D.L. Neuhoff

University of Michigan, Ann Arbor, Michigan, United States

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Publications (171)159.61 Total impact

  • Yuanhao Zhai · David Neuhoff
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    ABSTRACT: This paper has been submitted to IEEE Transaction on Image Processing in May 2015.
  • Yuanhao Zhai · David Neuhoff
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    ABSTRACT: The data in this file is intended for scholarly, non-commercial use only. The images cannot be re-distributed. Copyright to the data is retained by Yuanhao Zhai and David L. Neuhoff. If issues arise, please contact yhzhai@umich.edu or neuhoff@umich.edu.
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    Matthew A. Prelee · David L. Neuhoff
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    ABSTRACT: This paper introduces Manhattan sampling in two and higher dimensions, and proves sampling theorems. In two dimensions, Manhattan sampling, which takes samples densely along a Manhattan grid of lines, can be viewed as sampling on the union of two rectangular lattices, one dense horizontally, being a multiple of the fine spacing of the other. The sampling theorem shows that images bandlimited to the union of the Nyquist regions of the two rectangular lattices can be recovered from their Manhattan samples, and an efficient procedure for doing so is given. Such recovery is possible even though there is overlap among the spectral replicas induced by Manhattan sampling. In three and higher dimensions, there are many possible configurations for Manhattan sampling, each consisting of the union of special rectangular lattices called bi-step lattices. This paper identifies them, proves a sampling theorem showing that images bandlimited to the union of the Nyquist regions of the bi-step rectangular lattices are recoverable from Manhattan samples, and presents an efficient onion-peeling procedure for doing so. Furthermore, it develops a special representation for the bi-step lattices and an algebra with nice properties. It is also shown that the set of reconstructable images is maximal in the Landau sense. While most of the paper deals with continuous-space images, Manhattan sampling of discrete-space images is also considered, for infinite, as well as finite, support images.
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    ABSTRACT: The development and testing of objective texture similarity metrics that agree with human judgments of texture similarity require, in general, extensive subjective tests. The effectiveness and efficiency of such tests depend on a careful analysis of the abilities of human perception and the application requirements. The focus of this paper is on defining performance requirements and testing procedures for objective texture similarity metrics. We identify three operating domains for evaluating the performance of a similarity metric: the ability to retrieve “identical” textures; the top of the similarity scale, where a monotonic relationship between metric values and subjective scores is desired; and the ability to distinguish between perceptually similar and dissimilar textures. Each domain has different performance goals and requires different testing procedures. For the third domain, we propose ViSiProG, a new Visual Similarity by Progressive Grouping procedure for conducting subjective experiments that organizes a texture database into clusters of visually similar images. The grouping is based on visual blending and greatly simplifies labeling image pairs as similar or dissimilar. ViSiProG collects subjective data in an efficient and effective manner, so that a relatively large database of textures can be accommodated. Experimental results and comparisons with structural texture similarity metrics demonstrate both the effectiveness of the proposed subjective testing procedure and the performance of the metrics.
    Journal of the Optical Society of America A 02/2015; 32(2):329. DOI:10.1364/JOSAA.32.000329 · 1.56 Impact Factor
  • G. Jin · T.N. Pappas · D.L. Neuhoff
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    ABSTRACT: Matched-texture coding (MTC) exploits the redundancy of textured regions in natural images in order to achieve low-encoding-rate structurally lossless compression. A key element of MTC identifying large image blocks that can be replaced with previously encoded blocks that have similar structure. The side matching (SM) approach attempts to do this by matching the upper and left boundary (side) of a target block with the corresponding boundary of the candidate block, and then, among the best side matches, selecting the one that best matches the target block. We explore the effectiveness of, and interplay between, three SM criteria in order to increase the number and quality of matches and to reduce the computational complexity. The criteria are mean-squared-error, log variance ratio, and partial implementations of STSIM-2, a recently proposed structural texture similarity metric. We propose a hierarchical algorithm for side matching, with three layers that utilize the three metrics, that improves performance and reduces the computation complexity. To set thresholds for the first and second layers of the hierarchical algorithm, we rely on Bayesian hypothesis testing. To estimate the necessary local probability densities, we introduce an adaptive estimation technique that depends on the side matching search region. Experimental results demonstrate an improvement of quality for a given encoding rate over previous realizations of MTC.
  • Yuanhao Zhai · David L. Neuhoff · Thrasyvoulos N. Pappas
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    ABSTRACT: In order to provide ground truth for subjectively comparing compression methods for scenic bilevel images, as well as for judging objective similarity metrics, this paper describes the subjective similarity rating of a collection of distorted scenic bilevel images. Unlike text, line drawings, and silhouettes, scenic bilevel images contain natural scenes, e.g., landscapes and portraits. Seven scenic images were each distorted in forty-four ways, including random bit flipping, dilation, erosion and lossy compression. To produce subjective similarity ratings, the distorted images were each viewed by 77 subjects. These are then used to compare the performance of four compression algorithms and to assess how well percentage error and SmSIM work as bilevel image similarity metrics. These subjective ratings can also provide ground truth for future tests of objective bilevel image similarity metrics.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Yuanhao Zhai · David L. Neuhoff
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    ABSTRACT: This paper proposes new objective similarity metrics for scenic bilevel images, which are images containing natural scenes such as landscapes and portraits. Though percentage error is the most commonly used similarity metric for bilevel images, it is not always consistent with human perception. Based on hypotheses about human perception of bilevel images, this paper proposes new metrics that outperform percentage error in the sense of attaining significantly higher Pearson and Spearman-rank correlation coefficients with respect to subjective ratings. The new metrics include Adjusted Percentage Error, Bilevel Gradient Histogram and Connected Components Comparison. The subjective ratings come from similarity evaluations described in a companion paper. Combinations of these metrics are also proposed, which exploit their complementarity to attain even better performance.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Matthew A. Prelee · David L. Neuhoff
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    ABSTRACT: This work explores performance vs. communication energy tradeoffs in wireless sensor networks that use the recently proposed cutset deployment strategy in which sensors are placed densely along a grid of intersecting lines. For a given number of sensors, intersensor spacing is less for a cutset network than for a conventional lattice deployment, so that cutset networks require less communication energy, albeit with some potential loss in network performance. Previous work analyzed the energy-performance tradeoffs for square-grid cutset networks in the context of specific decentralized algorithms for source localization based on received signal strength (RSS). The current work also considers the RSS based source localization problem. However, it takes a more fundamental approach to analyzing the tradeoff by considering a centralized task, minimum energy communication paths, Maximum Likelihood estimation algorithms and Cramér-Rao bounds. Moreover, it analyzes triangular and honeycomb cutset deployments, in addition to square-grid ones. The results indicate that cutset networks offer sizable decreases in energy with only modest losses of performance.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Guoxin Jin · Thrasyvoulos N. Pappas · David L. Neuhoff
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    ABSTRACT: Matched-Texture Coding is a novel image coder that utilizes the self-similarity of natural images that include textures, in order to achieve structurally lossless compression. The key to a high compression ratio is replacing large image blocks with previously encoded blocks with similar structure. Adjusting the lighting of the replaced block is critical for eliminating illumination artifacts and increasing the number of matches. We propose a new adaptive lighting correction method that is based on the Poisson equation with incomplete boundary conditions. In order to fully exploit the benefits of the adaptive Poisson lighting correction, we also propose modifications of the side-matching (SM) algorithm and structural texture similarity metric. We show that the resulting matched-texture algorithm achieves better coding performance.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Matthew G Reyes · David L Neuhoff · Thrasyvoulos N Pappas
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    ABSTRACT: An effective, low complexity method for lossy compression of scenic bilevel images, called lossy cutset coding, is proposed based on a Markov random field model. It operates by losslessly encoding pixels in a square grid of lines, which is a cutset with respect to a Markov random field model, and preserves key structural information, such as borders between black and white regions. Relying on the Markov random field model, the decoder takes a MAP approach to reconstructing the interior of each grid block from the pixels on its boundary, thereby creating a piecewise smooth image that is consistent with the encoded grid pixels. The MAP rule, which reduces to finding the block interiors with fewest black-white transitions, is directly implementable for the most commonly occurring block boundaries, thereby avoiding the need for brute force or iterative solutions. Experimental results demonstrate that the new method is computationally simple, outperforms the current lossy compression technique most suited to scenic bilevel images, and provides substantially lower rates than lossless techniques, e.g., JBIG, with little loss in perceived image quality.
    IEEE Transactions on Image Processing 04/2014; 23(4):1652-1665. DOI:10.1109/TIP.2014.2302678 · 3.63 Impact Factor
  • Yuanhao Zhai · D.L. Neuhoff · T.N. Pappas
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    ABSTRACT: We develop a new type of statistical texture image feature, called a Local Radius Index (LRI), which can be used to quantify texture similarity based on human perception. Image similarity metrics based on LRI can be applied to image compression, identical texture retrieval and other related applications. LRI extracts texture features by using simple pixel value comparisons in space domain. Better performance can be achieved when LRI is combined with complementary texture features, e.g., Local Binary Patterns (LBP) and the proposed Subband Contrast Distribution. Compared with Structural Texture Similarity Metrics (STSIM), the LRI-based metrics achieve better retrieval performance with much less computation. Applied to the recently developed structurally lossless image coder, Matched Texture Coding, LRI enables similar performance while significantly accelerating the encoding.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 10/2013
  • D.L. Neuhoff · S. Sandeep Pradhan
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    ABSTRACT: This paper establishes rates attainable by several lossy schemes for coding a continuous parameter source to a specified mean-squared-error distortion based on sampling at asymptotically large rates. First, a densely sampled, spatiotemporal, stationary Gaussian source is distributively encoded. The Berger-Tung bound to the distributed rate-distortion function and three convergence theorems are used to obtain an upper bound, expressed in terms of the source spectral density, to the smallest attainable rate at asymptotically large sampling rates. The bound is tighter than that recently obtained by Kashyap Both indicate that with ideal distributed lossy coding, dense sensor networks can efficiently sense and convey a field, in contrast to the negative result obtained by Marco for encoders based on scalar quantization and Slepian-Wolf distributed lossless coding. The second scheme is transform coding with scalar coefficient quantization. A new generalized transform coding analysis, as well as the aforementioned convergence theorems, is used to find the smallest attainable rate at asymptotically large sampling rates in terms of the source spectral density and the operational rate-distortion function of the family of quantizers, which in contrast to previous analyses need not be convex. The result shows that when a transform is used, scalar quantization need not cause the poor performance found by Marco As a corollary, the final result pursues an approach, originally proposed by Berger, to show that the inverse water-pouring formula for the rate-distortion function can be attained at high sampling rates by transform coding with ideal vector quantization to encode the coefficients. Also established in the paper are relations between operational rate-distortion and distortion-rate functions for a continuous parameter source and those for the discrete parameter source that results from sampling.
    IEEE Transactions on Information Theory 09/2013; 59(9):5641-5664. DOI:10.1109/TIT.2013.2266653 · 2.33 Impact Factor
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    ABSTRACT: Texture is an important visual attribute both for human perception and image analysis systems. We review recently proposed texture similarity metrics and applications that critically depend on such metrics, with emphasis on image and video compression and content-based retrieval. Our focus is on natural textures and structural texture similarity metrics (STSIMs). We examine the relation of STSIMs to existing models of texture perception, texture analysis/synthesis, and texture segmentation. We emphasize the importance of signal characteristics and models of human perception, both for algorithm development and testing/validation.
    Proceedings of the IEEE 09/2013; 101(9):2044-2057. DOI:10.1109/JPROC.2013.2262912 · 4.93 Impact Factor
  • J Ehmann · Thrasyvoulos N. Pappas · David L. Neuhoff
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    ABSTRACT: We develop new metrics for texture similarity that account for human visual perception and the stochastic nature of textures. The metrics rely entirely on local image statistics and allow substantial point-by-point deviations between textures that according to human judgment are essentially identical. The proposed metrics extend the ideas of structural similarity (SSIM) and are guided by research in texture analysis-synthesis. They are implemented using a steerable filter decomposition and incorporate a concise set of subband statistics, computed globally or in sliding windows. We conduct systematic tests to investigate metric performance in the context of known-item search, the retrieval of textures that are identical to the query texture. This eliminates the need for cumbersome subjective tests, thus enabling comparisons with human performance on a large database. Our experimental results indicate that the proposed metrics outperform PSNR, SSIM and its variations, as well as state-of-the-art texture classification metrics, using standard statistical measures.
    IEEE Transactions on Image Processing 03/2013; 22(7). DOI:10.1109/TIP.2013.2251645 · 3.63 Impact Factor
  • M.A. Prelee · D.L. Neuhoff
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    ABSTRACT: For wireless sensor networks, many decentralized algorithms have been developed to address the problem of locating a source that emits acoustic or electromagnetic waves based on received signal strength. Among the motivations for decentralized algorithms is that they reduce the number of transmissions between sensors, thereby increasing sensor battery life. Whereas most such algorithms are designed for arbitrary sensor placements, such as random placements, this paper focuses on applications that permit a choice of sensor placement. In particular, to make communications costs small, it is proposed to place sensors uniformly along evenly spaced rows and columns, i.e., a Manhattan grid. For such a placement, the Midpoint Algorithm is proposed, which is a simple noniterative decentralized algorithm. The results of this paper show that Manhattan grid networks offer improved accuracy vs. energy tradeoff over randomly distributed networks. Results also show the proposed Midpoint Algorithm offers further energy savings over the recent POCS algorithm.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013
  • Sangsin Na · D.L. Neuhoff
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    ABSTRACT: Asymptotic formulas are derived for the mean-squared error (MSE) distortion of N-level uniform scalar quantizers designed to be MSE optimal for one density function, but applied to another, as N → ∞. These formulas, which are based on the Euler-Maclaurin formula, are then applied with generalized gamma, Bucklew-Gallagher, and Hui-Neuhoff density functions as the designed-for and applied-to densities. It is found that the mismatch between the designed-for and applied-to densities can disturb the delicate balance between granular and overload distortions in optimal quantization, with the result that, generally speaking, the granular or overload distortion dominates, respectively, depending on whether the applied-to density function has a lighter or heavier tail than the designed-for density. Specifically, in the case of generalized gamma densities, a variance mismatch makes overload distortion dominate for an applied-to source with a slightly larger variance, whereas a shape mismatch can tolerate a wider variance difference while retaining the dominance of the granular distortion. In addition, for the studied density functions, the Euler-Maclaurin approach is used to derive asymptotic formulas for the optimal quantizer step size in a simpler, more direct, way than previous approaches.
    IEEE Transactions on Information Theory 05/2012; 58(5):3169-3181. DOI:10.1109/TIT.2011.2179843 · 2.33 Impact Factor
  • Guoxin Jin · Yuanhao Zhai · T.N. Pappas · D.L. Neuhoff
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    ABSTRACT: We propose a new texture-based compression approach that relies on new texture similarity metrics and is able to exploit texture redundancies for significant compression gains without loss of visual quality, even though there may visible differences with the original image (structurally lossless). Existing techniques rely on point-by-point metrics that cannot account for the stochastic and repetitive nature of textures. The main idea is to encode selected blocks of textures - as well as smooth blocks and blocks containing boundaries between smooth and/or textured regions - by pointing to previously occurring (already encoded) blocks of similar textures, blocks that are not encoded in this way, are encoded by a baseline method, such as JPEG. Experimental results with natural images demonstrate the advantages of the proposed approach.
    Image Processing (ICIP), 2012 19th IEEE International Conference on; 01/2012
  • Shengxin Zha · T.N. Pappas · D.L. Neuhoff
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    ABSTRACT: We propose a hierarchical lossy bilevel image compression method that relies on adaptive cutset sampling (along lines of a rectangular grid with variable block size) and Markov Random Field based reconstruction. It is an efficient encoding scheme that preserves image structure by using a coarser grid in smooth areas of the image and a finer grid in areas with more detail. Experimental results demonstrate that the proposed method performs as well as or better than the fixed-grid approach, and outperforms other lossy bilevel compression methods in its rate-distortion performance.
    Image Processing (ICIP), 2012 19th IEEE International Conference on; 01/2012
  • Source
    M.A. Prelee · D.L. Neuhoff
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    ABSTRACT: This paper presents a sampling theorem for Manhattan-grid sampling, which is a sampling scheme in which data is taken along evenly spaced rows and columns. Given the spacing between the rows, the columns, and the samples along the rows and columns, the theorem shows that an image can be perfectly reconstructed from Manhattan-grid sampling if its spectrum is bandlimited to a cross-shaped region whose arm lengths and widths are determined by the aforementioned sample spacings. The nature of such cross-bandlimited images is demonstrated by filtering an image with a cross-bandlimiting filter for several choices of sampling parameters.
    Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on; 01/2012
  • M.A. Prelee · D.L. Neuhoff · T.N. Pappas
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    ABSTRACT: This paper builds upon previous work for image reconstruction problems in which samples are taken on evenly spaced rows and columns, i.e., a Manhattan grid. A new reconstruction method is proposed that uses three steps to interpolate the interior of each block under the model that an image can be decomposed into piecewise planar regions plus noise. First, the K-planes algorithm is developed in order to fit several planes to the observed pixel values on the border. Second, one of theK planes is assigned to each pixel of the block interior, by a process of partitioning the block with polygons, thereby creating a piecewise planar approximation. Third, the interior pixels are interpolated by modeling them as a Gauss Markov random field whose mean is the piecewise planar approximation just obtained. The new method is shown to improve significantly upon previous methods, especially in the preservation of “soft” image edges.
    Image Processing (ICIP), 2012 19th IEEE International Conference on; 01/2012

Publication Stats

2k Citations
159.61 Total Impact Points


  • 1985–2014
    • University of Michigan
      • • Division of Computer Science and Engineering
      • • Department of Electrical Engineering and Computer Science (EECS)
      Ann Arbor, Michigan, United States
  • 2005–2010
    • California Institute of Technology
      • Department of Electrical Engineering
      Pasadena, CA, United States
  • 2008–2009
    • Northwestern University
      • Department of Electrical Engineering and Computer Science
      Evanston, Illinois, United States
  • 1991–2007
    • Concordia University–Ann Arbor
      Ann Arbor, Michigan, United States
  • 2006
    • Queen's University
      • Department of Mathematics & Statistics
      Kingston, Ontario, Canada
  • 1998
    • Stanford University
      • Department of Electrical Engineering
      Stanford, CA, United States
  • 1997
    • Northern Illinois University
      • Department of Computer Science
      DeKalb, IL, United States
  • 1995
    • Ajou University
      Sŏul, Seoul, South Korea
  • 1991–1995
    • AT&T Labs
      Austin, Texas, United States
  • 1988–1994
    • Rutgers, The State University of New Jersey
      • Department of Electrical and Computer Engineering
      New Brunswick, NJ, United States
  • 1993
    • Rochester Institute of Technology
      Rochester, New York, United States
    • MITRE
      McLean, Virginia, United States
    • Georgia Institute of Technology
      Atlanta, Georgia, United States