orthogonality of the digital ORIMs. Simulation results show that our optimized digital ORIMs can be used to reproduce subtle details of images.

In this paper, we introduce a new image segmentation scheme that is based on bidirectional labeling and registration and prove that its segmentation performance is equivalent to that of the conventional watershed segmentation algorithm. The proposed bidirectional labeling and registration scheme, which we refer to as bidirectional labeling and registration scheme (BIDS), involves only linear scans of image pixels. It uses one-dimensional operations rather than the queues that are used in traditional segmentation algorithms, which are two-dimensional problems. BIDS also provides unique labels for individual homogeneous regions. In addition to achieving the same segmentation results, BIDS is four times less computationally complex than the conventional watershed by immersion technique.

The shape information of objects is perceptually very significant and can aid in the recognition of objects. Depending upon the bit budget, Internet browsing applications can take advantage of an arbitrary-shape region-of-interest (ROI) coding method by sending only the shape information first, and then progressively transmitting the ROI texture, followed by the less important non-ROI texture information. Unfortunately, the existing ROI coding methods, including the JPEG2000-based methods, do not support separate coding and decoding of the shape information. In particular, the maxshift and the scaling-based methods of JPEG2000 are limited in that the former does not allow the coding of any non-ROI bitplanes prior to the coding of the ROI bitplanes, and the latter supports only rectangular and circular ROIs. The paper presents a novel ROI-based coding method that extends the scaling-based method of JPEG2000, and allows the coding of arbitrary-shape ROIs, as well as the separate coding and transmission of the ROI shape information prior to the texture information. Coding results are presented to illustrate the performance of the proposed ROI-based coding method.

This paper introduces the concept of similarity check functions that measure the degree of corruption in transmitted multimedia data packets. Rather than directly discarding or directly retransmitting erroneous packets, the degree of corruption contributed by a particular packet is measured by a similarity check function at the receiver, which does not require explicit knowledge of the source data. Accordingly, if the packet is found to be badly corrupted based on a specified similarity criteria, it can be considered as a lost packet and recovered using forward error correcting codes or retransmission. An example of a similarity check function design and a selective forward error correction (S-FEC) scheme that allocates channel protection bits only to those packets that are in most need of correction, is presented to illustrate the proposed concept.

The JPEG2000 standard supports two methods to code regions-of-interest (ROIs) in an image-the maxshift method and the scaling-based method. Compared to the maxshift method, the scaling-based method is preferable in many applications because it allows the nonROI (background) region to be partially coded prior to coding all of the ROIs. This is accomplished by supporting arbitrary bitplane shift factors and by filling the most significant bit-planes of the nonROI region with zeros after the ROI bitplanes have been shifted up in value. In both of these methods, the embedded block coding with optimum truncation (EBCOT) algorithm is then applied to code the wavelet coefficients. However, when multiple regions-of-interest need to be coded, the performance of the scaling-based method degrades significantly since the EBCOT coder is no longer able to efficiently code the nonROI region, which is now less contiguous as compared to the single-ROI case. Also, the large header information that is required in JPEG2000 for each ROI degrades the coding performance. This paper presents an improved scaling-based method for the efficient coding of multiple, arbitrarily-shaped ROIs using a modified EBCOT algorithm. The proposed method utilizes the shape information of the different ROIs to generate a stripe mask, which is then used to optimize the coding performance. Coding results and comparisons with the JPEG2000 scaling-based method are presented to illustrate the improved performance of the proposed scheme.

Watershed is one of the commonly used methods for image segmentation. In this paper, we introduce a new segmentation scheme based on bi-directional labeling and registration and prove that its segmentation performance is equivalent to that of conventional watershed but it is much more computational efficient The bi-directional labeling and registration scheme, which will be referred to as BIDS, involves only linear scans of image pixels. It uses one dimensional operations instead of queues while traditional segmentation algorithms are two dimensional problems. BIDS also provides unique labels for each homogeneous regions. In addition to achieving the same segmentation results as conventional watershed, BIDS is four times less computationally complex than the conventional watersheds by immersion.

In this paper, we present an optimal, low-complexity method of transmitting digitally compressed imagery through interference-dominated and frequency-selective fading channels. The proposed method combines a wavelet-based image coder that employs phase scrambling and variable quality-of-service (VQoS) trellis-coded quantization (TCQ), and VQoS multi carrier (MC) power allocation across the channel. Optimal image quality is achieved through a joint iterative process between the image coder and MC allocation algorithm. Using our VQoS allocation method, we show that the image quality can be improved significantly, as compared to an approach that uses an arbitrary fixed joint QoS-modulation allocation across the channel. Further, this improvement extends to a variety of channels, including those that exhibit jamming interference and frequency-selective fading. The method is also shown to be computationally efficient.

In this paper, we present a target-aided, error-resilient system for coding synthetic aperture radar (SAR) imagery, whereby regions of interest and background information are coded independently of each other. A multiresolution constant-false-alarm-rate (CFAR) detection scheme is utilized to discriminate between target regions and natural clutter. Based upon the detected target regions, we apply less compression to targets, and more compression to background data. This methodology preserves relevant features of targets for further analysis, and preserves the background only to the extent of providing contextual information. The resilience to channel errors is obtained without the use of channel coding or error concealment techniques. The robust nature of the coder eliminates the appearance of impulsive channel-error-induced artifacts in the decoded imagery, while increasing the security level of the encoded bit stream. The proposed coder is evaluated in terms of target detection performance on the decoded SAR image for a wide variety of channel conditions and encoding bit rates. The resulting system dramatically reduces the bandwidth requirements of the digital SAR imagery, and is shown to provide outstanding performance in adverse channel conditions.

Detecting regions of interest (ROIs) in a complex image is a critical step in many image processing applications. In this paper, we present a new algorithm that addresses several challenges in ROI detection. The novelty of our algorithm includes: (i) every ROI contains one and only one object; (ii) the detected ROIs can have irregular shapes as opposed to the rectangular shapes that are typical of other algorithms; (iii) the algorithm is applicable to images that contain connected objects, or when the objects are broken into pieces; (iv) the algorithm is not sensitive to contrast levels in the image, and is robust to noise. These characteristics make the proposed algorithm applicable to low-resolution, real-world imagery without costly post-processing. The proposed algorithm is shown to provide outstanding performance with low-quality imagery, and is shown to be fast and robust.

We present a target-aided, fixed-quality-of-service (FQoS) system for coding synthetic aperture radar (SAR) imagery, whereby regions of interest and background information are coded independently of each other. A multiresolution, constant-false-alarm-rate (CFAR) detection scheme is used to discriminate between target regions and natural clutter. Based on the detected target regions, we apply less compression to targets, and more compression to background data. This methodology preserves the relevant features of targets for further analysis, and preserves the background only to the extent of providing contextual information. The background data is coded using channel-optimized trellis-coded quantization (COTCQ), while the target regions are coded using a combination of COTCQ and channel coding, depending upon the channel state. The combination of COTCQ and channel coding provides fixed target detection performance at the decoder, independent of the channel bit error probability. Additionally, all-pass filtering is employed, which significantly increases the detection performance at low encoding bit rates, while increasing the security level of the encoded bit stream. The proposed coder is evaluated in terms of target detection performance on the decoded SAR image for a wide variety of channel conditions and encoding bit rates. The resulting system dramatically reduces the bandwidth requirements of digital SAR imagery, and is shown to provide outstanding performance in adverse channel conditions.

This paper presents a wavelet-based hyperspectral image coder that is optimized for transmission over the binary symmetric channel (BSC). The proposed coder uses a robust channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. This optimization is performed only at the level of the source encoder and does not include any channel coding for error protection. The robust nature of the coder increases the security level of the encoded bit stream, and provides a much higher quality decoded image. In the absence of channel noise, the proposed coder is shown to achieve a compression ratio greater than 70:1, with an average peak SNR of the coded hyperspectral sequence exceeding 40 dB. Additionally, the coder is shown to exhibit graceful degradation with increasing channel errors.

We present a system for coding synthetic aperture radar (SAR) imagery, whereby regions of interest and background information are coded independently of each other. A multiresolution constant-false-alarm-rate (CFAR) detection scheme is utilized to discriminate between target regions and natural clutter. Based upon the detected target regions, we apply less compression to targets, and more compression to background data. This methodology preserves relevant features of targets for further analysis, and preserves the background only to the extent of providing contextual information. The resulting system dramatically reduces the bandwidth/storage requirements of the digital SAR imagery, while preserving the target-specific utility of the imagery.

This paper presents a multiple description image coding scheme that facilitates the transmission of digital imagery over multiple noisy channels. The proposed scheme divides the image into smaller parts that are transmitted over the individual channels of an inverse multiplexing system. The division or splitting is done in such a fashion that it facilitates the interpolation of lost coefficients in the case of one or more channel failures. At the receiver, the image is reconstructed by proper assembly of the data received from each channel. In case of channel failure, the missing coefficients are estimated from the available data with the use of a novel post-processing scheme. For operation over four noisy channels with various bit error probabilities, we investigate the quantitative and subjective performance of the proposed system for the case of multiple channel failures.

This paper presents a multiple description image coding scheme that facilitates the transmission of digital imagery over multiple noisy channels. The proposed scheme divides the image into smaller parts that are transmitted over the individual channels of an inverse multiplexing system. The division or splitting is done in such a fashion that it facilitates the interpolation of lost coefficients in the case of one or more channel failures. To combat the effects of channel noise, a novel joint source-channel coding scheme is employed, which uses punctured convolutional channel codes and trellis-coded quantization. At the receiver, the image is reconstructed by proper assembly of the data received from each channel. In case of channel failure, the missing coefficients are estimated from the available data with the use of a novel post processing scheme. For operation over four noisy channels with various bit error probabilities, we investigate the quantitative and subjective performance with and without channel failures.

This paper presents a low-complexity method of transmitting digitally compressed imagery through AWGN and fading channels. The proposed method combines a wavelet-based image coder that employs phase scrambling and trellis-coded quantization (TCQ), and orthogonal source adaptive modulation (OSAM), which is implemented using time diversity and orthogonal modulation. We compare the performance of the OSAM-TCQ system to that of a system that utilizes unequal error protection (UEP). We show that for the AWGN channel, the OSAM-TCQ system performs nearly 4 dB better than UEP-TCQ at high bit error rates (BERs), and the same as the UEP-TCQ system at low bit error rates, with much lower complexity than the UEP-based system.

This paper presents the design of multiple description trellis-coded quantizers that are optimized for transmitting data over multiple noisy channels. A multiple description channel-optimized trellis-coded quantization (MDCOTCQ) scheme is developed whereby all trellis-coded quantizers (side and central) are designed to operate over the binary symmetric channel with a given bit error rate (BER). Using the tensor product of trellises, an expression of the multi-channel transition probability matrix is derived. This formulation enables the construction of multiple description channel-optimized trellis-coded quantizers for arbitrary bit rates and arbitrary channel bit error rates. The proposed MDCOTCQ system enables the transmission of compressed data over noisy diversity or packet-based communication systems, and enables outstanding robustness to channel or packet loss. Examples are presented to illustrate the performance of the proposed MDCOTCQ-based source coder.

We present an error-resilient channel-optimized coder for the transmission of video over noisy channels. The proposed coder uses a robust channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the video coding based on the channel characteristics. The resilience to channel errors is obtained only at the level of the source encoder, with no explicit use of channel coding. The robust nature of the coder increases the security level of the encoded bit stream, and eliminates impulsive artifacts induced by channel errors. A novel adaptive classification scheme is employed, which eliminates the need for entropy coding and motion compensation. Consequently, the proposed channel-optimized video coder is especially suitable for wireless video transmission due to its reduced complexity, its robustness to nonstationary signals and channels, and its increased security level. Simulation results are presented to illustrate the performance of the proposed video coder for a wide variety of channel conditions, without the use of error concealment techniques.

This paper presents a wavelet-based image coder that is optimized for transmission over the binary symmetric channel (BSC). The proposed coder uses a robust channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. A phase scrambling stage is also used to further increase the coding performance and robustness to nonstationary signals and channels. The resilience to channel errors is obtained by optimizing the coder performance only at the level of the source encoder with no explicit channel coding for error protection. For the considered TCQ trellis structure, a general expression is derived for the transition probability matrix. In terms of the TCQ encoding rat and the channel bit error rate, and is used to design the COTCQ stage of the image coder. The robust nature of the coder also increases the security level of the encoded bit stream and provides a much more visually pleasing rendition of the decoded image. Examples are presented to illustrate the performance of the proposed robust image coder.

Two systems are presented for compression of hyperspectral imagery. These systems utilize adaptive classification, trellis-coded quantization, and optimal rate allocation. In the first system, DPCM is used for spectral decorrelation, while an adaptive wavelet-based coding scheme is used for spatial decorrelation. The second system uses DPCM in conjunction with an adaptive DCT-based coding scheme. In each system, entropy-constrained trellis-coded quantization (ECTCQ) is used to quantize the transform coefficients. Entropy-constrained codebooks are designed for generalized Gaussian distributions by using a modified version of the generalized Lloyd algorithm. The wavelet-based system compresses an AVIRIS hyperspectral test sequence at 0.118 bits/pixel/band, while retaining an average peak signal-to-noise ratio (PSNR) of 41.24 dB. The DCT-based system achieves the same bit rate with an average PSNR of 40.72 dB.

This paper presents a wavelet-based image coder optimized for transmission over binary symmetric channels (BSC). The proposed coder uses a channel-optimized trellis-coded quantization (COTCQ) stage that is designed to optimize the image coding based on the channel characteristics. This optimization is performed only at the level of the source encoder, and does not include any channel coding for error protection. Consequently, the proposed channel-optimized image coder is especially suitable for wireless transmission due to its reduced complexity. Furthermore, the improvement over TCQ-based image coders is significant. Examples are presented to illustrate the performance of the proposed COTCQ-based image coder.

The paper presents a robust, low-complexity method of transmitting digitally compressed imagery through very noisy channels. The method is applicable to AWGN and fading channels. The proposed method uses a robust wavelet based image coder employing trellis-coded quantization (TCQ), and optimal source adaptive modulation (SAM) which is implemented through time-frequency diversity. We compare the performance of the SAM-TCQ system to that of a system that utilizes unequal error protection (UEP). We show that for the binary symmetric channel (BSC), the SAM-TCQ system performs as well as UEP-TCQ at high bit error rates (BER), and within 1 dB of the UEP-TCQ system at low bit error rates.

A system is presented for the compression of digital imagery over binary symmetric channels (BSC). The objective is to minimize the perceived distortion for a desired target bit rate. The proposed system utilizes wavelet decomposition, channel-optimized trellis-coded quantization (COTCQ), and a perceptually-tuned rate allocation scheme to control the quantization. Because of the inherent noise-robust properties of the COTCQ stage, no channel coding is employed. Additionally, the COTCQ design does not utilize entropy coding. Consequently, the proposed image coder is especially suitable for wireless transmission due to its reduced complexity and robustness to channel errors. Examples are presented to illustrate the performance of the proposed image coding system.

Summary form only given. We propose two methods to provide optimal image quality at a fixed image delivery rate for any given transmission channel condition. The first method, channel-controlled variable-rate (CCVR) image coding, employs adaptive-rate source coding and channel coding, while operating with a fixed modulation symbol rate. The second method, adaptive-rate coding-modulation (ARCM), extends the CCVR system by utilizing adaptive modulation. Both methods use a variable-compression-ratio image coder and variable-rate channel coding. The objective is to maximize the quality of the reconstructed image at the receiver when transmitted through Rayleigh fading and additive white Gaussian noise (AWGN). The CCVR system maximizes the reconstructed image quality through a bit-rate trade-off between the source and channel coders. The ARCM method provides a trade-off between the rates of source and channel coding, and the modulation rate. Both methods require knowledge of the channel state which is used by the receiver to inform the transmitter, via a feedback channel, of the optimal strategy for image compression, channel coding, and modulation format. The resulting systems achieve up to a 17 dB improvement over the peak signal-to-noise ratio (PSNR) performance of a system using a fixed-compression-ratio image coder and fixed-rate channel coding. Reconstructed image quality is evaluated through both quantitative and subjective measures using peak signal-to-noise ratio and visual analysis, respectively.

The vast amount of data needed to represent digital imagery motivates the use of advanced compression systems to reduce the bandwidth required to transmit high-resolution source imagery. We propose two methods to provide optimal image quality at a fixed image delivery rate. The first method, channel-controlled variable-rate (CCVR) image coding, operates within the constraint that the modulation symbol rate is fixed. The second method, adaptive-rate coding-modulation (ARCM), utilizes adaptive modulation, and is less complex, while providing increased performance. Both methods use a variable-compression-ratio image coder and variable-rate channel coding. The objective is to maximize the quality of the reconstructed image at the receiver when transmitted through Rayleigh fading and additive white Gaussian noise (AWGN). The reconstructed image quality is maximized through adaptive source-channel coding in the context of a bit rate tradeoff between the source and channel coders (CCVR), and then, in addition, adaptive-rate modulation (ARCM). Both methods require knowledge of the channel state which is used by the receiver to inform the transmitter, via a feedback channel, of the optimal strategy for image compression, channel coding, and modulation format. The resulting system using ARCM achieves up to a 17 dB improvement over the peak signal-to-noise ratio (PSNR) performance of a system using a fixed-compression-ratio image coder and fixed-rate channel coding.

A training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme is presented for encoding autoregressive sources. For encoding a first-order Gauss-Markov source, the mean squared error (MSE) performance of an eight-state ECPTCQ system exceeds that of entropy-constrained differential pulse code modulation (ECDPCM) by up to 1.0 dB. In addition, a hyperspectral image compression system is developed, which utilizes ECPTCQ. A hyperspectral image sequence compressed at 0.125 b/pixel/band retains an average peak signal-to-noise ratio (PSNR) of greater than 43 dB over the spectral bands.

A system is presented for compression of hyperspectral imagery which utilizes trellis coded quantization (TCQ). Specifically, DPCM is used to spectrally decorrelate the hyperspectral data, while a 2-D discrete cosine transform (DCT) coding scheme is used for spatial decorrelation. Entropy-constrained codebooks are designed using a modified version of the generalized Lloyd algorithm. This coder achieves compression ratios of greater than 70:1 with average PSNR of the coded hyperspectral sequence exceeding 40.0 dB.

Two systems are presented for compression of hyperspectral imagery which utilize trellis coded quantization (TCQ). Specifically, the first system uses TCQ to encode transform coefficients resulting from the application of an 8/spl times/8/spl times/8 discrete cosine transform (DCT). The second systems uses DPCM to spectrally decorrelate the data, while a 2D 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 PSNRs of the coded hyperspectral sequences exceeding 40.0 dB.>

A training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme is presented for encoding autoregressive sources. For encoding a first-order Gauss-Markov source, the MSE performance of an 8-state ECPTCQ system exceeds that of entropy-constrained DPCM by up to 1.0 dB. In addition, a hyperspectral image compression system is developed which utilizes ECPTCQ. A hyperspectral image sequence compressed at 0.15 bits/pixel/band retains peak signal-to-noise ratios greater than 42 dB over most spectral bands.>

The use of a complex MUSIC (multiple signal classification) algorithm to signal average MR spectroscopic data from a 1-cm/sup 3/ voxel of diseased brain tissue for only five min and obtain diagnostically useful studies is discussed. A complex eigenvector-based method for performing spectral analysis of time-domain data independent of the signal-to-noise ratio is demonstrated. The implementation of the procedure requires no preprocessing of the time-domain data record. The technique is well suited for magnetic resonance spectroscopy and imaging, where the signal available from small regions corresponding to areas of diseased tissue in patients presenting for diagnosis is always dominated by the Johnson noise present in the receiver circuit.>

The application of a variety of parametric modeling techniques to short complex nuclear magnetic resonance (NMR) data sequences is demonstrated. These techniques have the potential of identifying frequency clusters of signals without being compromised by truncation artifacts. These adaptive algorithms are as rapid as the fast Fourier transform (FFT), and are often a practical alternative to the FFT for generating magnetic resonance images from time-domain data sequences with only 16 complex points. There are two distinct methods of processing nonstationary NMR data, i.e. block and recursive processing. Least-mean-square and modified-least-mean-square algorithms are examples of recursive adaptive procedures, while the Yule-Walker and Burg methods are examples of block processing. Application of the adaptive algorithms yields results where the inherent information content of short time-domain data records are accurately represented. The resolution of these representations is comparable to a DFT analysis with twice the number of samples. This increase in resolution and the accuracy of the signal can be obtained without any increase in acquisition or processing time. Hence, the techniques is well suited for clinical applications on NMR instruments.>

The utility of adaptive prediction and filtering algorithms and the autocorrelation-based Yule-Walker algorithm to predict and filter complex NMR (nuclear magnetic resonance) data is demonstrated. The application of these methods improves the available signal-to-noise ratio using time-domain analysis, and increases the low resolution via prediction algorithms in data containing phase errors introduced by hardware limitations. The application of the complex least-mean-squares and the modified-least-mean-squares transversal and lattice algorithms to low- and high-resolution NMR data records is demonstrated. The resolution and windowing problems found in the discrete Fourier transform are overcome by these alternative methods.>