Showing papers in "IEEE Transactions on Image Processing in 1994"
TL;DR: A system for representing moving images with sets of overlapping layers that is more flexible than standard image transforms and can capture many important properties of natural image sequences.
Abstract: We describe a system for representing moving images with sets of overlapping layers. Each layer contains an intensity map that defines the additive values of each pixel, along with an alpha map that serves as a mask indicating the transparency. The layers are ordered in depth and they occlude each other in accord with the rules of compositing. Velocity maps define how the layers are to be warped over time. The layered representation is more flexible than standard image transforms and can capture many important properties of natural image sequences. We describe some methods for decomposing image sequences into layers using motion analysis, and we discuss how the representation may be used for image coding and other applications. >
1,360 citations
TL;DR: A spatially selective noise filtration technique based on the direct spatial correlation of the wavelet transform at several adjacent scales is introduced and can reduce noise contents in signals and images by more than 80% while maintaining at least 80% of the value of the gradient at most edges.
Abstract: Wavelet transforms are multiresolution decompositions that can be used to analyze signals and images They describe a signal by the power at each scale and position Edges can be located very effectively in the wavelet transform domain A spatially selective noise filtration technique based on the direct spatial correlation of the wavelet transform at several adjacent scales is introduced A high correlation is used to infer that there is a significant feature at the position that should be passed through the filter The authors have tested the technique on simulated signals, phantom images, and real MR images It is found that the technique can reduce noise contents in signals and images by more than 80% while maintaining at least 80% of the value of the gradient at most edges The authors did not observe any Gibbs' ringing or significant resolution loss on the filtered images Artifacts that arose from the filtration are very small and local The noise filtration technique is quite robust There are many possible extensions of the technique The authors see its applications in spatially dependent noise filtration, edge detection and enhancement, image restoration, and motion artifact removal They have compared the performance of the technique to that of the Weiner filter and found it to be superior >
793 citations
TL;DR: A full color video compression strategy, based on 3-D subband coding with camera pan compensation, to generate a single embedded bit stream supporting multiple decoder display formats and a wide, finely gradated range of bit rates is proposed.
Abstract: We propose a full color video compression strategy, based on 3-D subband coding with camera pan compensation, to generate a single embedded bit stream supporting multiple decoder display formats and a wide, finely gradated range of bit rates. An experimental implementation of our algorithm produces a single bit stream, from which suitable subsets are extracted to be compatible with many decoder frame sizes and frame rates and to satisfy transmission bandwidth constraints ranging from several tens of kilobits per second to several megabits per second. Reconstructed video quality from any of these bit stream subsets is often found to exceed that obtained from an MPEG-1 implementation, operated with equivalent bit rate constraints, in both perceptual quality and mean squared error. In addition, when restricted to 2-D, the algorithm produces some of the best results available in still image compression. >
688 citations
TL;DR: Simulations on synthetic images indicate that the new algorithm performs better and requires much less computation than MAP estimation using simulated annealing, and is found to improve classification accuracy when applied to the segmentation of multispectral remotely sensed images with ground truth data.
Abstract: Many approaches to Bayesian image segmentation have used maximum a posteriori (MAP) estimation in conjunction with Markov random fields (MRF). Although this approach performs well, it has a number of disadvantages. In particular, exact MAP estimates cannot be computed, approximate MAP estimates are computationally expensive to compute, and unsupervised parameter estimation of the MRF is difficult. The authors propose a new approach to Bayesian image segmentation that directly addresses these problems. The new method replaces the MRF model with a novel multiscale random field (MSRF) and replaces the MAP estimator with a sequential MAP (SMAP) estimator derived from a novel estimation criteria. Together, the proposed estimator and model result in a segmentation algorithm that is not iterative and can be computed in time proportional to MN where M is the number of classes and N is the number of pixels. The also develop a computationally efficient method for unsupervised estimation of model parameters. Simulations on synthetic images indicate that the new algorithm performs better and requires much less computation than MAP estimation using simulated annealing. The algorithm is also found to improve classification accuracy when applied to the segmentation of multispectral remotely sensed images with ground truth data. >
687 citations
TL;DR: The scheme is interpreted more generally, viewed as a motion-compensated short-time spectral analysis of video sequences, which can adapt to the quickness of changes.
Abstract: Three-dimensional (3-D) frequency coding is an alternative approach to hybrid coding concepts used in today's standards. The first part of this paper presents a study on concepts for temporal-axis frequency decomposition along the motion trajectory in video sequences. It is shown that, if a two-band split is used, it is possible to overcome the problem of spatial inhomogeneity in the motion vector field (MVF), which occurs at the positions of uncovered and covered areas. In these cases, original pixel values from one frame are placed into the lowpass-band signal, while displaced-frame-difference values are embedded into the highpass band. This technique is applicable with arbitrary MVF's; examples with block-matching and interpolative motion compensation are given. Derivations are first performed for the example of two-tap quadrature mirror filters (QMF's), and then generalized to any linear-phase QMF's. With two-band analysis and synthesis stages arranged as cascade structures, higher resolution frequency decompositions are realizable. In the second part of the paper, encoding of the temporal-axis subband signals is discussed. A parallel filterbank scheme was used for spatial subband decomposition, and adaptive lattice vector quantization was employed to approach the entropy rate of the 3-D subband samples. Coding results suggest that high-motion video sequences can be encoded at significantly lower rates than those achievable with conventional hybrid coders. Main advantages are the high energy compaction capability and the nonrecursive decoder structure. In the conclusion, the scheme is interpreted more generally, viewed as a motion-compensated short-time spectral analysis of video sequences, which can adapt to the quickness of changes. Although a 3-D multiresolution representation of the picture information is produced, a true multiresolution representation of motion information, based on spatio-temporal decimation and interpolation of the MVF, is regarded as the still-missing part. >
625 citations
TL;DR: A method for nonlinear image expansion which preserves the discontinuities of the original image, producing an expanded image with improved definition is introduced.
Abstract: Accurate image expansion is important in many areas of image analysis. Common methods of expansion, such as linear and spline techniques, tend to smooth the image data at edge regions. This paper introduces a method for nonlinear image expansion which preserves the discontinuities of the original image, producing an expanded image with improved definition. The maximum a posteriori (MAP) estimation techniques that are proposed for noise-free and noisy images result in the optimization of convex functionals. The expanded images produced from these methods will be shown to be aesthetically and quantitatively superior to images expanded by the standard methods of replication, linear interpolation, and cubic B-spline expansion. >
580 citations
TL;DR: It is shown how a certain monotonicity property of the dependent R-D curves can be exploited in formulating fast ways to obtain optimal and near-optimal solutions and how to obtain fast solutions that provide nearly optimal full resolution quality while providing much better performance for the subresolution layer.
Abstract: We address the problem of efficient bit allocation in a dependent coding environment. While optimal bit allocation for independently coded signal blocks has been studied in the literature, we extend these techniques to the more general temporally and spatially dependent coding scenarios. Of particular interest are the topical MPEG video coder and multiresolution coders. Our approach uses an operational rate-distortion (R-D) framework for arbitrary quantizer sets. We show how a certain monotonicity property of the dependent R-D curves can be exploited in formulating fast ways to obtain optimal and near-optimal solutions. We illustrate the application of this property in specifying intelligent pruning conditions to eliminate suboptimal operating points for the MPEG allocation problem, for which we also point out fast nearly-optimal heuristics. Additionally, we formulate an efficient allocation strategy for multiresolution coders, using the spatial pyramid coder as an example. We then extend this analysis to a spatio-temporal 3-D pyramidal coding scheme. We tackle the compatibility problem of optimizing full-resolution quality while simultaneously catering to subresolution bit rate or quality constraints. We show how to obtain fast solutions that provide nearly optimal (typically within 0.3 dB) full resolution quality while providing much better performance for the subresolution layer (typically 2-3 dB better than the full-resolution optimal solution). >
492 citations
TL;DR: This analysis establishes for the first time how (and why) OBMC can offer substantial reductions in prediction error as well, even with no change in the encoder's search and no extra side information.
Abstract: We present an estimation-theoretic analysis of motion compensation that, when used with fields of block-based motion vectors, leads to the development of overlapped block algorithms with improved compensation accuracy. Overlapped block motion compensation (OBMC) is formulated as a probabilistic linear estimator of pixel intensities given the limited block motion information available to the decoder. Although overlapped techniques have been observed to reduce blocking artifacts in video coding, this analysis establishes for the first time how (and why) OBMC can offer substantial reductions in prediction error as well, even with no change in the encoder's search and no extra side information. Performance can be further enhanced with the use of state variable conditioning in the compensation process. We describe the design of optimized windows for OBMC. We also demonstrate how, with additional encoder complexity, a motion estimation algorithm optimized for OBMC offers further significant gains in compensation accuracy. Overall mean-square prediction improvements in the range of 16 to 40% (0.8 to 2.2 dB) are demonstrated. >
473 citations
TL;DR: The authors describe the theory needed to construct quadtree data structures that optimally allocate rate, given a set of quantizers, and derive a new quadtree construction method that uses a stepwise search to find the overall optimal quadtree structure.
Abstract: The quadtree data structure is commonly used in image coding to decompose an image into separate spatial regions to adaptively identify the type of quantizer used in various regions of an image. The authors describe the theory needed to construct quadtree data structures that optimally allocate rate, given a set of quantizers. A Lagrange multiplier method finds these optimal rate allocations with no monotonicity restrictions. They use the theory to derive a new quadtree construction method that uses a stepwise search to find the overall optimal quadtree structure. The search can be driven with either actual measured quantizer performance or ensemble average predicted performance. They apply this theory to the design of a motion compensated interframe video coding system using a quadtree with vector quantization. >
349 citations
TL;DR: To decide if two regions should be merged, instead of comparing the difference of region feature means with a predefined threshold, the authors adaptively assess region homogeneity from region feature distributions, resulting in an algorithm that is robust with respect to various image characteristics.
Abstract: Proposes a simple, yet general and powerful, region-growing framework for image segmentation. The region-growing process is guided by regional feature analysis; no parameter tuning or a priori knowledge about the image is required. To decide if two regions should be merged, instead of comparing the difference of region feature means with a predefined threshold, the authors adaptively assess region homogeneity from region feature distributions. This results in an algorithm that is robust with respect to various image characteristics. The merge criterion also minimizes the number of merge rejections and results in a fast region-growing process that is amenable to parallelization. >
321 citations
TL;DR: A heuristic algorithm based on Lagrangian optimization using an operational rate-distortion framework that, with computing complexity reduced by an order of magnitude, approaches the optimally achievable performance.
Abstract: The authors formalize the description of the buffer-constrained adaptive quantization problem. For a given set of admissible quantizers used to code a discrete nonstationary signal sequence in a buffer-constrained environment, they formulate the optimal solution. They also develop slightly suboptimal but much faster approximations. These solutions are valid for any globally minimum distortion criterion, which is additive over the individual elements of the sequence. As a first step, they define the problem as one of constrained, discrete optimization and establish its equivalence to some of the problems studied in the field of integer programming. Forward dynamic programming using the Viterbi algorithm is shown to provide a way of computing the optimal solution. Then, they provide a heuristic algorithm based on Lagrangian optimization using an operational rate-distortion framework that, with computing complexity reduced by an order of magnitude, approaches the optimally achievable performance. The algorithms can serve as a benchmark for assessing the performance of buffer control strategies and are useful for applications such as multimedia workstation displays, video encoding for CD-ROMs, and buffered JPEG coding environments, where processing delay is not a concern but decoding buffer size has to be minimized. >
TL;DR: This paper presents a general overview of Khoros with emphasis on its image processing and DSP tools.
Abstract: Data flow visual language systems allow users to graphically create a block diagram of their applications and interactively control input, output, and system variables. Khoros is an integrated software development environment for information processing and visualization. It is particularly attractive for image processing because of its rich collection of tools for image and digital signal processing. This paper presents a general overview of Khoros with emphasis on its image processing and DSP tools. Various examples are presented and the future direction of Khoros is discussed. >
TL;DR: A hierarchical morphological segmentation algorithm for image sequence coding that directly segments 3-D regions and concentrates on the coding residue, all the information about the 3- D regions that have not been properly segmented and therefore coded.
Abstract: This paper deals with a hierarchical morphological segmentation algorithm for image sequence coding. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features such as size, shape, contrast, or connectivity that can be considered as segmentation-oriented features. The algorithm follows a top-down procedure. It first takes into account the global information and produces a coarse segmentation, that is, with a small number of regions. Then, the segmentation quality is improved by introducing regions corresponding to more local information. The algorithm, considering sequences as being functions on a 3-D space, directly segments 3-D regions. A 3-D approach is used to get a segmentation that is stable in time and to directly solve the region correspondence problem. Each segmentation stage relies on four basic steps: simplification, marker extraction, decision, and quality estimation. The simplification removes information from the sequence to make it easier to segment. Morphological filters based on partial reconstruction are proven to be very efficient for this purpose, especially in the case of sequences. The marker extraction identifies the presence of homogeneous 3-D regions. It is based on constrained flat region labeling and morphological contrast extraction. The goal of the decision is to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a modified watershed algorithm. Finally, the quality estimation concentrates on the coding residue, all the information about the 3-D regions that have not been properly segmented and therefore coded. The procedure allows the introduction of the texture and contour coding schemes within the segmentation algorithm. The coding residue is transmitted to the next segmentation stage to improve the segmentation and coding quality. Finally, segmentation and coding examples are presented to show the validity and interest of the coding approach. >
TL;DR: The proposed representation retains the salient merit of the original model as a feature tracker based on local and collective information, while facilitating more accurate image interpolation and prediction, and can successfully track facial feature movements in head-and-shoulder type of sequences.
Abstract: This paper introduces a representation scheme for image sequences using nonuniform samples embedded in a deformable mesh structure. It describes a sequence by nodal positions and colors in a starting frame, followed by nodal displacements in the following frames. The nodal points in the mesh are more densely distributed in regions containing interesting features such as edges and corners; and are dynamically updated to follow the same features in successive frames. They are determined automatically by maximizing feature (e.g., gradient) magnitudes at nodal points, while minimizing interpolation errors within individual elements, and matching errors between corresponding elements. In order to avoid the mesh elements becoming overly deformed, a penalty term is also incorporated, which measures the irregularity of the mesh structure. The notions of shape functions and master elements commonly used in the finite element method have been applied to simplify the numerical calculation of the energy functions and their gradients. The proposed representation is motivated by the active contour or snake model proposed by Kass, Witkin, and Terzopoulos (1988). The current representation retains the salient merit of the original model as a feature tracker based on local and collective information, while facilitating more accurate image interpolation and prediction. Our computer simulations have shown that the proposed scheme can successfully track facial feature movements in head-and-shoulder type of sequences, and more generally, interframe changes that can be modeled as elastic deformation. The treatment for the starting frame also constitutes an efficient representation of arbitrary still images. >
TL;DR: A class of nonlinear filters called rank conditioned rank selection (RCRS) filters is developed and analyzed and extensive computer simulation results that illustrate the performance of RCRS filters in comparison with other techniques in image restoration applications are presented.
Abstract: A class of nonlinear filters called rank conditioned rank selection (RCRS) filters is developed and analyzed in this paper. The RCRS filters are developed within the general framework of rank selection (RS) filters, which are filters constrained to output an order statistic from the observation set. Many previously proposed rank order based filters can be formulated as RS filters. The only difference between such filters is in the information used in deciding which order statistic to output. The information used by RCRS filters is the ranks of selected input samples, hence the name rank conditioned rank selection filters. The number of input sample ranks used is referred to as the order of the RCRS filter. The order can range from zero to the number of samples in the observation window, giving the filters valuable flexibility. Low-order filters can give good performance and are relatively simple to optimize and implement. If improved performance is demanded, the order can be increased but at the expense of filter simplicity. In this paper, many statistical and deterministic properties of the RCRS filters are presented. A procedure for optimizing over the class of RCRS filters is also presented. Finally, extensive computer simulation results that illustrate the performance of RCRS filters in comparison with other techniques in image restoration applications are presented. >
TL;DR: Two solutions are proposed to solve the problem of model parameter estimation from incomplete data: a Monte Carlo scheme and a scheme related to Besag's (1986) iterated conditional mode (ICM) method, both of which make use of Markov random-field modeling assumptions.
Abstract: An unsupervised stochastic model-based approach to image segmentation is described, and some of its properties investigated. In this approach, the problem of model parameter estimation is formulated as a problem of parameter estimation from incomplete data, and the expectation-maximization (EM) algorithm is used to determine a maximum-likelihood (ML) estimate. Previously, the use of the EM algorithm in this application has encountered difficulties since an analytical expression for the conditional expectations required in the EM procedure is generally unavailable, except for the simplest models. In this paper, two solutions are proposed to solve this problem: a Monte Carlo scheme and a scheme related to Besag's (1986) iterated conditional mode (ICM) method. Both schemes make use of Markov random-field modeling assumptions. Examples are provided to illustrate the implementation of the EM algorithm for several general classes of image models. Experimental results on both synthetic and real images are provided. >
TL;DR: A rate-distortion optimal way to threshold or drop the DCT coefficients of the JPEG and MPEG compression standards using a fast dynamic programming recursive structure.
Abstract: We show a rate-distortion optimal way to threshold or drop the DCT coefficients of the JPEG and MPEG compression standards. Our optimal algorithm uses a fast dynamic programming recursive structure. The primary advantage of our approach lies in its complete compatibility with standard JPEG and MPEG decoders. >
TL;DR: A simple way to get better compression performances (in MSE sense) via quadtree decomposition, by using near to optimal choice of the threshold for quad tree decomposition; and bit allocation procedure based on the equations derived from rate-distortion theory.
Abstract: Quadtree decomposition is a simple technique used to obtain an image representation at different resolution levels. This representation can be useful for a variety of image processing and image compression algorithms. This paper presents a simple way to get better compression performances (in MSE sense) via quadtree decomposition, by using near to optimal choice of the threshold for quadtree decomposition; and bit allocation procedure based on the equations derived from rate-distortion theory. The rate-distortion performance of the improved algorithm is calculated for some Gaussian field, and it is examined vie simulation over benchmark gray-level images. In both these cases, significant improvement in the compression performances is shown. >
TL;DR: The new algorithm provides an excellent initialization for the iterative algorithms associated with the smoothness constraint problem formulation and should extend to a wide variety of ill-posed inverse problems in which variational techniques seeking a "smooth" solution are generally used.
Abstract: A new approach to regularization methods for image processing is introduced and developed using as a vehicle the problem of computing dense optical flow fields in an image sequence. The solution of the new problem formulation is computed with an efficient multiscale algorithm. Experiments on several image sequences demonstrate the substantial computational savings that can be achieved due to the fact that the algorithm is noniterative and in fact has a per pixel computational complexity that is independent of image size. The new approach also has a number of other important advantages. Specifically, multiresolution flow field estimates are available, allowing great flexibility in dealing with the tradeoff between resolution and accuracy. Multiscale error covariance information is also available, which is of considerable use in assessing the accuracy of the estimates. In particular, these error statistics can be used as the basis for a rational procedure for determining the spatially-varying optimal reconstruction resolution. Furthermore, if there are compelling reasons to insist upon a standard smoothness constraint, the new algorithm provides an excellent initialization for the iterative algorithms associated with the smoothness constraint problem formulation. Finally, the usefulness of the approach should extend to a wide variety of ill-posed inverse problems in which variational techniques seeking a "smooth" solution are generally used. >
TL;DR: This work introduces a new image coding scheme using lattice vector quantization of wavelet coefficients, and investigates the case of Laplacian sources where surfaces of equal probability are spheres for the L(1) metric (pyramids) for arbitrary lattices.
Abstract: Introduces a new image coding scheme using lattice vector quantization. The proposed method involves two steps: biorthogonal wavelet transform of the image, and lattice vector quantization of wavelet coefficients. In order to obtain a compromise between minimum distortion and bit rate, we must truncate and scale the lattice suitably. To meet this goal, we need to know how many lattice points lie within the truncated area. We investigate the case of Laplacian sources where surfaces of equal probability are spheres for the L/sup 1/ metric (pyramids) for arbitrary lattices. We give explicit generating functions for the codebook sizes for the most useful lattices like Z/sup n/, D/sub n/, E/sub s/, /spl and//sub 16/. >
TL;DR: This paper presents a review of promising techniques for very low bit-rate, below 64 kb/s, image sequence coding, which will be a crucial technique in forthcoming visual services, e.g., visual information transmission and storage.
Abstract: This paper presents a review of promising techniques for very low bit-rate, below 64 kb/s, image sequence coding. Image sequence coding at such low rates will be a crucial technique in forthcoming visual services, e.g., visual information transmission and storage. A typical application is to transmit moving videophone scenes through the existing analog telephone lines or via a mobile channel. Two types of potential coding techniques are addressed: waveform-based image sequence coding and model-based image sequence coding. >
TL;DR: A new method for space-varying image restoration using the method of projection onto convex sets (POCS) that allows the use of a different blurring function at each pixel of the image in a computationally efficient manner is proposed.
Abstract: We propose a new method for space-varying image restoration using the method of projection onto convex sets (POCS). The formulation allows the use of a different blurring function at each pixel of the image in a computationally efficient manner. We illustrate the performance of the proposed approach by comparing the new results with those of the ROMKF method on simulated images. We also present results on a real-life image with unknown space-varying out-of-focus blur. >
TL;DR: A method of selecting the color filters using a priori information about the viewing illuminants is introduced, which is found in the design of scanners, copiers, and television systems.
Abstract: The quality of color correction is dependent on the filters used to scan the image. This paper introduces a method of selecting the color filters using a priori information about the viewing illuminants. Color correction results using the derived filters are compared with color correction results using filters that are optimal for individual viewing and recording illuminants. The comparison is performed using the CIE PEL/sub L*a*b*/ perceptual color difference measure. Applications of this work are found in the design of scanners, copiers, and television systems. >
TL;DR: A low-complexity iterative algorithm is introduced for joint estimation of forward and backward motion vectors in interpolative prediction of video, demonstrating that with this technique the prediction error in some scenes is significantly reduced.
Abstract: A low-complexity iterative algorithm is introduced for joint estimation of forward and backward motion vectors in interpolative prediction of video. Starting from the initial values obtained by a commonly-used block matching independent search method, the motion vectors are iteratively refined until a locally optimal solution to the motion estimation problem for interpolative prediction is achieved. Each iteration consists of a series of two similar procedures. First, the backward motion vector is fixed and a new forward motion vector is searched to minimize the interpolation error. Then the forward motion vector is fixed and the backward motion vector is similarly refined by minimizing the interpolation error. This process is repeated until the interpolation error stops decreasing. Computer simulation results demonstrate that with this technique the prediction error in some scenes is significantly reduced. >
TL;DR: A novel technique to dynamically adapt motion interpolation structures by temporal segmentation is presented, and the results compare favorably with those for conventional fixed GOP structures.
Abstract: In this paper we present a novel technique to dynamically adapt motion interpolation structures by temporal segmentation. The number of reference frames and the intervals between them are adjusted according to the temporal variation of the input video. Bit-rate control for this dynamic group of pictures (GOP) structure is achieved by taking advantage of temporal masking in human vision. Constant picture quality can be obtained by variable-bit-rate coding using this approach. Further improvement can be made when the intervals between reference frames are chosen by minimizing a measure of the coding difficulty of a GOP. Advantages for low bit-rate coding and implications for variable-bit-rate coding are discussed. Simulations on test video are presented for various GOP structures and temporal segmentation methods, and the results compare favorably with those for conventional fixed GOP structures. >
TL;DR: Although the proposed algorithm does not provide optimal performance, the algorithm can be implemented easily with quite low computational complexity, as compared with others.
Abstract: We propose a stable feedback control algorithm of the buffer state using the controlled Lagrange multiplier in combined rate-distortion characteristics for a buffer-constrained adaptive quantization. The proposed algorithm is established using feedback control theory and is described by the state equation with nonlinearity in the feedback path. The nonlinearity of the state equation comes from the average distortion-rate curve of the information source. The stability of this buffer control algorithm is shown using Lyapunov stability theory. It is shown that the stability of the algorithm depends on average distortion-rate curve and buffer size, and sufficient conditions for the algorithm to be stable are obtained from the usual average distortion-rate curve. In addition, it is observed from experimental results that the performance of the proposed algorithm and the optimal algorithm is not significantly different. Although the proposed algorithm does not provide optimal performance, the algorithm can be implemented easily with quite low computational complexity, as compared with others. >
TL;DR: The proposed subband coding scheme utilizes orientation of local image features to avoid the highly objectionable Gibbs-like phenomena observed at reconstructed image edges with conventional subband schemes at low bit rates.
Abstract: In the subband coding of images, directionality of image features has thus far been exploited very little. The proposed subband coding scheme utilizes orientation of local image features to avoid the highly objectionable Gibbs-like phenomena observed at reconstructed image edges with conventional subband schemes at low bit rates, At comparable bit rates, the subjective image quality obtained by our orientation adaptive scheme is considerably enhanced over a conventional separable subband coding scheme, as well as other separable approaches such as the JPEG compression standard. >
TL;DR: Adapt sidelobe reduction (ASR) provides a single-realization complex-valued estimate of the Fourier transform that suppresses sidelobes and noise, which is critical for large multidimensional problems such as synthetic aperture radar (SAR) image formation.
Abstract: The paper describes a class of adaptive weighting functions that greatly reduce sidelobes, interference, and noise in Fourier transform data By restricting the class of adaptive weighting functions, the adaptively weighted Fourier transform data can be represented as the convolution of the unweighted Fourier transform with a data adaptive FIR filter where one selects the FIR filter coefficients to maximize signal-to-interference ratio This adaptive sidelobe reduction (ASR) procedure is analogous to Capon's (1969) minimum variance method (MVM) of adaptive spectral estimation Unlike MVM, which provides a statistical estimate of the real-valued power spectral density, thereby estimating noise level and improving resolution, ASR provides a single-realization complex-valued estimate of the Fourier transform that suppresses sidelobes and noise Further, the computational complexity of ASR is dramatically lower than that of MVM, which is critical for large multidimensional problems such as synthetic aperture radar (SAR) image formation ASR performance characteristics can be varied through the choice of filter order, l/sub 1/- or l/sub 2/-norm filter vector constraints and a separable or nonseparable multidimensional implementation The author compares simulated point scattering SAR imagery produced by the ASR, MVM, and MUSIC algorithms and illustrates ASR performance on three sets of collected SAR imagery >
TL;DR: Two approaches for ultrasonic image processing are examined and a modification of the learning vector quantizer (L(2 ) LVQ) is proposed in such a way that the weight vectors of the output neurons correspond to the L(2) mean instead of the sample arithmetic mean of the input observations.
Abstract: Two approaches for ultrasonic image processing are examined. First, signal-adaptive maximum likelihood (SAML) filters are proposed for ultrasonic speckle removal. It is shown that in the case of displayed ultrasound (US) image data the maximum likelihood (ML) estimator of the original (noiseless) signal closely resembles the L/sub 2/ mean which has been proven earlier to be the ML estimator of the original signal in US B-mode data. Thus, the design of signal-adaptive L/sub 2/ mean filters is treated for US B-mode data and displayed US image data as well. Secondly, the segmentation of ultrasonic images using self-organizing neural networks (NN) is investigated. A modification of the learning vector quantizer (L/sub 2/ LVQ) is proposed in such a way that the weight vectors of the output neurons correspond to the L/sub 2/ mean instead of the sample arithmetic mean of the input observations. The convergence in the mean and in the mean square of the proposed L/sub 2/ LVQ NN are studied. L/sub 2/ LVQ is combined with signal-adaptive filtering in order to allow preservation of image edges and details as well as maximum speckle reduction in homogeneous regions. >
TL;DR: It is shown that a VBR encoder using this algorithm will provide decoded image sequences with a consistent perceived quality that is comparable with, or better than, the perceived quality of images coded with a CBR encoder.
Abstract: This paper describes a rate control algorithm for a variable bit-rate (VBR) video coder. The algorithm described varies the quantizer step size of the coder according to properties of an image sequence that affect the perception of errors. The algorithm also limits the output bit-rate of the coder without the use of buffers to more efficiently use network bandwidth. It is shown that a VBR encoder using this algorithm will provide decoded image sequences with a consistent perceived quality that is comparable with, or better than, the perceived quality of images coded with a CBR encoder. >