nils-herrmann/clean_scirepeval huggingface dataset:
Locally monotonic regression

The concept of local monotonicity appears in the study of the set of root signals of the median filter and provides a measure of the smoothness of the signal. The median filter is a suboptimal smoother under this measure of smoothness, since a filter pass does necessarily yield a locally monotonic output; even if a locally monotonic output does result, there is no guarantee that it will possess other desirable properties such as optimal similarity to the original signal. Locally monotonic regression is a technique for the optimal smoothing of finite-length discrete real signals under such a criterion. A theoretical framework in which the existence of locally monotonic regression is proved and algorithms for their computation are given. Regression is considered as an approximation problem in R/sub n/, the criterion of approximation is derived from a semimetric, and the approximating set is the collection of signals sharing the property of being locally monotonic.
Three Dimensional Face Recognition Using Iso-Geodesic and Iso-Depth Curves

In this paper a new framework for personal identity verification using 3-D geometry of the face is introduced. Initially, 3-D facial surfaces are represented by curves extracted from facial surfaces (facial curves). Two alternative facial curves are examined in this research: iso-depth and iso-geodesic curves. Iso-depth curves are produced by intersecting a facial surface with parallel planes perpendicular to the direction of gaze, at different depths from the nose tip. An Iso-geodesic curve is defined to be the locus of all points on the facial surface having the same geodesic distance from a given facial landmark (e.g. the nose tip). Once the facial curves are extracted, their characteristics are encoded by several features like the shape descriptors or polar Euclidean distances from the origin (nose tip). The final step is to verify or disapprove requests from users claiming the identity of registered individuals (gallery members) by comparing their features using Euclidean distance classifier or support vector machine (SVM). The performance results of the identity verification experiments are reported and a comparison is made between the two alternative curve-based facial surface representations.
No-reference task performance prediction on distorted LWIR images

Recent work on the problem of Image Quality Assessment (IQA) has produced accurate subjective quality evaluators for visible light images. Two such algorithms are the Blind/Referenceless Image Spatial QUality Evaluator (BRISQUE) and the Natural Image Quality Evaluator (NIQE). Both models are useful in that they correlate highly with human visual perception of image quality. Given that other kinds of non-visible light images are also 'natural' projections of the world, and can be distorted thereby reducing the perceived quality, it is of interest to study whether quality prediction on other image modality can find practical use. To this end we have extended the application of modern blind IQA models.
Frame based multiple description image coding in the wavelet domain

Multiple description codes generated by quantized frame expansions have been shown to perform well on erasure channels when compared to traditional channel codes. In this paper we propose a multiple description image coding scheme in the wavelet domain using quantized frame expansions. We form zerotrees from wavelet coefficients and apply a tight frame operator to the zerotrees. We then group appropriate expansions to form packets and evaluate the performance of the scheme over an erasure channel. We compare the performance of the proposed scheme with a conventional channel coding scheme.
Shape description of biological objects via stereo light microscopy

Computational stereo vision techniques are applied to the analysis of the three-dimensional (3-D) shape of biological specimens imaged through a stereo light microscope. 3-D shape descriptions are derived by integrating binocular and monocular measurements. Microscopic biological objects viewed through a light microscope generally have ill-defined boundaries in shape and reflectance and often exhibit transparency. In addition, the limited illumination available in the light microscope often makes it difficult to obtain images of sufficiently high contrast. In the approach presented, a new matching algorithm is introduced using intensity gradient information to solve the problem. Shape descriptions are computed by exploiting a number of additional processing steps. Two different classes of shapes are considered, which lead to two distinct approaches for 3-D analysis: solid objects and vascular networks. The first class of objects is described using a visible surface representation, while the second is expressed in terms of a set of space curves. The efficacy of each approach is demonstrated using microscopic-scale solid and microvascular specimens.
DCT statistics model-based blind image quality assessment

We propose an efficient, general-purpose, distortion-agnostic, blind/no-reference image quality assessment (NR-IQA) algorithm based on a natural scene statistics model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. We propose a generalized parametric model of the extracted DCT coefficients. The parameters of the model are utilized to predict image quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human visual perception of quality, at a level that is even competitive with the powerful full-reference SSIM index.
Study of subject agreement on stereoscopic video quality

We describe a study that aims towards enhancing our understanding of the perception of H.264/AVC compressed stereoscopic 3D videos, in particular spatial video quality, depth quality, visual comfort and overall 3D video quality. The results of this study indicate that the human subjects have diverse opinions on depth quality scores but a high agreement on spatial video quality. Their agreement on overall 3D video quality is intermediate relative to that on spatial video quality and depth quality. Based on our analysis, we propose to use separate quality assessment models: spatial video quality models and depth quality models.
A motion compensated approach to video quality assessment

We propose a new full reference video quality assessment algorithm (FR VQA) - the motion compensated structural similarity index (MC-SSIM). MC-SSIM evaluates spatial quality as well as quality along temporal trajectories. Its computationally simplicity makes it a prime choice for practical implementation. In this paper we describe the algorithm and evaluate its performance on a publicly available VQA dataset. We demonstrate that MC-SSIM correlates well with human perception of quality. We also explore its relationship to the human visual system and describe how a simple and efficient implementation of MC-SSIM can be realized.
Rate Adaptation and Admission Control for Video Transmission With Subjective Quality Constraints

Adapting video data rate during streaming can effectively reduce the risk of playback interruptions caused by channel throughput fluctuations. The variations in rate, however, also introduce video quality fluctuations and thus potentially affects viewers' Quality of Experience (QoE). We show how the QoE of video users can be improved by rate adaptation and admission control. We conducted a subjective study wherein we found that viewers' QoE was strongly correlated with the empirical cumulative distribution function (eCDF) of the predicted video quality. Based on this observation, we propose a rate-adaptation algorithm that can incorporate QoE constraints on the empirical cumulative quality distribution per user. We then propose a threshold-based admission control policy to block users whose empirical cumulative quality distribution is not likely to satisfy their QoE constraint. We further devise an online adaptation algorithm to automatically optimize the threshold. Extensive simulation results show that the proposed scheme can reduce network resource consumption by 40% over conventional average-quality maximized rate-adaptation algorithms.
Optimal Channel Adaptation of Scalable Video Over a Multicarrier-Based Multicell Environment

To achieve seamless multimedia streaming services over wireless networks, it is important to overcome inter-cell interference (ICI), particularly in cell border regions. In this regard scalable video coding (SVC) has been actively studied due to its advantage of channel adaptation. We explore an optimal solution for maximizing the expected visual entropy over an orthogonal frequency division multiplexing (OFDM)-based broadband network from the perspective of cross-layer optimization. An optimization problem is parameterized by a set of source and channel parameters that are acquired along the user location over a multicell environment. A suboptimal solution is suggested using a greedy algorithm that allocates the radio resources to the scalable bitstreams as a function of their visual importance. The simulation results show that the greedy algorithm effectively resists ICI in the cell border region, while conventional nonscalable coding suffers severely because of ICI.
A Two-Step Framework for Constructing Blind Image Quality Indices

Present day no-reference/no-reference image quality assessment (NR IQA) algorithms usually assume that the distortion affecting the image is known. This is a limiting assumption for practical applications, since in a majority of cases the distortions in the image are unknown. We propose a new two-step framework for no-reference image quality assessment based on natural scene statistics (NSS). Once trained, the framework does not require any knowledge of the distorting process and the framework is modular in that it can be extended to any number of distortions. We describe the framework for blind image quality assessment and a version of this framework-the blind image quality index (BIQI) is evaluated on the LIVE image quality assessment database. A software release of BIQI has been made available online: http://live.ece.utexas.edu/research/quality/BIQI_release.zip.
Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain

We develop an efficient general-purpose blind/no-reference image quality assessment (IQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. The approach relies on a simple Bayesian inference model to predict image quality scores given certain extracted features. The features are based on an NSS model of the image DCT coefficients. The estimated parameters of the model are utilized to form features that are indicative of perceptual quality. These features are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. Given the extracted features from a test image, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human judgments of quality, at a level that is competitive with the popular SSIM index.
3D Visual Discomfort Prediction: Vergence, Foveation, and the Physiological Optics of Accommodation

To achieve clear binocular vision, neural processes that accomplish accommodation and vergence are performed via two collaborative, cross-coupled processes: accommodation-vergence (AV) and vergence-accommodation (VA). However, when people watch stereo images on stereoscopic displays, normal neural functioning may be disturbed owing to anomalies of the cross-link gains. These anomalies are likely the main cause of visual discomfort experienced when viewing stereo images, and are called Accommodation-Vergence Mismatches (AVM). Moreover, the absence of any useful accommodation depth cues when viewing 3D content on a flat panel (planar) display induces anomalous demands on binocular fusion, resulting in possible additional visual discomfort. Most prior efforts in this direction have focused on predicting anomalies in the AV cross-link using measurements on a computed disparity map. We further these contributions by developing a model that accounts for both accommodation and vergence, resulting in a new visual discomfort prediction algorithm dubbed the 3D-AVM Predictor. The 3D-AVM model and algorithm make use of a new concept we call local 3D bandwidth (BW) which is defined in terms of the physiological optics of binocular vision and foveation. The 3D-AVM Predictor accounts for anomalous motor responses of both accommodation and vergence, yielding predictive power that is statistically superior to prior models that rely on a computed disparity distribution only.
3D visual discomfort predictor based on neural activity statistics

Visual discomfort assessment (VDA) on stereoscopic images is of fundamental importance for making decisions regarding visual fatigue caused by unnatural binocular alignment. Nevertheless, no solid framework exists to quantify this discomfort using models of the responses of visual neurons. Binocular vision is realized by means of neural mechanisms that subserve the sensorimotor control of eye movements. We propose a neuronal model-based framework called Neural 3D Visual Discomfort Predictor (N3D-VDP) that automatically predicts the level of visual discomfort experienced when viewing stereoscopic 3D (S3D) images. The N3D-VDP model extracts features derived by estimating the neural activity associated with the processing of binocular disparities. In this regard we deploy a model of disparity processing in the extra-striate middle temporal (MT) region of occipital lobe. We compare the performance of N3D-VDP with other recent VDA algorithms using correlations against reported subjective visual discomfort, and show that N3D-VDP is statistically superior to the other methods.
Non-Euclidean locally monotonic regression

The concept of locally monotonic regression is extended by considering metrics on r/sup n/ that are different from the Euclidean metric. The existence of regressions for a large class of metrics is shown. Algorithms that show the computability of locally monotonic regressions are given. A general algorithm that computes regressions for the l/sub p/ metrics is given; considered in detail are the cases corresponding to the metrics l/sub 1/ and l/sub infinity /, where the sample median and the sample midrange play important roles.<<ETX>>
Stereo image compression using VPIC

A binocular stereo system for images coded by visual pattern image coding (VPIC) is presented and evaluated. The use of VPIC for stereo vision applications is motivated by the capability of its coding primitives to reflect meaningful physical properties of projected real-scene surfaces: high-information edge regions and uniform regions. An algorithm for spatial matching of VPIC primitives is proposed. A dense disparity map is obtained and then used, together with one of the VPIC coded images of the stereo pair, to predict the second stereo image. The proposed stereo system allows a very high compression of the overall stereo information.
Feature Normalization via Expectation Maximization and Unsupervised Nonparametric Classification For M-FISH Chromosome Images

Multicolor fluorescence in situ hybridization (M-FISH) techniques provide color karyotyping that allows simultaneous analysis of numerical and structural abnormalities of whole human chromosomes. Chromosomes are stained combinatorially in M-FISH. By analyzing the intensity combinations of each pixel, all chromosome pixels in an image are classified. Often, the intensity distributions between different images are found to be considerably different and the difference becomes the source of misclassifications of the pixels. Improved pixel classification accuracy is the most important task to ensure the success of the M-FISH technique. In this paper, we introduce a new feature normalization method for M-FISH images that reduces the difference in the feature distributions among different images using the expectation maximization (EM) algorithm. We also introduce a new unsupervised, nonparametric classification method for M-FISH images. The performance of the classifier is as accurate as the maximum-likelihood classifier, whose accuracy also significantly improved after the EM normalization. We would expect that any classifier will likely produce an improved classification accuracy following the EM normalization. Since the developed classification method does not require training data, it is highly convenient when ground truth does not exist. A significant improvement was achieved on the pixel classification accuracy after the new feature normalization. Indeed, the overall pixel classification accuracy improved by 20% after EM normalization.
What You See Is What You Learn [DSP Education]

I find teaching to be an extremely personal experience. After all, there are few occupations where one encloses herself or himself in a room for 50 hours or more, over a period of five months, with the same group of dozens (or more) individuals, all listening closely (we hope) as the professor pontificates upon the subject of his or her most intensive personal inquiries and study that may have spanned decades. Since I am to explain my own personal take on the experience of teaching a vital and sensorial topic, I will take the first person throughout. Hopefully, this will make it possible for me to better convey my own sense of joy and satisfaction working in the field of video processing education, as well as better justifying my views of the process of teaching this subject.
Adaptive policies for real-time video transmission: A Markov decision process framework

We study the problem of adaptive video data scheduling over wireless channels. We prove that, under certain assumptions, adaptive video scheduling can be reduced to a Markov decision process over a finite state space. Therefore, the scheduling policy can be optimized via standard stochastic control techniques using a Markov decision formulation. Simulation results show that significant performance improvement can be achieved over heuristic transmission schemes.
No-Reference Quality Assessment of Natural Stereopairs

We develop a no-reference binocular image quality assessment model that operates on static stereoscopic images. The model deploys 2D and 3D features extracted from stereopairs to assess the perceptual quality they present when viewed stereoscopically. Both symmetric- and asymmetric-distorted stereopairs are handled by accounting for binocular rivalry using a classic linear rivalry model. The NSS features are used to train a support vector machine model to predict the quality of a tested stereopair. The model is tested on the LIVE 3D Image Quality Database, which includes both symmetric- and asymmetric-distorted stereoscopic 3D images. The experimental results show that our proposed model significantly outperforms the conventional 2D full-reference QA algorithms applied to stereopairs, as well as the 3D full-reference IQA algorithms on asymmetrically distorted stereopairs.
Discrete quasi-eigenfunction approximation for AM-FM image analysis

We introduce a multidimensional quasi-eigenfunction approximation useful for characterizing and analyzing the responses of discrete linear systems to inputs modeled as AM-FM image functions. A new theorem is presented which provides a global bound on the approximation error. We demonstrate how the approximation may be used to develop discrete AM-FM demodulation algorithms, and show dramatic demodulation examples where the essential structure of natural images is captured using only a small number of AM-FM components.
Computing 3-D symbolic representation of blood vessels from stereo-microscopic images

The authors consider the three-dimensional analysis of vacular networks from stereo-microscopic images. The principal concern is to describe their 3-D structure using a set of parametric space curves. This representation is computed by integrating monocular and binocular processing: the 2-D curve representation of blood vessels computed by monocular analysis is integrated with disparity data to yield a space curve representation. A connection graph is computed to indicate the connection among space curves. The efficacy of the approach was demonstrated using vascular cast images.<<ETX>>
Adaptive video transmission with subjective quality constraints

We conducted a subjective study wherein we found that viewers' Quality of Experience (QoE) was strongly correlated with the empirical cumulative distribution function (eCDF) of the predicted video quality. Based on this observation, we propose a rate-adaptation algorithm that can incorporate QoE constraints on the empirical cumulative quality distribution per user. Simulation results show that the proposed technique can reduce network resource consumption by 29% over conventional average-quality maximized rate-adaptation algorithms.
Estimation and analysis of urban traffic flow

This paper describes methods for extracting traffic flow information from urban traffic scenes. The ultimate goal is to collect a macroscopic view of traffic flow information in a fully automatic and segmentation-free way. First, traffic flow is calculated by optical flow estimation. Then, traffic flow regions are defined by the initial traffic flow, and further analysis is performed only in the defined traffic flow regions. Basic statistics of the traffic flow vectors are studied. It is shown that traffic flow computed by optical flow estimation effectively captures traffic scene activity. Also, the statistics of traffic flow vectors contain meaningful and interesting characteristics. An example application demonstrates the applicability and potential uses of the statistics.
Reconstruction from the multi-component AM-FM image representation

We present powerful multi-component AM-FM image models capable of efficiently representing complicated nonstationary multi-partite images, and show how the representation can be computed in a practical implementation. With images of this type, important structural and perceptual information is often manifest in the nonstationarities. Highly localized nonlinear operators are used to simultaneously estimate the amplitude and frequency modulating functions associated with each of the multiple components on a pixel-by-pixel basis. For the first time, we also demonstrate image reconstruction from the AM-FM representation.
Multi-view stereo ranging via Distributed Ray Tracing

We explore the use of Distributed Ray Tracing (DRT), an anti-aliasing technique from computer graphics, in multi-view computational stereo. As an example, we study ABM, a multi-view stereo algorithm based on a set of Hough transform accumulation operations. Augmenting ABM with DRT improves both internal signal quality and reconstruction accuracy. Results are given for both fundamental and complex “super-resolution reconstruction” tasks, where the voxel side length is less than the image ground sample distance. DRT improves ABM accuracy by 18% and can be generalized to improve other stereo algorithms.