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Signal Processing Letters, IEEE

Issue 10 • Date Oct. 2011

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Displaying Results 1 - 25 of 25
  • [Front cover]

    Page(s): C1
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  • IEEE Signal Processing Letters publication information

    Page(s): C2
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  • Table of contents

    Page(s): 541 - 542
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  • Hybrid Codec-Based Intra-Frame Joint Rate Control for Stereoscopic Video

    Page(s): 543 - 546
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (618 KB) |  | HTML iconHTML  

    An intra-frame joint rate control scheme is first proposed for a hybrid coder to encode stereoscopic video, which is based on an optimization framework with a gradient-based quadratic rate-quantization model and a gradient-based linear distortion-quantization model. The proposed rate control scheme jointly works on the left and right encoders for stereoscopic video input by controlling the output bit rates of both encoders in the sense that the sum of the two decoded video qualities is maximized and the quality difference is maintained around a desired level for a given target bit budget at the same time. In experiments, the proposed intra-frame joint rate control scheme for the hybrid coder produces the average 0.62 dB gain in PSNR, 64.93% reduction in the mean PSNR differences and 72.04% reduction in the MSE of PSNR difference, compared with the independent rate control schemes of the MPEG-2 TM5 and H.264 JM 16. View full abstract»

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  • Reduced-Complexity Soft-Decision Aided Space-Time Shift Keying

    Page(s): 547 - 550
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (888 KB) |  | HTML iconHTML  

    In this letter, we propose to reduce the detection complexity of soft-decision aided Space-Time Shift Keying (STSK). More explicitly, we propose a vector-by-vector based STSK detector, which exhibits a lower complexity compared to the classic block-by-block based Space-Time Modulation (STM) detector. We further operate the STSK detector on a symbol-by-symbol basis, so that a near-capacity performance may be achieved with the aid of channel coding at a reduced complexity. View full abstract»

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  • An Efficient Switching Median Filter Based on Local Outlier Factor

    Page(s): 551 - 554
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1330 KB) |  | HTML iconHTML  

    An effective algorithm for removing impulse noise from corrupted images is presented under the framework of switching median filtering. Firstly, noisy pixels are distinguished by Local Outlier Factor incorporating with Boundary Discriminative Noise Detection (LOFBDND). Then, the directional weighted median filter is adopted to remove the detected impulses by replacing each noisy pixel with the weighted mean of its neighbors in the filtering window. Our noise detection algorithm makes the decision so accurate that the miss detection rate and false detection rate are very low. Extensive simulation results show that our method provides better performance in terms of PSNR and MAE than many other median filters for impulse noise removal. View full abstract»

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  • Grassmannian Subspace Prediction for Precoded Spatial Multiplexing MIMO With Delayed Feedback

    Page(s): 555 - 558
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1282 KB) |  | HTML iconHTML  

    In unitary precoded multi-antenna systems with delayed feedback, the quantized channel state information (CSI) may become outdated before its actual use at the transmitter. In this letter, by exploiting the geometric properties of the Grassmannian manifold, we invoke the use of Grassmannian subspace predictive coding (GSPC) to overcome this problem. Under the framework of GSPC, a novel limited feedback strategy is proposed which consists of a two-stage optimization process at the receiver. The first stage quantizes the estimated CSI by accounting for the feedback delay; while the second stage optimizes the prediction on the Grassmannian manifold. View full abstract»

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  • Log-Polar Based Scheme for Revealing Duplicated Regions in Digital Images

    Page(s): 559 - 562
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (846 KB) |  | HTML iconHTML  

    Region duplication is a common type of digital image tampering. This paper presents a log-polar based approach to detect such forgery even if the copied area has been rotated and/or scaled. We compute log-polar fast Fourier transform (LPFFT) on image blocks to approximate the log-polar Fourier transform (LPFT). LPFFT is based on a nearly log-polar system where conversion to log-polar coordinates only involves 1-D Fourier transform and interpolation operations. In addition to rotation and scaling invariance, computation complexity of LPFFT is O(n2logn) , much lower than O(n4) of LPFT when n is large. Similarity of the LPFFT results between different blocks provides indication of image tampering. Experimental results show efficacy of the proposed method. View full abstract»

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  • Missing-Feature Reconstruction With a Bounded Nonlinear State-Space Model

    Page(s): 563 - 566
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (484 KB) |  | HTML iconHTML  

    Missing-feature reconstruction can improve speech recognition performance in unknown noisy environments. In this work, we examine using a nonlinear state-space model (NSSM) for missing-feature reconstruction and propose estimation with observed bounds to improve the NSSM performance. Evaluated in large-vocabulary continuous speech recognition task with babble and impulsive noise, using observed bounds in NSSM state estimation significantly improved the method performance. View full abstract»

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  • Face Recognition Based on Projected Color Space With Lighting Compensation

    Page(s): 567 - 570
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (569 KB) |  | HTML iconHTML  

    In this letter, we propose a novel color space conversion method called adaptive projection color space (APCS). This method includes two portions: adaptive singular value decomposition and an inner product conversion algorithm for color images. We employed images from the Color FERET and CMU-PIE databases for training and experiment. The results revealed that the recognition rates from our proposed APCS approach were higher than other color spaces and those of methods proposed in relevant studies. View full abstract»

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  • Modeling Rates and Distortions Based on a Mixture of Laplacian Distributions for Inter-Predicted Residues in Quadtree Coding of HEVC

    Page(s): 571 - 574
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1082 KB) |  | HTML iconHTML  

    Inprobability model based rate control of video coding, modeling of residual distribution is important in predicting precise distortions so as to determine appropriate quantization parameter values. For this, single probability model approaches have been popularly taken which may fail to model the underlying statistical characteristics of different residues from variable block-sized coding. In this letter, new rate and distortion models based on a mixture of multiple Laplacian distributions are presented for the transform coefficients of inter-predicted residues in quadtree coding. The proposed mixture model of multiple Laplacian distributions is tested for the High Efficiency Video Coding (HEVC) Test Model (HM) with quadtree-structured Coding Unit and Transform Unit. The experimental results show that the proposed model achieves more accurate results of rate and distortion estimation than the single probability models. View full abstract»

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  • A New Approach for Optimal Multiple Watermarks Injection

    Page(s): 575 - 578
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1333 KB) |  | HTML iconHTML  

    The digital imaging technology has grown explosively for multimedia applications in recent years. The need for the copyrighted digitalized media becomes urgent nowadays. An approach for the digital copyright protection is to employ advanced watermarking techniques, where watermarks can reveal the ownership identities. Generally speaking, the watermarks are embedded into image or video signals. In this paper, we will investigate digital watermarking techniques and propose a new optimal watermarking scheme. When multiple embedded watermarks are considered, a new analysis for the signal-to-interference-plus-noise-ratios (SINRs) with respect to the subject signal and the watermark signals is carried out. The objective quality measure for the digital watermarking applications should essentially consist of both signal-to-interference-plus-noise-ratio for the subject signal and similarity coefficients for the watermarks. In order to optimize the aforementioned objective measure, we design a novel efficient scale-factor optimization scheme, which can lead to the maximum overall SINR for both subject signal and watermarks. Simulation results are also demonstrated to illustrate the effectiveness of our proposed method. View full abstract»

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  • A DCT Approximation for Image Compression

    Page(s): 579 - 582
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (972 KB) |  | HTML iconHTML  

    An orthogonal approximation for the 8-point discrete cosine transform (DCT) is introduced. The proposed transformation matrix contains only zeros and ones; multiplications and bit-shift operations are absent. Close spectral behavior relative to the DCT was adopted as design criterion. The proposed algorithm is superior to the signed discrete cosine transform. It could also outperform state-of-the-art algorithms in low and high image compression scenarios, exhibiting at the same time a comparable computational complexity. View full abstract»

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  • Vector Sampling Expansions and Linear Canonical Transform

    Page(s): 583 - 586
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (863 KB) |  | HTML iconHTML  

    In a vector sampling expansion (VSE), N signals bandlimited to W rad/s in the conventional Fourier domain (CFD) pass through multi-input multi-output (MIMO) linear time-invariant (LTI) systems and produce M ≥ N output bandlimited signals in the CFD. In the uniformly sampled VSE, the outputs of all the M systems are uniformly sampled at the same rate while preserving the total number of samples per second equal to NW/π. In the literature, a necessary condition for perfect signal reconstruction in such a uniformly sampled VSE is discussed which requires M/N to be an integer. It is the purpose of this paper to demonstrate that use of linear canonical transform (LCT) in uniformly sampled VSE allows perfect signal reconstruction even when M/N is not an integer. View full abstract»

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  • Compressed LED Illumination Sensing

    Page(s): 587 - 590
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (754 KB) |  | HTML iconHTML  

    Modern day light emitting diodes (LEDs) are capable of producing high intensity light across a wide spread of frequencies. Hence, they are becoming a common ingredient in many lighting systems. In order to obtain desired lighting effects efficiently, it is important to sense the light received across different target locations and estimate the unknown properties (amplitudes, frequency offsets and phases) of the modulating signals. This facilitates the design of the driving waveforms for the LEDs. This procedure is known as illumination sensing and it enables efficient and effective usage of light energy to achieve the intended effects. We propose a novel two step approach to perform this estimation using sparse modeling which exploits the fact that the measurements at the sensors are sparse in the frequency offset space and the phase space. Further, we employ compressive sensing to reduce the dimensions of the measurement vector, thereby reducing the complexity of the estimation algorithm. This will enable quick estimation which is essential to avoid any lag in attaining the desired illumination effects. Also, we demonstrate the performance of the proposed approach in the presence of a modeling mismatch. View full abstract»

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  • A New Feedforward Hybrid Active Noise Control System

    Page(s): 591 - 594
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (810 KB) |  | HTML iconHTML  

    Performance of the conventional broadband active noise control (ANC) system may degrade severely if its primary and reference noise signals contain both wideband and narrowband components simultaneously. In this letter, we propose a new feedforward hybrid ANC system capable of reducing such primary noise signals. First, typical simulation results are provided to show the performance deterioration of the conventional system in the presence of mixture of wideband and narrowband components. Next, a new hybrid ANC system is proposed to tackle the problem. The new system consists of three subsystems, i.e., a sinusoidal noise canceller, a broadband and a narrowband ANC subsystem, which work in harmony. Extensive simulations are conducted to demonstrate the effectiveness of the proposed system. View full abstract»

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  • On Variable Density Compressive Sampling

    Page(s): 595 - 598
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1161 KB) |  | HTML iconHTML  

    Incoherence between sparsity basis and sensing basis is an essential concept for compressive sampling. In this context, we advocate a coherence-driven optimization procedure for variable density sampling. The associated minimization problem is solved by use of convex optimization algorithms. We also propose a refinement of our technique when prior information is available on the signal support in the sparsity basis. The effectiveness of the method is confirmed by numerical experiments. Our results also provide a theoretical underpinning to state-of-the-art variable density Fourier sampling procedures used in MRI. View full abstract»

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  • Gaussian Specific Compensation for Channel Distortion in Speech Recognition

    Page(s): 599 - 602
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (765 KB) |  | HTML iconHTML  

    Channel distortion is one of the major factors degrading the performance of automatic speech recognition (ASR) systems. Most of the current compensation methods rely on the assumption that the channel distortion remains unchanged within an utterance or globally. However, we show in this letter that the distortion varies over speech frames even if the channel response is unchanged. To address this problem, we relax the above-mentioned assumption and propose a new method to compensate the channel distortion for each Gaussian of the acoustic models. Firstly, we derive the relationship between the clean and distorted models, and then estimate the channel magnitude response with the expectation-maximization (EM) algorithm. Finally, we obtain the matched models with the estimated magnitude response and the clean models. Experiments were conducted on the TIMIT/NTIMIT databases and the results confirmed the effectiveness of the proposed method. View full abstract»

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  • Unsharp Masking Sharpening Detection via Overshoot Artifacts Analysis

    Page(s): 603 - 606
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (687 KB) |  | HTML iconHTML  

    In this letter, we propose a new method in detecting unsharp masking (USM) sharpening operation in digital images. Overshoot artifacts are found to occur around side-planar edges in the sharpened images. Such artifacts, measured by a sharpening detector, can serve as a rather unique feature for identifying the previous performance of sharpening operation. Test results on photograph images with regard to various sharpening operators show the effectiveness of our proposed method. View full abstract»

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  • Hourly Traffic Forecasts Using Interacting Multiple Model (IMM) Predictor

    Page(s): 607 - 610
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1021 KB) |  | HTML iconHTML  

    Accurate and timely forecasting of traffic status is crucial to effective management of intelligent transportation systems (ITS). An interacting multiple model (IMM) predictor is proposed to forecast travel time index (TTI) data in the letter. To the best of our knowledge, it is the first time to propose the novel combined predictor. Seven baseline individual predictors are selected as combination components because of their proved effectiveness. Experimental results demonstrate that the IMM predictor can significantly outperform the other predictors and provide a large improvement in stability and robustness. This reveals that the approach is practically promising in traffic forecasting. View full abstract»

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  • Resolution Improvement of Infrared Images Using Visible Image Information

    Page(s): 611 - 614
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1742 KB) |  | HTML iconHTML  

    This letter presents a new framework for improving the spatial resolution of infrared (IR) images based on the high-resolution visible image information. Edge regions in an IR image, which have a strong correlation with the aligned edges in the visible image, are sharpened by using their high frequency patches, which are locally estimated from the visible image. The estimation is performed on the basis of intensity correlations between two images. In addition, in order to improve the resolution in the uncorrelated edge regions and the texture regions where visible image information is not available, we adopt learning-based and reconstruction-based super resolution algorithms, respectively. Experimental results demonstrate that the proposed algorithm improves the spatial resolution compared with the existing upsampling algorithms. View full abstract»

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  • Blank page

    Page(s): 615 - 616
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  • IEEE Signal Processing Letters Information for authors

    Page(s): 617 - 618
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  • IEEE Signal Processing Society Information

    Page(s): C3
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  • Blank page [back cover]

    Page(s): C4
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Aims & Scope

The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

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Editor-in-Chief
Peter Willett
University of Connecticut
Storrs, CT 06269
peter.willett@uconn.edu