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Image Processing, IEEE Transactions on

Issue 8 • Date Aug. 2004

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Displaying Results 1 - 22 of 22
  • Table of contents

    Page(s): c1 - c4
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  • IEEE Transactions on Image Processing publication information

    Page(s): c2
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  • Embedded image compression based on wavelet pixel classification and sorting

    Page(s): 1011 - 1017
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (365 KB) |  | HTML iconHTML  

    The method of modeling and ordering in wavelet domain is very important to design a successful algorithm of embedded image compression. In this paper, the modeling is limited to "pixel classification," the relationship between wavelet pixels in significance coding. Similarly, the ordering is limited to "pixel sorting," the coding order of wavelet pixels. We use pixel classification and sorting to provide a better understanding of previous works. The image pixels in wavelet domain are classified and sorted, either explicitly or implicitly, for embedded image compression. A new embedded image code is proposed based on a novel pixel classification and sorting (PCAS) scheme in wavelet domain. In PCAS, pixels to be coded are classified into several quantized contexts based on a large context template and sorted based on their estimated significance probabilities. The purpose of pixel classification is to exploit the intraband correlation in wavelet domain. Pixel sorting employs several fractional bit-plane coding passes to improve the rate-distortion performance. The proposed pixel classification and sorting technique is simple, yet effective, producing an embedded image code with excellent compression performance. In addition, our algorithm is able to provide either spatial or quality scalability with flexible complexity. View full abstract»

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  • Invertible temporal subband/wavelet filter banks with half-pixel-accurate motion compensation

    Page(s): 1018 - 1028
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3659 KB) |  | HTML iconHTML  

    Three-dimensional (3-D) subband/wavelet coding with motion compensation has been demonstrated to be an efficient technique for video coding applications in some recent research works. When motion compensation is performed with half-pixel accuracy, images need to be interpolated in both temporal subband analysis and synthesis stages. The resulting subband filter banks developed in these former algorithms were not invertible due to image interpolation. In this paper, an invertible temporal analysis/synthesis system with half-pixel-accurate motion compensation is presented. We look at temporal decomposition of image sequences as a kind of down-conversion of the sampling lattices. The earlier motion-compensated (MC) interlaced/progressive scan conversion scheme is extended for temporal subband analysis/synthesis. The proposed subband/wavelet filter banks allow perfect reconstruction of the decomposed video signal while retaining high energy compaction of subband transforms. The invertible filter banks are then utilized in our 3-D subband video coder. This video coding system does not contain the temporal DPCM loop employed in the conventional hybrid coder and the earlier MC 3-D subband coders. The experimental results show a significant PSNR improvement by the proposed method. The generalization of our algorithm for MC temporal filtering at arbitrary subpixel accuracy is also discussed. View full abstract»

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  • Highly scalable video compression with scalable motion coding

    Page(s): 1029 - 1041
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (536 KB) |  | HTML iconHTML  

    A scalable video coder cannot be equally efficient over a wide range of bit rates unless both the video data and the motion information are scalable. We propose a wavelet-based, highly scalable video compression scheme with rate-scalable motion coding. The proposed method involves the construction of quality layers for the coded sample data and a separate set of quality layers for the coded motion parameters. When the motion layers are truncated, the decoder receives a quantized version of the motion parameters used to code the sample data. The effect of motion parameter quantization on the reconstructed video distortion is described by a linear model. The optimal tradeoff between the motion and subband bit rates is determined after compression. We propose two methods to determine the optimal tradeoff, one of which explicitly utilizes the linear model. This method performs comparably to a brute force search method, reinforcing the validity of the linear model itself. Experimental results indicate that the cost of scalability is small. In addition, considerable performance improvements are observed at low bit rates, relative to lossless coding of the motion information. View full abstract»

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  • Fast motion estimation using bidirectional gradient methods

    Page(s): 1042 - 1054
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1974 KB) |  | HTML iconHTML  

    Gradient-based motion estimation methods (GMs) are considered to be in the heart of state-of-the-art registration algorithms, being able to account for both pixel and subpixel registration and to handle various motion models (translation, rotation, affine, and projective). These methods estimate the motion between two images based on the local changes in the image intensities while assuming image smoothness. This paper offers two main contributions. The first is enhancement of the GM technique by introducing two new bidirectional formulations of the GM. These improve the convergence properties for large motions. The second is that we present an analytical convergence analysis of the GM and its properties. Experimental results demonstrate the applicability of these algorithms to real images. View full abstract»

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  • Some computational aspects of discrete orthonormal moments

    Page(s): 1055 - 1059
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (516 KB) |  | HTML iconHTML  

    Discrete orthogonal moments have several computational advantages over continuous moments. However, when the moment order becomes large, discrete orthogonal moments (such as the Tchebichef moments) tend to exhibit numerical instabilities. This paper introduces the orthonormal version of Tchebichef moments, and analyzes some of their computational aspects. The recursive procedure used for polynomial evaluation can be suitably modified to reduce the accumulation of numerical errors. The proposed set of moments can be used for representing image shape features and for reconstructing an image from its moments with a high degree of accuracy. View full abstract»

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  • Efficient implementation of accurate geometric transformations for 2-D and 3-D image processing

    Page(s): 1060 - 1065
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (159 KB)  

    This paper proposes the use of a polynomial interpolator structure (based on Horner's scheme) which is efficiently realizable in hardware, for high-quality geometric transformation of two- and three-dimensional images. Polynomial-based interpolators such as cubic B-splines and optimal interpolators of shortest support are shown to be exactly implementable in the Horner structure framework. This structure suggests a hardware/software partition which can lead to efficient implementations for multidimensional interpolation. View full abstract»

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  • Content-based adaptive spatio-temporal methods for MPEG repair

    Page(s): 1066 - 1077
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    Block loss and propagation error due to cell loss or missing packet information during the transmission over lossy networks can cause severe degradation of block and predictive-based video coding. Herein, new fast spatial and temporal methods are presented for block loss recovery. In the spatial algorithm, missing block recovery and edge extention are performed by pixel replacement based on range constraints imposed by surrounding neighborhood edge information and structure. In the temporal algorithm, an adaptive temporal correlation method is proposed for motion vector (MV) recovery. Parameters for the temporal correlation measurement are adaptively changed in accordance to surrounding edge information of a missing macroblock (MB). The temporal technique utilizes pixels in the reference frame as well as surrounding pixels of the lost block. Spatial motion compensation is applied after MV recovery when the reference frame does not have sufficient information for lost MB restoration. Simulations demonstrate that the proposed algorithms recover image information reliably using both spatial and temporal restoration. We compare the proposed algorithm with other procedures with consistently favorable results. View full abstract»

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  • The efficient algorithms for achieving Euclidean distance transformation

    Page(s): 1078 - 1091
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1079 KB) |  | HTML iconHTML  

    Euclidean distance transformation (EDT) is used to convert a digital binary image consisting of object (foreground) and nonobject (background) pixels into another image where each pixel has a value of the minimum Euclidean distance from nonobject pixels. In this paper, the improved iterative erosion algorithm is proposed to avoid the redundant calculations in the iterative erosion algorithm. Furthermore, to avoid the iterative operations, the two-scan-based algorithm by a deriving approach is developed for achieving EDT correctly and efficiently in a constant time. Besides, we discover when obstacles appear in the image, many algorithms cannot achieve the correct EDT except our two-scan-based algorithm. Moreover, the two-scan-based algorithm does not require the additional cost of preprocessing or relative-coordinates recording. View full abstract»

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  • Perceptually-weighted evaluation criteria for segmentation masks in video sequences

    Page(s): 1092 - 1103
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1299 KB) |  | HTML iconHTML  

    In order to complement subjective evaluation of the quality of segmentation masks, this paper introduces a procedure for automatically assessing this quality. Algorithmically computed figures of merit are proposed. Assuming the existence of a perfect reference mask (ground truth), generated manually or with a reliable procedure over a test set, these figures of merit take into account visually desirable properties of a segmentation mask in order to provide the user with metrics that best quantify the spatial and temporal accuracy of the segmentation masks. For the sake of easy interpretation, results are presented on a peaked signal-to-noise ratio-like logarithmic scale. View full abstract»

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  • Multiresolution segmentation of natural images: from linear to nonlinear scale-space representations

    Page(s): 1104 - 1114
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1253 KB) |  | HTML iconHTML  

    In this paper, we introduce a framework that merges classical ideas borrowed from scale-space and multiresolution segmentation with nonlinear partial differential equations. A nonlinear scale-space stack is constructed by means of an appropriate diffusion equation. This stack is analyzed and a tree of coherent segments is constructed based on relationships between different scale layers. Pruning this tree proves to be a very efficient tool for unsupervised segmentation of different classes of images (e.g., natural, medical, etc.). This technique is light on the computational point of view and can be extended to nonscalar data in a straightforward manner. View full abstract»

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  • A maximum likelihood approach for image registration using control point and intensity

    Page(s): 1115 - 1127
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7155 KB) |  | HTML iconHTML  

    Registration of multidate or multisensor images is an essential process in many image processing applications including remote sensing, medical image analysis, and computer vision. Control point (CP) and intensity are the two basic features used separately for image registration in the literature. In this paper, an exact maximum likelihood (EML) registration method, which combines both CP and intensity, is proposed for image alignment. The EML registration method maximizes the likelihood function based on CP and intensity to estimate the registration parameters, including affine transformation and CP coordinates. The explicit formulas of the Cramer-Rao bound (CRB) are also derived for the proposed EML and conventional image registration algorithms. The performances of these image registration techniques are evaluated with the CRBs. View full abstract»

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  • Improved Poisson intensity estimation: denoising application using Poisson data

    Page(s): 1128 - 1135
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    Recently, Timmermann and Nowak (1999) developed algorithms for estimating the means of independent Poisson random variables. The algorithms are based on a multiscale model where certain random variables are assumed to obey a beta-mixture density function. Timmermann and Nowak simplify the density estimation problem by assuming the beta parameters are known and only one mixture parameter is unknown. They use the observed data and the method of moments to estimate the unknown mixture parameter. Taking a different approach, we generate training data from the observed data and compute maximum likelihood estimates of all of the beta-mixture parameters. To assess the improved performance obtained by the proposed modification, we consider a denoising application using Poisson data. View full abstract»

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  • Binary halftone image resolution increasing by decision tree learning

    Page(s): 1136 - 1146
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2690 KB) |  | HTML iconHTML  

    This paper presents a new, accurate, and efficient technique to increase the spatial resolution of binary halftone images. It makes use of a machine learning process to automatically design a zoom operator starting from pairs of input-output sample images. To accurately zoom a halftone image, a large window and large sample images are required. Unfortunately, in this case, the execution time required by most of the previous techniques may be prohibitive. The new solution overcomes this difficulty by using decision tree (DT) learning. Original DT learning is modified to obtain a more efficient technique (WZDT learning). It is useful to know, a priori , sample complexity (the number of training samples needed to obtain, with probability 1-δ, an operator with accuracy ε): we use the probably approximately correct (PAC) learning theory to compute the sample complexity. Since the PAC theory usually yields an overestimated sample complexity, statistical estimation is used to evaluate, a posteriori, a tight error bound. Statistical estimation is also used to choose an appropriate window and to show that DT learning has good inductive bias. The new technique is more accurate than a zooming method based on simple inverse halftoning techniques. The quality of the proposed solution is very close to the theoretical optimal obtainable quality for a neighborhood-based zooming process using the Hamming distance to quantify the error. View full abstract»

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  • Reversible watermark using the difference expansion of a generalized integer transform

    Page(s): 1147 - 1156
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    A reversible watermarking algorithm with very high data-hiding capacity has been developed for color images. The algorithm allows the watermarking process to be reversed, which restores the exact original image. The algorithm hides several bits in the difference expansion of vectors of adjacent pixels. The required general reversible integer transform and the necessary conditions to avoid underflow and overflow are derived for any vector of arbitrary length. Also, the potential payload size that can be embedded into a host image is discussed, and a feedback system for controlling this size is developed. In addition, to maximize the amount of data that can be hidden into an image, the embedding algorithm can be applied recursively across the color components. Simulation results using spatial triplets, spatial quads, cross-color triplets, and cross-color quads are presented and compared with the existing reversible watermarking algorithms. These results indicate that the spatial, quad-based algorithm allows for hiding the largest payload at the highest signal-to-noise ratio. View full abstract»

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  • IEEE Transactions on Image Processing Edics

    Page(s): 1158
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  • IEEE Transactions on Image Processing Information for authors

    Page(s): 1159 - 1160
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  • IEEE 2005 International Conference on Image Processing

    Page(s): 1161
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  • 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

    Page(s): 1162
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  • IEEE Signal Processing Society Information

    Page(s): c3
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Aims & Scope

IEEE Transactions on Image Processing focuses on signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing.

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Meet Our Editors

Editor-in-Chief
Scott Acton
University of Virginia
Charlottesville, VA, USA
E-mail: acton@virginia.edu 
Phone: +1 434-982-2003