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Image Processing, IEEE Transactions on

Issue 9 • Date Sept. 2007

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Displaying Results 1 - 25 of 30
  • Table of contents

    Page(s): C1 - C4
    Save to Project icon | Request Permissions | PDF file iconPDF (40 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Image Processing publication information

    Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (35 KB)  
    Freely Available from IEEE
  • New Architecture for MPEG Video Streaming System With Backward Playback Support

    Page(s): 2169 - 2183
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1129 KB) |  | HTML iconHTML  

    MPEG digital video is becoming ubiquitous for video storage and communications. It is often desirable to perform various video cassette recording (VCR) functions such as backward playback in MPEG videos. However, the predictive processing techniques employed in MPEG severely complicate the backward-play operation. A straightforward implementation of backward playback is to transmit and decode the whole group-of-picture (GOP), store all the decoded frames in the decoder buffer, and play the decoded frames in reverse order. This approach requires a significant buffer in the decoder, which depends on the GOP size, to store the decoded frames. This approach could not be possible in a severely constrained memory requirement. Another alternative is to decode the GOP up to the current frame to be displayed, and then go back to decode the GOP again up to the next frame to be displayed. This approach does not need the huge buffer, but requires much higher bandwidth of the network and complexity of the decoder. In this paper, we propose a macroblock-based algorithm for an efficient implementation of the MPEG video streaming system to provide backward playback over a network with the minimal requirements on the network bandwidth and the decoder complexity. The proposed algorithm classifies macroblocks in the requested frame into backward macroblocks (BMBs) and forward/backward macroblocks (FBMBs). Two macroblock-based techniques are used to manipulate different types of macroblocks in the compressed domain and the server then sends the processed macroblocks to the client machine. For BMBs, a VLC-domain technique is adopted to reduce the number of macroblocks that need to be decoded by the decoder and the number of bits that need to be sent over the network in the backward-play operation. We then propose a newly mixed VLC/DCT-domain technique to handle FBMBs in order to further reduce the computational complexity of the decoder. With these compressed-domain techniques, the prop- - osed architecture only manipulates macroblocks either in the VLC domain or the quantized DCT domain resulting in low server complexity. Experimental results show that, as compared to the conventional system, the new streaming system reduces the required network bandwidth and the decoder complexity significantly. View full abstract»

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  • Color Reproduction From Noisy CFA Data of Single Sensor Digital Cameras

    Page(s): 2184 - 2197
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1464 KB) |  | HTML iconHTML  

    Single sensor digital color still/video cameras capture images using a color filter array (CFA) and require color interpolation (demosaicking) to reconstruct full color images. The color reproduction has to combat sensor noises which are channel dependent. If untreated in demosaicking, sensor noises can cause color artifacts that are hard to remove later by a separate denoising process, because the demosaicking process complicates the noise characteristics by blending noises of different color channels. This paper presents a joint demosaicking-denoising approach to overcome this difficulty. The color image is restored from noisy mosaic data in two steps. First, the difference signals of color channels are estimated by linear minimum mean square-error estimation. This process exploits both spectral and spatial correlations to simultaneously suppress sensor noise and interpolation error. With the estimated difference signals, the full resolution green channel is recovered. The second step involves in a wavelet-based denoising process to remove the CFA channel-dependent noises from the reconstructed green channel. The red and blue channels are subsequently recovered. Simulated and real CFA mosaic data are used to evaluate the performance of the proposed joint demosaicking-denoising scheme and compare it with many recently developed sophisticated demosaicking and denoising schemes. View full abstract»

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  • Combined Curvelet Shrinkage and Nonlinear Anisotropic Diffusion

    Page(s): 2198 - 2206
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3291 KB) |  | HTML iconHTML  

    In this paper, a diffusion-based curvelet shrinkage is proposed for discontinuity-preserving denoising using a combination of a new tight frame of curvelets with a nonlinear diffusion scheme. In order to suppress the pseudo-Gibbs and curvelet-like artifacts, the conventional shrinkage results are further processed by a projected total variation diffusion, in which only the insignificant curvelet coefficients or high-frequency part of the signal are changed by use of a constrained projection. Numerical experiments from piecewise-smooth to textured images show good performances of the proposed method to recover the shape of edges and important detailed components, in comparison to some existing methods. View full abstract»

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  • Edge-Based Color Constancy

    Page(s): 2207 - 2214
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (946 KB) |  | HTML iconHTML  

    Color constancy is the ability to measure colors of objects independent of the color of the light source. A well-known color constancy method is based on the gray-world assumption which assumes that the average reflectance of surfaces in the world is achromatic. In this paper, we propose a new hypothesis for color constancy namely the gray-edge hypothesis, which assumes that the average edge difference in a scene is achromatic. Based on this hypothesis, we propose an algorithm for color constancy. Contrary to existing color constancy algorithms, which are computed from the zero-order structure of images, our method is based on the derivative structure of images. Furthermore, we propose a framework which unifies a variety of known (gray-world, max-RGB, Minkowski norm) and the newly proposed gray-edge and higher order gray-edge algorithms. The quality of the various instantiations of the framework is tested and compared to the state-of-the-art color constancy methods on two large data sets of images recording objects under a large number of different light sources. The experiments show that the proposed color constancy algorithms obtain comparable results as the state-of-the-art color constancy methods with the merit of being computationally more efficient. View full abstract»

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  • Geometric Direct Search Algorithms for Image Registration

    Page(s): 2215 - 2224
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1222 KB) |  | HTML iconHTML  

    A widely used approach to image registration involves finding the general linear transformation that maximizes the mutual information between two images, with the transformation being rigid-body [i.e., belonging to SE(3)] or volume-preserving [i.e., belonging to SL(3)]. In this paper, we present coordinate-invariant, geometric versions of the Nelder-Mead optimization algorithm on the groups SL(3), SE(3), and their various subgroups, that are applicable to a wide class of image registration problems. Because the algorithms respect the geometric structure of the underlying groups, they are numerically more stable, and exhibit better convergence properties than existing local coordinate-based algorithms. Experimental results demonstrate the improved convergence properties of our geometric algorithms. View full abstract»

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  • Low Bit-Rate Image Coding Using Adaptive Geometric Piecewise Polynomial Approximation

    Page(s): 2225 - 2233
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2949 KB) |  | HTML iconHTML  

    We present a new image coding algorithm, the geometric piecewise polynomials (GPP) method, that draws on recent developments in the theory of adaptive multivariate piecewise polynomials approximation. The algorithm relies on a segmentation stage whose goal is to minimize a functional that is conceptually similar to the Mumford-Shah functional except that it measures the smoothness of the segmentation instead of the length. The initial segmentation is ldquoprunedrdquo and the remaining curve portions are lossy encoded. The image is then further partitioned and approximated by low order polynomials on the subdomains. We show examples where our algorithm outperforms state-of-the-art wavelet coding in the low bit-rate range. The GPP algorithm significantly outperforms wavelet based coding methods on graphic and cartoon images. Also, at the bit rate 0.05 bits per pixel, the GPP algorithm achieves on the test image Cameraman, which has a geometric structure, a PSNR of 21.5 dB, while the JPEG2000 Kakadu software obtains PSNR of 20 dB. For the test image Lena, the GPP algorithm obtains the same PSNR as JPEG2000, but with better visual quality at 0.03 bpp. View full abstract»

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  • Efficient Coding of Shape and Transparency for Video Objects

    Page(s): 2234 - 2244
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (810 KB) |  | HTML iconHTML  

    A novel scheme for coding gray-level alpha planes in object-based video is presented. Gray-level alpha planes convey the shape and the transparency information, which are required for smooth composition of video objects. The algorithm proposed is based on the segmentation of the alpha plane in three layers: binary shape layer, opaque layer, and intermediate layer. Thus, the latter two layers replace the single transparency layer of MPEG-4 Part 2. Different encoding schemes are specifically designed for each layer, utilizing cross-layer correlations to reduce the bit rate. First, the binary shape layer is processed by a novel video shape coder. In intra mode, the DSLSC binary image coder presented in is used. This is extended here with an intermode utilizing temporal redundancies in shape image sequences. Then the opaque layer is compressed by a newly designed scheme which models the strong correlation with the binary shape layer by morphological erosion operations. Finally, three solutions are proposed for coding the intermediate layer. The knowledge of the two previously encoded layers is utilized in order to increase compression efficiency. Experimental results are reported demonstrating that the proposed techniques provide substantial bit rate savings coding shape and transparency when compared to the tools adopted in MPEG-4 Part 2. View full abstract»

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  • Joint Exact Histogram Specification and Image Enhancement Through the Wavelet Transform

    Page(s): 2245 - 2250
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3315 KB) |  | HTML iconHTML  

    Histogram specification (equalization) is an important tool for tasks such as image enhancement and normalization. Although this problem has exact solution for continuous images, in the case of digital images, it is ill posed. Recently, an exact pixel ordering method based solely on local image intensity was proposed, yet still with some limitations, especially since it ignores the important image edge information. In this paper, we present a wavelet-based method that simultaneously achieves the exact histogram specification and good image enhancement performance. It does so through a carefully designed strict pixel ordering process, during which the wavelet coefficients are fine tuned for the image enhancement purpose. Compared to previous work, this approach takes into account not only local mean intensity values, but also local edge information. Other advantages include fast pixel ordering, good statistical models, and better image enhancement performance. Experimental results and comparison with state-of-the-art methods are presented. View full abstract»

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  • Variational Models for Image Colorization via Chromaticity and Brightness Decomposition

    Page(s): 2251 - 2261
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1254 KB) |  | HTML iconHTML  

    Colorization refers to an image processing task which recovers color in grayscale images when only small regions with color are given. We propose a couple of variational models using chromaticity color components to colorize black and white images. We first consider total variation minimizing (TV) colorization which is an extension from TV inpainting to color using chromaticity model. Second, we further modify our model to weighted harmonic maps for colorization. This model adds edge information from the brightness data, while it reconstructs smooth color values for each homogeneous region. We introduce penalized versions of the variational models, we analyze their convergence properties, and we present numerical results including extension to texture colorization. View full abstract»

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  • Spatiotemporal Inpainting for Recovering Texture Maps of Occluded Building Facades

    Page(s): 2262 - 2271
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1145 KB) |  | HTML iconHTML  

    We present a technique for constructing a ldquocleanrdquo texture map of a partially occluded building facade from a series of images taken from a moving camera. Building regions blocked by trees, signs, people, and other foreground objects in a minority of views can be recovered via temporal median filtering on a registered image mosaic of the planar facade. However, when such areas are occluded in the majority of camera views, appearance information from other visible portions of the facade provides a critical cue to correctly complete the mosaic. In this paper, we apply a robust measure of spread to infer whether a particular mosaic pixel is occluded in a majority of views, and introduce a novel spatiotemporal timeline-based inpainting algorithm that uses appearance and motion cues in order to fill the texture map in majority-occluded regions. We describe methods for automatically training appearance-based classifiers from a coarse motion-based segmentation to efficiently recognize foreground and background patches in static imagery. Results of recovered building facades are shown for various sequences. View full abstract»

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  • Learning Multimodal Dictionaries

    Page(s): 2272 - 2283
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2592 KB) |  | HTML iconHTML  

    Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal is proposed, as well. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible, and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and it is able to discover underlying structures in the data. The detection of such audio-video patterns in audiovisual clips allows to effectively localize the sound source on the video in presence of substantial acoustic and visual distractors, outperforming state-of-the-art audiovisual localization algorithms. View full abstract»

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  • VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images

    Page(s): 2284 - 2298
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1022 KB) |  | HTML iconHTML  

    This paper presents an efficient metric for quantifying the visual fidelity of natural images based on near-threshold and suprathreshold properties of human vision. The proposed metric, the visual signal-to-noise ratio (VSNR), operates via a two-stage approach. In the first stage, contrast thresholds for detection of distortions in the presence of natural images are computed via wavelet-based models of visual masking and visual summation in order to determine whether the distortions in the distorted image are visible. If the distortions are below the threshold of detection, the distorted image is deemed to be of perfect visual fidelity (VSNR = infin)and no further analysis is required. If the distortions are suprathreshold, a second stage is applied which operates based on the low-level visual property of perceived contrast, and the mid-level visual property of global precedence. These two properties are modeled as Euclidean distances in distortion-contrast space of a multiscale wavelet decomposition, and VSNR is computed based on a simple linear sum of these distances. The proposed VSNR metric is generally competitive with current metrics of visual fidelity; it is efficient both in terms of its low computational complexity and in terms of its low memory requirements; and it operates based on physical luminances and visual angle (rather than on digital pixel values and pixel-based dimensions) to accommodate different viewing conditions. View full abstract»

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  • PKCS: A Polynomial Kernel Family With Compact Support for Scale- Space Image Processing

    Page(s): 2299 - 2308
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1542 KB) |  | HTML iconHTML  

    In a scale-space framework, the Gaussian kernel has some properties that make it unique. However, because of its infinite support, exact implementation of this kernel is not possible. To avoid this drawback, there exist two different approaches: approximating the Gaussian kernel by a finite support kernel, or defining new kernels with properties closed to the Gaussian. In this paper, we propose a polynomial kernel family with compact support which overcomes the Gaussian practical drawbacks while preserving a large number of the useful Gaussian properties. The new kernels are not obtained by approximating the Gaussian, though they are derived from it. We show that, for a suitable choice of kernel parameters, this family provides an approximated solution of the diffusion equation and satisfies some other basic constraints of the linear scale-space theory. The construction and properties of the proposed kernel are described, and an application in which handwritten data are extracted from noisy document images is presented. View full abstract»

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  • SAR Image Autofocus By Sharpness Optimization: A Theoretical Study

    Page(s): 2309 - 2321
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2217 KB) |  | HTML iconHTML  

    Synthetic aperture radar (SAR) autofocus techniques that optimize sharpness metrics can produce excellent restorations in comparison with conventional autofocus approaches. To help formalize the understanding of metric-based SAR autofocus methods, and to gain more insight into their performance, we present a theoretical analysis of these techniques using simple image models. Specifically, we consider the intensity-squared metric, and a dominant point-targets image model, and derive expressions for the resulting objective function. We examine the conditions under which the perfectly focused image models correspond to stationary points of the objective function. A key contribution is that we demonstrate formally, for the specific case of intensity-squared minimization autofocus, the mechanism by which metric-based methods utilize the multichannel defocusing model of SAR autofocus to enforce the stationary point property for multiple image columns. Furthermore, our analysis shows that the objective function has a special separble property through which it can be well approximated locally by a sum of 1-D functions of each phase error component. This allows fast performance through solving a sequence of 1-D optimization problems for each phase component simultaneously. Simulation results using the proposed models and actual SAR imagery confirm that the analysis extends well to realistic situations. View full abstract»

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  • A Unified Approach to Superresolution and Multichannel Blind Deconvolution

    Page(s): 2322 - 2332
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2582 KB) |  | HTML iconHTML  

    This paper presents a new approach to the blind deconvolution and superresolution problem of multiple degraded low-resolution frames of the original scene. We do not assume any prior information about the shape of degradation blurs. The proposed approach consists of building a regularized energy function and minimizing it with respect to the original image and blurs, where regularization is carried out in both the image and blur domains. The image regularization based on variational principles maintains stable performance under severe noise corruption. The blur regularization guarantees consistency of the solution by exploiting differences among the acquired low-resolution images. Several experiments on synthetic and real data illustrate the robustness and utilization of the proposed technique in real applications. View full abstract»

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  • Movie Denoising by Average of Warped Lines

    Page(s): 2333 - 2347
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6506 KB) |  | HTML iconHTML  

    Here, we present an efficient method for movie denoising that does not require any motion estimation. The method is based on the well-known fact that averaging several realizations of a random variable reduces the variance. For each pixel to be denoised, we look for close similar samples along the level surface passing through it. With these similar samples, we estimate the denoised pixel. The method to find close similar samples is done via warping lines in spatiotemporal neighborhoods. For that end, we present an algorithm based on a method for epipolar line matching in stereo pairs which has per-line complexity O(N) , where is the number of columns in the image. In this way, when applied to the image sequence, our algorithm is computationally efficient, having a complexity of the order of the total number of pixels. Furthermore, we show that the presented method is unsupervised and is adapted to denoise image sequences with an additive white noise while respecting the visual details on the movie frames. We have also experimented with other types of noise with satisfactory results. View full abstract»

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  • Spatiotemporal Selective Extrapolation for 3-D Signals and Its Applications in Video Communications

    Page(s): 2348 - 2360
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1710 KB) |  | HTML iconHTML  

    In this paper, we derive a spatiotemporal extrapolation method for 3-D discrete signals. Extending a discrete signal beyond a limited number of known samples is commonly referred to as discrete signal extrapolation. Extrapolation problems arise in many applications in video communications. Transmission errors in video communications may cause data losses which are concealed by extrapolating the surrounding video signal into the missing area. The same principle is applied for TV logo removal. Prediction in hybrid video coding is also interpreted as an extrapolation problem. Conventionally, the unknown areas in the video sequence are estimated from either the spatial or temporal surrounding. Our approach considers the spatiotemporal signal including the missing area in a volume and replaces the unknown samples by extrapolating the surrounding signal from spatial, as well as temporal direction. By exploiting spatial and temporal correlations at the same time, it is possible to inherently compensate motion. Deviations in luminance occurring from frame to frame can be compensated, too. View full abstract»

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  • Nonparametric Snakes

    Page(s): 2361 - 2368
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (903 KB) |  | HTML iconHTML  

    Active contours, or so-called snakes, require some parameters to determine the form of the external force or to adjust the tradeoff between the internal forces and the external forces acting on the active contour. However, the optimal values of these parameters cannot be easily identified in a general sense. The usual way to find these required parameters is to run the algorithm several times for a different set of parameters, until a satisfactory performance is obtained. Our nonparametric formulation translates the problem of seeking these unknown parameters into the problem of seeking a good edge probability density estimate. Density estimation is a well-researched field, and our nonparametric formulation allows using well-known concepts of density estimation to get rid of the exhaustive parameter search. Indeed, with the use of kernel density estimation these parameters can be defined locally, whereas, in the original snake approach, all the shape parameters are defined globally. We tested the proposed method on synthetic and real images and obtained comparatively better results. View full abstract»

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  • Subspace-Based and DIRECT Algorithms for Distorted Circular Contour Estimation

    Page(s): 2369 - 2378
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (996 KB) |  | HTML iconHTML  

    Circular features are commonly sought in digital image processing. The subspace-based line detection (SLIDE) method proposed to estimate the center and the radius of a single circle. In this paper, we introduce a novel method for estimating several radii while extending the circle estimation to retrieve circular-like distorted contours. Particularly, we develop and validate a new model for virtual signal generation by simulating a circular antenna. The circle center is estimated by the SLIDE method. A variable speed propagation scheme toward the circular antenna yields a linear phase signal. Therefore, a high-resolution method provides the radius. Either the gradient method or the more robust combination of dividing rectangles and spline interpolation can extend this method for free form object segmentation. The retrieval of multiple non concentric circles and rotated ellipses is also considered. To evaluate the performance of the proposed methods, we compare them with a least-squares method, Hough transform, and gradient vector flow. We apply the proposed method to hand-made images while considering some real-world images. View full abstract»

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  • Low Bit-Rate Compression of Facial Images

    Page(s): 2379 - 2383
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1655 KB) |  | HTML iconHTML  

    An efficient approach for face compression is introduced. Restricting a family of images to frontal facial mug shots enables us to first geometrically deform a given face into a canonical form in which the same facial features are mapped to the same spatial locations. Next, we break the image into tiles and model each image tile in a compact manner. Modeling the tile content relies on clustering the same tile location at many training images. A tree of vector-quantization dictionaries is constructed per location, and lossy compression is achieved using bit-allocation according to the significance of a tile. Repeating this modeling/coding scheme over several scales, the resulting multiscale algorithm is demonstrated to compress facial images at very low bit rates while keeping high visual qualities, outperforming JPEG-2000 performance significantly. View full abstract»

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  • On the Unequal Error Protection for Progressive Image Transmission

    Page(s): 2384 - 2388
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (418 KB) |  | HTML iconHTML  

    In this paper, we consider the unequal error protection (UEP) for progressive image transmission when the coded packet size is fixed. First, we prove that, for the source code with convex rate-distortion (R-D) function and practically used channel codes, the channel code rate for each packet in the optimal rate allocation is nondecreasing indeed. Then, we give an upper bound for the channel code rate of the last packet so that the number of rate allocations in the exhaustive search can be predicted. Further, we propose a heuristic optimization method which has low complexity and obtains performance approaching to the optimal solutions for various channel conditions and transmission rates. View full abstract»

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  • Median Filtering in Constant Time

    Page(s): 2389 - 2394
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1342 KB) |  | HTML iconHTML  

    The median filter is one of the basic building blocks in many image processing situations. However, its use has long been hampered by its algorithmic complexity O(tau) of in the kernel radius. With the trend toward larger images and proportionally larger filter kernels, the need for a more efficient median filtering algorithm becomes pressing. In this correspondence, a new, simple, yet much faster, algorithm exhibiting O(1) runtime complexity is described and analyzed. It is compared and benchmarked against previous algorithms. Extensions to higher dimensional or higher precision data and an approximation to a circular kernel are presented, as well. View full abstract»

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  • IEEE Transactions on Image Processing EDICS

    Page(s): 2395
    Save to Project icon | Request Permissions | PDF file iconPDF (24 KB)  
    Freely Available from IEEE

Aims & Scope

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

Full Aims & Scope

Meet Our Editors

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