nils-herrmann/clean_scirepeval huggingface dataset:
A class of iterative signal restoration algorithms

A class of iterative signal restoration algorithms is derived based on a representation theorem for the generalized inverse of a matrix. These algorithms exhibit a first or higher order of convergence, and some of them consist of an online and an offline computational part. The conditions for convergence, the rate of convergence of these algorithms, and the computational load required to achieve the same restoration results are derived. An iterative algorithm is also presented which exhibits a higher rate of convergence than the standard quadratic algorithm with no extra computational load. These algorithms can be applied to the restoration of signals of any dimensionality. The presented approach unifies a large number of iterative restoration algorithms. Based on the convergence properties of these algorithms, combined algorithms are proposed that incorporate a priori knowledge about the solution in the form of constraints and converge faster than previously published algorithms.
A VQ-based image restoration algorithm

In this paper, we develop a novel VQ-based image restoration algorithm. The mapping between high frequency information in the original images and low frequency information in the corresponding degraded ones is established and stored in the VQ codebooks. Prototype images are used, which belong to the same class of images. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the designed codebook. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighborhood approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.
Video compressive sensing using multiple measurement vectors

Compressive Sensing (CS) suggests that, under certain conditions, a signal can be reconstructed using a small number of incoherent measurements. We propose a novel video CS framework based on Multiple Measurement Vectors (MMV) which is suitable for signals with temporal correlation such as video sequences. In addition, a CS circulant matrix is employed for fast reconstruction. Furthermore, the proposed framework allows the number of CS measurements associated with each frame to be chosen in the decoder rather than the encoder offering robustness compared to the multi-scale approaches. Experimental results on two video sequences exhibiting fast motion and occlusions, show the advantages of the proposed method over the current state-of-the-art in video CS.
Regularized blur-assisted displacement field estimation

Due to the finite acquisition time of practical cameras, objects can move during image acquisition, therefore introducing motion blur degradations. Traditionally, these degradations are treated as undesirable artifacts that should be removed before further processing. We consider the use of motion blur as an indication of scene motion. We present two robust regularized motion estimation algorithms that consider the use of (motion) blur in their formulation. The first algorithm uses motion blur as prior knowledge for the estimation of the motion field. The second algorithm considers the joint estimation of the motion and motion blur. Each approach results in a motion blur point spread field, a motion field and a restored image in an approach that is different from previous work. Preliminary results are presented.
Enhancement of compressed video using visual quality measurements

The enhancement of compressed video is considered. We present a general algorithm for processing the compressed data, with three variants of the algorithm having practical application. We then consider the algorithm within the context of MPEG-2. Assuming complete knowledge of the compressed bitstream, experiments compare the different realizations of the enhancement algorithm. Our comparisons stress improvements in visual quality, measured by models of the human visual system. Quantitative and qualitative results are provided.
Simultaneous recursive displacement estimation and restoration of noisy-blurred image sequences

We develop a recursive model-based maximum a posteriori (MAP) estimator that simultaneously estimates the displacement vector field (DVF) and the intensity field from a noisy-blurred image sequence. Current motion-compensated spatio-temporal noise filters treat the estimation of the DVF as a preprocessing step. Generally, no attempt is made to verify the accuracy of these estimates prior to their use in the filter. By simultaneously estimating these two fields, we establish a link between the two estimators. It is through this link that the DVF estimate and its corresponding accuracy information are shared with the other intensity estimator, and vice versa. To model the DVF and the intensity field, we use coupled Gauss-Markov (CGM) models. A CGM model consists of two levels: an upper level, which is made up of several submodels with various characteristics, and a lower level or line field, which governs the transitions between the submodels. The CGM models are well suited for estimating the displacement and intensity fields since the resulting estimates preserve the boundaries between the stationary areas present in both fields. Detailed line fields are proposed for the modeling of these boundaries, which also take into account the correlations that exist between these two fields. A Kalman-type estimator results, followed by a decision criterion for choosing the appropriate set of line fields. Several experiments using noisy and noisy-blurred image sequences demonstrate the superior performance of the proposed algorithm with respect to prediction error and mean-square error.
Shape coding using temporal correlation and joint VLC optimization

This paper investigates ways to explore the between frame correlation of shape information within the framework of an operationally rate-distortion (ORD) optimized coder. Contours are approximated both by connected second-order spline segments, each defined by three consecutive control points, and by segments of the motion-compensated reference contours. Consecutive control points are then encoded predictively using angle and run temporal contexts or by tracking the reference contour. We utilize a novel criterion for selecting global object motion vectors, which improves the efficiency. The problem is formulated as a Lagrangian minimization and solved using dynamic programming. Furthermore, we employ an iterative technique to remove dependency on a particular variable length code and jointly arrive at the ORD globally optimal solution and an optimized conditional parameter distribution.
Binocular video object tracking with fast disparity estimation

This paper presents a binocular PTU (pan-tilt unit) camera video object tracking scheme using the MeanShift algorithm and the runtime disparity estimation. The proposed method is to accommodate the requirement of 3D content generation and accurate tracking in more advanced video surveillance applications. The disparity estimation process for each stereoscopic pair is formulated as an energy minimization problem. The iterative solution procedure is implemented in a course-to-fine manner. The estimated disparity is used to scale the tracking window by the MeanShift algorithm, i.e. the size of the tracking area is adjustable according to its inner disparity, and thus the moving object can be better located by the camera. The program maintains the semi-real-time performance and acceptable accuracy as evaluated on a set of standard test data. In our experiment, two PointGrey cameras are controlled through a PTU device. The disparity estimation process on the recorded tracking video (640×480) achieves 6fps on an ordinary PC (2.66GHz CPU, 4GB RAM).
Comparison of MPEG-4 facial animation parameter groups with respect to audio-visual speech recognition performance

In this paper, we describe an audio-visual automatic speech recognition (AV-ASR) system that utilizes facial animation parameters (FAPs), supported by the MPEG-4 standard, for the visual representation of speech. We describe the visual feature extraction algorithms used for extracting FAPs, which control outer- and inner-lip movement. Principal component analysis (PCA) is performed on both inner- and outer-lip FAP vector in order to decrease their dimensionality and decorrelate them. The PCA-based projection weights of the extracted FAP vectors are used as visual features. Multi-stream hidden Markov models (HMMs) and a late integration approach are used to integrate audio and visual information and train a continuous AV-ASR system. We compare the performance of the developed AV-ASR system utilizing outer- and inner lip FAPs, individually and jointly. Experiments were performed for different dimensionalities of the visual features, at various SNRs (0-30dB) with additive white Gaussian noise, on a relatively large vocabulary (approximately 1000 words) database. The proposed system reduces the word error rate (WER) by 20% to 23% relatively to audio-only ASR WERs. Conclusions are drawn on the individual and combined effectiveness of the inner- and outer-lip FAPs, the trade off between the dimensionality of the visual features and the amount of speechreading information contained in them and its influence on the AV-ASR performance.
An integrated joint source-channel coding framework for video transmission over packet lossy networks

The problem of application-layer error control for real-time video transmission over packet lossy networks is commonly addressed by joint source-channel coding (JSCC). The traditional JSCC approaches solve this problem in a sequential manner, where source coding and channel coding are not fully integrated. In this paper, we present an integrated joint source-channel coding (IJSCC) framework, where error resilient source coding, channel coding and error concealment are jointly considered in an integrated manner. We show through both analysis and simulations the advantages of the proposed IJSCC approach, in comparison to a sequential JSCC approach.
Distortion Estimation Using Structural Similarity for Video Transmission over Wireless Networks

Efficient streaming of video over wireless networks requires real-time assessment of distortion due to packet loss, especially because predictive coding at the encoder can cause inter-frame propagation of errors and impact the overall quality of the transmitted video. This paper presents an algorithm to evaluate the expected receiver distortion on the source side by utilizing encoder information, transmission channel characteristics and error concealment. Specifically, distinct video transmission units, Group of Blocks (GOBs), are iteratively built at the source by taking into account macroblock coding modes and motion-compensated error concealment for three different combinations of packet loss. Distortion of these units is then calculated using the structural similarity (SSIM) metric and they are stochastically combined to derive the overall expected distortion. The proposed model provides a more accurate estimate of the distortion that closely models quality as perceived through the human visual system. When incorporated into a content-aware utility function, preliminary experimental results show improved packet ordering & scheduling efficiency and overall video signal at the receiver.
Source fidelity over fading channels: performance of erasure and scalable codes

We consider the transmission of a Gaussian source through a block fading channel. Assuming each block is decoded independently, the received distortion depends on the tradeoff between quantization accuracy and probability of outage. Namely, higher quantization accuracy requires a higher channel code rate, which increases the probability of outage. We first treat an outage as an erasure, and evaluate the received mean distortion with erasure coding across blocks as a function of the code length. We then evaluate the performance of scalable, or multi-resolution coding in which coded layers are superimposed within a coherence block, and the layers are sequentially decoded. Both the rate and power allocated to each layer are optimized. In addition to analyzing the performance with a finite number of layers, we evaluate the mean distortion at high signal-to-noise ratios as the number of layers becomes infinite. As the block length of the erasure code increases to infinity, the received distortion converges to a deterministic limit, which is less than the mean distortion with an infinite-layer scalable coding scheme. However, for the same standard deviation in received distortion, infinite layer scalable coding performs slightly better than erasure coding, and with much less decoding delay.
Joint source coding and transmission power management for energy efficient wireless video communications

We consider a situation where a video sequence is to be compressed and transmitted over a wireless channel. Our goal is to limit the amount of distortion in the received video sequence, while minimizing transmission energy. To accomplish this goal, we consider error resilience and concealment techniques at the source coding level, and transmission power management at the physical layer. We jointly consider these approaches in a novel framework. In this setting, we formulate and solve an optimization problem that corresponds to minimizing the energy required to transmit video under distortion and delay constraints. Experimental results show that simultaneously adjusting the source coding and transmission power is more energy efficient than considering these factors separately.
A unified approach to iterative signal restoration

A class of iterative image restoration algorithms is derived. The algorithms are based on a representation theorem for the generalized inverse of a matrix. These algorithms exhibit a first or higher order of convergence and some of them consist of an online and an offline computation part. The conditions for convergence and the range of convergence of these algorithms are derived. An iterative algorithm is also presented which exhibits a higher rate of convergence than the standard quadratic algorithm with no extra computational load. These algorithms can be applied to the restoration of signals of any dimensionality. Iterative restoration algorithms that have appeared in the literature represent special cases of the class of algorithms described. Therefore, the approach presented unifies a large number of iterative restoration algorithms.<<ETX>>
Classification of multiple annotator data using variational Gaussian process inference

In this paper we address supervised learning problems where, instead of having a single annotator who provides the ground truth, multiple annotators, usually with varying degrees of expertise, provide conflicting labels for the same sample. Once Gaussian Process classification has been adapted to this problem we propose and describe how Variational Bayes inference can be used to, given the observed labels, approximate the posterior distribution of the latent classifier and also estimate each annotator's reliability. In the experimental section, we evaluate the proposed method on both generated synthetic and real data, and compare it with state of the art crowd-sourcing methods.
Content-aware H.264 encoding for traffic video tracking applications

The compression of video can reduce the accuracy of tracking algorithms, which is problematic for centralized applications that rely on remotely captured and compressed video for input. We show the effects of high compression on the features commonly used in real-time video object tracking. We propose a computationally efficient Region of Interest (ROI) extraction method, which is used during standard-compliant H.264 encoding to concentrate bitrate on regions in video most likely to contain objects of tracking interest (vehicles). This algorithm is shown to significantly increase tracking accuracy, which is measured by employing a commonly used automatic tracker.
Content-Aware Resource Allocation for Scalable Video Transmission to Multiple Users Over a Wireless Network

Wireless video transmission is prone to unpredictable degradations due to time-varying channel conditions. Such degradations are difficult to overcome using conventional video coding techniques. Scalable video coding offers a flexible bitstream that can be dynamically adapted to fit the prevailing channel conditions. Within a scalable video coding framework, we develop simple packet prioritization strategies, which, when combined with a reasonable error concealment scheme and a content-aware resource allocation technique, provide for robust video transmission over time-varying channels. The packet prioritization as well as the calculation of the content-aware scheduling metric can be performed offline and signaled to the wireless scheduler.
Compressive sampling with unknown blurring function: Application to passive millimeter-wave imaging

We propose a novel blind image deconvolution (BID) regularization framework for compressive passive millimeter-wave (PMMW) imaging systems. The proposed framework is based on the variable-splitting optimization technique, which allows us to utilize existing compressive sensing reconstruction algorithms in compressive BID problems. In addition, a non-convex lp quasi-norm with 0 <; p <; 1 is employed as a regularization term for the image, while a simultaneous auto-regressive (SAR) regularization term is utilized for the blur. Furthermore, the proposed framework is very general and it can be easily adapted to other state-of-the-art BID approaches that utilize different image/blur regularization terms. Experimental results, obtained with simulations using a synthetic image and real PMMW images, show the advantage of the proposed approach compared to existing ones.
Simultaneous recursive displacement estimation and restoration of image sequences

The authors develop a recursive-model-based maximum a posteriori estimator that simultaneously estimates the displacement vector field (DVF) and intensity field from a noisy-blurred image sequence. Current motion-compensated spatio-temporal filters treat the estimation of the DVF as a preprocessing step. Therefore, no attempt is made to verify the accuracy of these estimates prior to their use in the filter. By simultaneously estimating these two fields, information is made available to each filter regarding the reliability of the estimates on which they are dependent. Nonstationary models are used for the DVF and the intensity field in the proposed estimator, thus avoiding the smoothing of boundaries present in both. Experimental results are provided which show the effectiveness of the proposed estimator.<<ETX>>
Resource-distortion optimal video coding and communications

We describe two major issues in object-based video coding and communications and provide solutions based on the MPEG-4 coding standard. We first consider the general problem of bit allocation among shape, texture and motion in video coding. and provide optimal solutions based on MINMAX (minimum maximum) and MINAVE (minimum average) distortion criteria, respectively. Then, we discuss the resource allocation problem in video communications and demonstrate a number of unequal error protection schemes, including separated packetization, joint source-channel coding and data hiding. Experimental results demonstrate significant gains by using these algorithms.
Rate-distortion optimized hybrid error control for real-time packetized video transmission

The problem of application-layer error control for real-time video transmission over packet lossy networks is commonly addressed via joint source-channel coding (JSCC), where source coding and forward error correction (FEC) are jointly designed to compensate for packet losses. In this paper, we consider hybrid application-layer error correction consisting of FEC and retransmissions. The study is carried out in an integrated joint source-channel coding (IJSCC) framework, where error resilient source coding, channel coding, and error concealment are jointly considered in order to achieve the best video delivery quality. We first show the advantage of the proposed IJSCC framework as compared to a sequential JSCC approach, where error resilient source coding and channel coding are not fully integrated. In the IJSCC framework, we also study the performance of different error control scenarios, such as pure FEC, pure retransmission, and their combination. Pure FEC and application layer retransmissions are shown to each achieve optimal results depending on the packet loss rates and the round-trip time. A hybrid of FEC and retransmissions is shown to outperform each component individually due to its greater flexibility.
Robust circle detection using a weighted MSE estimator

In this paper we introduce a novel circle detection algorithm based on a weighted minimum mean square error (MSE) formulation. Traditional approaches to circle detections consist of two stages, an edge detection stage and a circle detection stage using the edge detection result. There are several problems with this approach. First, the initial edge detection stage is sensitive to noise. Second, the second stage does not use all the information available in the image and therefore incorrect decisions made by the first stage cannot be corrected in the second stage. The proposed algorithm achieves its robustness by operating in one step, using all pixels of the image (correctly weighted) and not using any thresholds. The detected circle is the solution of several weighted MSE problems. Experimental results demonstrate the performance of the algorithm in noiseless and noisy conditions.
Reconstruction of a high resolution image from registration and restoration of low resolution images

In applications that demand highly detailed images, it is often not feasible nor sometimes possible to acquire images of such high resolution by just using hardware (high precision optics and charge coupled devices (CCDs)). Instead, image processing methods may be used to construct a high resolution image from multiple, degraded, low resolution images. It is assumed that the low resolution images have been sub-sampled and displaced by sub-pixel shifts. Therefore, the problem can be divided into three sub-problems: registration (estimating the shifts), restoration, and interpolation. This paper focuses on solving the first two sub-problems simultaneously, using the expectation-maximization (EM) algorithm. Experimental results are presented that demonstrate the effectiveness of this approach.<<ETX>>