nils-herrmann/clean_scirepeval huggingface dataset:
Reducing Blocking Artifacts Within Vector Quantization Algorithms

Vector quantization (VQ) provides a fast, low-power image compression technique for video communication products. However, since VQ techniques operate on each block of the image separately, they have been criticized for producing visually-disturbing blocking artifacts, especially at low bit rates. We present a VQ technique which uses hexagonally-shaped vectors to greatly reduce blocking artifacts. The effectiveness of the technique is demonstrated experimentally.
A general formulation of constrained iterative restoration algorithms

This paper introduces a general formulation of constrained iterative restoration algorithms in which deterministic and/or statistical information about the undistorted signal and statistical information about the noise are directly incorporated into the iterative procedure. This a priori information is incorporated into the restoration algorithm by what we call "soft" or statistical constraints. Their effect on the solution depends on the amount of noise on the data; that is, the constraint operator is "turned off" for noiseless data. The development of the new iterative algorithm is based on results from regularization techniques for stabilizing ill-posed problems.
A rate distortion optimal ECG coding algorithm

Signal compression is an important problem encountered in many applications. Various techniques have been proposed over the years for addressing the problem. Here, the authors present a time domain algorithm based on the coding of line segments which are used to approximate the signal. These segments are fit in a way that is optimal in the rate distortion sense. Although the approach is applicable to any type of signal, the authors focus, in this paper, on the compression of electrocardiogram (ECG) signals. ECG signal compression has traditionally been tackled by heuristic approaches. However, it has been demonstrated (R. Nygaard and D. Haugland,  Island of Rhodes, Greece, p. 2473-6, 1998) that exact optimization algorithms outperform these heuristic approaches by a wide margin with respect to reconstruction error. By formulating the compression problem as a graph theory problem, known optimization theory can be applied in order to yield optimal compression. In this paper, the authors present an algorithm that will guarantee the smallest possible distortion among all methods applying linear interpolation given an upper bound on the available number of bits. Using a varied signal test set, extensive coding experiments are presented. The authors compare the results from their coding method to traditional time domain ECG compression methods, as well as, to more recently developed frequency domain methods. Evaluation is based both on percentage root-mean-square difference (PRD) performance measure and visual inspection of the reconstructed signals. The results demonstrate that the exact optimization methods have superior performance compared to both traditional ECG compression methods and the frequency domain methods.
Lossless compression of images using logic minimization

A novel approach for the lossless compression of images is presented. After preprocessing, the image is split into bit planes which are then divided into smaller blocks. Mixed blocks are converted to Boolean switching functions and then subjected to minimization to arrive at a compact representation, leading to possible data compression. Blocks are then classified into distinct events based on the outcome of logic minimization. A Huffman code is constructed and the blocks are encoded suitably. This approach provides a framework to incorporate various possible switching theoretic techniques into the basic coding scheme proposed by us. This paper also shows that logic minimization can be used to handle blocks of larger size, than practically possible with Huffman coding, to yield significant compression on gray-level images. Our approach compares well with JPEG in terms of compression ratio.
Source fidelity over a two-hop fading channel

This paper studies the transmission of a continuous source over a two-hop fading channel. The source data traverses at least one intermediate node in order to reach the destination node. We assume a Gaussian source with Rayleigh fading links, and model the effect of each link through its outage capacity. We start with a single link, and optimize the transmission rate to minimize the received distortion. This minimum distortion is characterized as a function of transmission time, average channel gain, and transmitted power. We then consider a two-hop channel and optimize the transmission rates and transmission times over the two links. Two cases are compared: (i) The relay is allowed to optimize the transmission rate over the second link; and (ii) The relay retransmits the received bits over the second link at the same rate. Numerical examples show that both types of relays perform similarly over a wide parameter range of interest. We characterize the minimum achievable distortion in each case when the ratio between channel gains becomes large, and show that the former (smart) relay gives a performance gain of at most three dB.
Spatially adaptive wavelet-based multiscale image restoration

In this paper, we present a new spatially adaptive approach to the restoration of noisy blurred images, which is particularly effective at producing sharp deconvolution while suppressing the noise in the flat regions of an image. This is accomplished through a multiscale Kalman smoothing filter applied to a prefiltered observed image in the discrete, separable, 2-D wavelet domain. The prefiltering step involves constrained least-squares filtering based on optimal choices for the regularization parameter. This leads to a reduction in the support of the required state vectors of the multiscale restoration filter in the wavelet domain and improvement in the computational efficiency of the multiscale filter. The proposed method has the benefit that the majority of the regularization, or noise suppression, of the restoration is accomplished by the efficient multiscale filtering of wavelet detail coefficients ordered on quadtrees. Not only does this lead to potential parallel implementation schemes, but it permits adaptivity to the local edge information in the image. In particular, this method changes filter parameters depending on scale, local signal-to-noise ratio (SNR), and orientation. Because the wavelet detail coefficients are a manifestation of the multiscale edge information in an image, this algorithm may be viewed as an "edge-adaptive" multiscale restoration approach.
Parameter estimation in Bayesian high-resolution image reconstruction with multisensors

In this paper, we consider the estimation of the unknown parameters for the problem of reconstructing a high-resolution image from multiple undersampled, shifted, degraded frames with subpixel displacement errors. We derive mathematical expressions for the iterative calculation of the maximum likelihood estimate of the unknown parameters given the low resolution observed images. These iterative procedures require the manipulation of block-semi circulant (BSC) matrices, that is, block matrices with circulant blocks. We show how these BSC matrices can be easily manipulated in order to calculate the unknown parameters. Finally the proposed method is tested on real and synthetic images.
A regularized iterative image restoration algorithm

The development of the algorithm is based on a set theoretic approach to regularization. Deterministic and/or statistical information about the undistorted image and statistical information about the noise are directly incorporated into the iterative procedure. The restored image is the center of an ellipsoid bounding the intersection of two ellipsoids. The proposed algorithm, which has the constrained least squares algorithm as a special case, is extended into an adaptive iterative restoration algorithm. The spatial adaptivity is introduced to incorporate properties of the human visual system. Convergence of the proposed iterative algorithms is established. For the experimental results which are shown, the adaptively restored images have better quality than the nonadaptively restored ones based on visual observations and on an objective criterion of merit which accounts for the noise masking property of the visual system.
ECG-Based Classification of Resuscitation Cardiac Rhythms for Retrospective Data Analysis

There is a need to monitor the heart rhythm in resuscitation to improve treatment quality. Resuscitation rhythms are categorized into: ventricular tachycardia (VT), ventricular fibrillation (VF), pulseless electrical activity (PEA), asystole (AS), and pulse-generating rhythm (PR). Manual annotation of rhythms is time-consuming and infeasible for large datasets. Our objective was to develop ECG-based algorithms for the retrospective and automatic classification of resuscitation cardiac rhythms.  The dataset consisted of 1631 3-s ECG segments with clinical rhythm annotations, obtained from 298 out-of-hospital cardiac arrest patients. In total, 47 wavelet- and time-domain-based features were computed from the ECG. Features were selected using a wrapper-based feature selection architecture. Classifiers based on Bayesian decision theory, k-nearest neighbor, k-local hyperplane distance nearest neighbor, artificial neural network (ANN), and ensemble of decision trees were studied.  The best results were obtained for ANN classifier with Bayesian regularization backpropagation training algorithm with 14 features, which forms the proposed algorithm. The overall accuracy for the proposed algorithm was 78.5%. The sensitivities (and positive-predictive-values) for AS, PEA, PR, VF, and VT were 88.7% (91.0%), 68.9% (70.4%), 65.9% (69.0%), 86.2% (83.8%), and 78.8% (72.9%), respectively.  The results demonstrate that it is possible to classify resuscitation cardiac rhythms automatically, but the accuracy for the organized rhythms (PEA and PR) is low.  We have made an important step toward making classification of resuscitation rhythms more efficient in the sense of minimal feedback from human experts.
A VQ-based blind image restoration algorithm

In this paper, learning-based algorithms for image restoration and blind image restoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.
Detection of atrial fibrillation in ECG hand-held devices using a random forest classifier

Atrial Fibrillation (AF) is characterized by chaotic electrical impulses in the atria, which leads to irregular heartbeats and can develop blood clots and stroke. Therefore, early detection of AF is crucial for increasing the success rate of the treatment. This study is focused on detection of AF rhythm using hand-held ECG monitoring devices, in addition to three other classes: normal or sinus rhythm, other rhythms, and too noisy to analyze. The pipeline of the proposed method consists of three major components: preprocessing and feature extraction, feature selection, and classification. In total, 491 hand-crafted features are extracted. Then, 150 features are selected in a feature ranking procedure. The selected features are from time, frequency, time-frequency domains, and phase space reconstruction of the ECG signals. In the final stage, a random forest classifier is used to classify the selected features into one of the four aforementioned ECG classes. Using the scoring mechanism provided by PhysioNet/Computing in Cardiology (CinC) Challenge 2017, the overall score (mean±std) of 81.9±2.6% is achieved over the training dataset in 10-fold cross-validation. The proposed algorithm tied for the first place in the PhysioNet/CinC Challenge 2017 with an overall score of 82.6% (rounded to 83%) on the unseen test dataset.
Cross-validation And Maximum Likelihood Space Varying Regularized Image Restoration

In a number of image processing problems, such as image restoration problems, ill-conditioned equations must be solved [l]. Because of the presence of singularities and noise in the data, regularization is an effective method for obtaining a satisfactory solutions to this problem . The application of regularizing necessitates a choice of the regularization parameter. This parameter trades fidelity to the data with smoothness of the solution. For most problems of interest the choice of the regularizing parameter is not known a priori and has to be computed. For an M x M image and linear shift invariant degradation with white additive noise, the imaging equation is given by g = H f + n , (1) where the vectors g, f, and n are M 2 x 1 lexicographic of the degraded observed image, the original image and the noise, respectively. The M 2 x M 2 degradation matrix H is Block Toeplitz and ill-conditioned [l]. The regularized estimate f h a t for this problem is given by f̂ = ( H t H + XQtQ)-lHtg = A(X)g, (2) where Q is the regularizing functional and X the regularizing parameter. A number of approaches have been suggested to compute the regularizing parameter. These approaches can be classified into two broad categories ones requiring a priori knowledge of the noise variance such as the constrained least squares method [l] and the set theoretic approach  and ones that do not such as the cross-validation (CV)  and the maximum likelihood (ML) approach . For the CV approach the regularizing parameter is obtained by minimizing the cross-validation function given by and for the ML approach by minimizing (3)
A novel image retrieval framework exploring inter cluster distance

An image could be described with local features like SIFT and with those features, images could be represented as “Bag-of-Visual-Words” (BVW). This representation has been widely used in content based image retrieval. Comparing BVW of two images is usually done in Euclidean space, like Euclidean distance or weighted variants. Neither of these methods consider the inter cluster relations. If there is a feature in one image without any match in all the clusters of another image's features, there will be no score for that feature. But, there are still some match in neighbor clusters. In this paper, we use dynamic programming to calculate full inter cluster distance map and with the distance, we can evaluate a feature in neighbor clusters. Our proposed method is evaluated in Caltech 101 database and experiments show that our method generally exceeds the method that don't consider inter cluster distance.
Regularized constrained total least squares image restoration

In this paper, the problem of restoring an image distorted by a linear space-invariant (LSI) point-spread function (PSF) that is not exactly known is formulated as the solution of a perturbed set of linear equations. The regularized constrained total least-squares (RCTLS) method is used to solve this set of equations. Using the diagonalization properties of the discrete Fourier transform (DFT) for circulant matrices, the RCTLS estimate is computed in the DFT domain. This significantly reduces the computational cost of this approach and makes its implementation possible even for large images. An error analysis of the RCTLS estimate, based on the mean-squared-error (MSE) criterion, is performed to verify its superiority over the constrained total least-squares (CTLS) estimate. Numerical experiments for different errors in the PSF are performed to test the RCTLS estimator. Objective and visual comparisons are presented with the linear minimum mean-squared-error (LMMSE) and the regularized least-squares (RLS) estimator. Our experiments show that the RCTLS estimator reduces significantly ringing artifacts around edges as compared to the two other approaches.
Total least squares estimation of stereo optical flow

We propose a new method for disparity assisted stereo optical flow estimation. This method is based on the linearization of the round-about compatibility constraint, which converts a stereo optical flow estimation problem to a single channel optical flow estimation problem. An over-determined system of optical flow equations can then be constructed for estimating the flow fields. The total least squares, instead of the traditional least squares is used to solve the estimation problem. We also investigate the extension of the locally constant flow model across the time domain. The experiments presented demonstrate that the proposed technique performs very well.
Resource Allocation for Downlink Multiuser Video Transmission Over Wireless Lossy Networks

The emergence of 3G and 4G wireless networks brings with it the possibility of streaming high quality video content on-demand to mobile users. Wireless video applications require appropriate scheduling techniques that make use of the specific characteristics of video content, as well as the well known gains from multiuser diversity. While fast and frequent channel feedback is available in the new generation of wireless networks, the channel estimates cannot be perfect, and channel losses should be taken into account in the packet scheduling and resource allocation. The proposed scheme is formulated as a joint optimization over the resource allocation and channel loss protection, in order to minimize the distortion of the received video sequences. The distortion is a function of the packets deliberately dropped at the transmission queue due to congestion, as well as of random channel losses. The scheme makes use of a packet prioritization strategy that orders video packets based on their contribution to reducing the expected distortion of the received video sequence. Simulation results show that the proposed technique significantly outperforms content-independent packet scheduling schemes.
Bayesian super-resolution of text image sequences from low resolution observations

This paper deals with the problem of reconstructing high-resolution text images from an incomplete set of under-sampled, blurred, and noisy images shifted with subpixel displacement. We derive mathematical expressions for the calculation of the maximum a posteriori estimate of the high resolution image and the estimation of the parameters involved in the model. The method is tested on real text images and car plates, examining the impact of blurring and the number of available low resolution images on the final estimate.
Sampling optimization for on-chip compressive video

In this paper, we consider the problem of on-chip temporal compressive sensing for video reconstruction at high frame-rates without the need of any additional optical components. We devise an optimization scheme in order to achieve adequate spatio-temporal sampling of subsequent frames under maximal capturing speed, based on the bandwidth constraints of a sensor. We test this optimization strategy on a commercially available camera and propose a set of reconstruction steps that can achieve reasonable performance but, at the same time, accommodate high-resolution video reconstruction under realistic time requirements. Our analysis constitutes a set of first steps bringing high-speed compressive video capture within the realm of commercial availability.
Inter mode vertex-based optimal shape coding

This paper investigates the problem of optimal lossy encoding of object contours in the inter mode. Contours are approximated by connected second-order spline segments, each defined by three consecutive control points. Taking into account correlations in the temporal direction, control points are chosen optimally in the rate-distortion sense. Applying motion to contours in the reference frame followed by the temporal context extraction, we predict the next control point location, given the previously encoded one. Based on the chosen differential encoding scheme and an additive MPEG-4 based distortion metric, the problem is formulated as Lagrangian minimization. We utilize an iterative procedure to jointly find the optimal solution and the associated DPCM parameter probability mass functions.
DNA-based matching of digital signals

Adleman with his pioneering work set the stage for the new field of bio-computing research (Science, vol.266, p.1021-1024, 1994). His main idea was to use actual chemistry to solve problems that are either unsolvable by conventional computers, or require an enormous amount of computation. The main focus of our research is to consider the application of molecular computing to the domain of digital signal processing (DSP). In this paper, we consider matching problems that arise in signal processing applications and are amenable to a DNA-based solution. Digital data are encoded in DNA sequences using a sophisticated codeword set that satisfies the noise tolerance constraint (NTC) that we introduce. NTC, one of the main contributions of our work, takes into account the presence of noise in digital signals by exploiting the annealing between non-perfect complementary sequences. We propose an algorithm to map binary values into DNA codewords by satisfying a number of constraints, including the NTC. Using that algorithm, we retrieved 128 codewords that enables us to use a DNA based approach to digital signal matching.
A dynamic programming solution to tracking and elastically matching left ventricular walls in cardiac cine MRI

In this paper an algorithm to detect and elastically match the contours of the epicardial walls of the left ventricle (LV) in cardiac phase-resolved 2-D magnetic resonance (MR) images is presented. For both tasks, dynamic programming (DP) is used. A mask conforming to the six segment model of the LV is fitted on a reference image and propagated utilizing the elastic matching information. At its present form the algorithm requires minimal parameter corrections among different sets of cine MRI images. Future extensions include comparisons with contours hand labeled by imaging experts.
Quality-Driven Optimization for Content-Aware Real-Time Video Streaming in Wireless Mesh Networks

Video transport over multi-hop wireless networks has received significant research interests recently. The majority of the research efforts in this field have been conducted taking the approach of cross-layer optimization. However, video content and user perceived quality have been largely ignored in existing work. In this paper, we integrate video content analysis into video transport over wireless mesh networks (WMN). A content-aware quality-driven cross-layer optimization framework is proposed to achieve the best end-to-end user perceived video quality. In our framework, the extracted video regions of interest (ROI) are discriminatingly coded, transmitted and protected in video encoding, network routing and packet scheduling by different network layers. We aim at the optimization of key parameters of each layer while focusing on their interactions across the holistic network protocol stack. The proposed framework is evaluated by H.264/AVC codec and WMN simulations. Experimental results demonstrate that the proposed framework can effectively provide a good user perceived video quality, especially when the delay requirement is stringent.