nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive visual pattern image coding

Hierarchical visual pattern image coding (HVPIC) is a highly successful, low-computational-cost, pyramidal image coding scheme which utilizes human visual system (HVS) properties to achieve very low bit rates while maintaining a good perceived image quality. A description is presented of an extension of HVPIC, termed adaptive VPIC (AVPIC), which results in a decreased bit rate for most images with very little additional computation and virtually no further image degradation. After coding but before transmission, the encoder analyzes what it has done, and reduces the transmission bit rate by eliminating high-resolution information about low-resolution portions of the image. The method is adaptive in the sense that the bit rate is locally adjusted to match the level of detail present in an image region.<<ETX>>
A nonlinear compound representation of sea clutter

We explore the applicability of our recently developed Nonlinear Compound Gaussian (NCG)  distribution to modeling the statistics of sea clutter data. We first, for completeness, give a self-contained description of our NCG distribution; both its theoretical properties and the algorithmic details for parameter estimation. We then demonstrate the performance of NCG in modeling the range statistics of sea-clutter data . The results clearly demonstrate the superiority of NCG in modeling sea-clutter phenomena over the compound Gaussian (CG) distribution. We conclude with a brief discussion of a phenomenological interpretation of these results together with directions for future research.
Limits on discrete modulated signals

We develop theorems of a general nature that apply to the analysis of AM-FM signals of the form a(m)exp [j/spl phi/(m)] or a(m) cos [/spl phi/(m)] and to their behavior both in linear systems and in simple nonlinear systems comprised of products of linear elements. Such product-systems include interesting nonlinear demodulation operators, such as the Teager-Kaiser (1990) operator. Expressions for the approximate system responses to AM-FM signals are derived by making an analogy to the eigenfunction interpretation of sinusoids in linear systems; for the case of sinusoidal signals, the approximations are exact. These expressions are collectively called quasieigenfunction approximations (QEAs). For nonsinusoidal AM-FM signals, the approximations have errors that are tightly bounded by functionals that express the smoothness of the AM and FM information signals and the durations of the involved system impulse responses. The bounds are independent of the bandwidths of the AM and FM functions. Two general applications are considered. First, the approximations are found to be useful for analyzing discrete-time nonlinear energy operators, including the Teager-Kaiser operator. Next, the approximation theorems lead to the selection of an optimal class of bandpass filters for use in a discrete multiband AM-FM demodulation system. The filter class selected is optimal in the sense of achieving the lower bound of a novel discrete uncertainty principle.
Integral inequality bounding the weighted absolute deviation of an n-dimensional function

The author states and proves an integral inequality that bounds the weighted integrated absolute deviation of a differentiable n-dimensional real function over an interval, relative to any value the function takes within the interval. Examples illustrate the utility of the inequality. In particular, the inequality is shown to be applicable to certain set-theoretic signal restoration algorithms, which project an observed (degraded) signal onto a closed, convex prototype set defined by a linear filter and a suitable bound.
Sampling approximation of smooth functions via generalized coiflets

We present the sampling approximation power of a newly constructed class of compactly supported orthonormal wavelets called generalized coiflets. We study the accuracy of generalized coiflets-based sampling approximation of smooth functions by developing convergence rates for the pointwise approximation error as well as its L/sup P/-norm. We show: (i) that the L/sup 2/-error due to the approximation of expansion coefficients by function samples is asymptotically negligible compared with that due to projection and (ii) that generalized coiflets can achieve asymptotically better approximation than the original coiflets.
Image information and visual quality

Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Image QA algorithms generally interpret image quality as fidelity or similarity with a "reference" or "perfect" image in some perceptual space. Such "full-reference" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by signal fidelity measures. In this paper, we approach the image QA problem as an information fidelity problem. Specifically, we propose to quantify the loss of image information to the distortion process and explore the relationship between image information and visual quality. QA systems are invariably involved with judging the visual quality of "natural" images and videos that are meant for "human consumption." Researchers have developed sophisticated models to capture the statistics of such natural signals. Using these models, we previously presented an information fidelity criterion for image QA that related image quality with the amount of information shared between a reference and a distorted image. In this paper, we propose an image information measure that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image. Combining these two quantities, we propose a visual information fidelity measure for image QA. We validate the performance of our algorithm with an extensive subjective study involving 779 images and show that our method outperforms recent state-of-the-art image QA algorithms by a sizeable margin in our simulations. The code and the data from the subjective study are available at the LIVE website.
3D Visual discomfort prediction based on physiological optics of binocular vision and foveation

For clear binocular vision in the brain, the neural interaction for accommodation and vergence is performed via inter-operation of two cross-links: accommodative-vergence (AV) and vergence-accommodation (VA). However, when people watch stereo images on stereoscopic display, the neural operation is interrupted due to alternations of the cross-link gains, which are attributed as the main reason of visual discomfort on stereo images in terms of Accommodation-Vergence Mismatch (AVM). In this paper, we present a novel visual discomfort prediction algorithm dubbed 3D-AVM Predictor to quantify the visual discomfort by including neural responses to sensory stimuli of both accommodation and vergence. In particular, we define the 3D local bandwidth (BW) based on physiological optics of binocular vision and foveation for optical activity of accommodation from the perspective of the VA cross-link. Since the 3D-AVM Predictor encompasses the anomalous motor responses of both accommodation and vergence, it is demonstrated that the performance is statistically superior to those of conventional works which rely on the factor of disparity distribution only.
The missing cone problem and low-pass distortion in optical serial sectioning microscopy

In optical serial sectioning, the 3-D structure of a microscopic specimen is observed by incrementing the focusing plane of a light microscope through the specimen. If the depth of field of the microscope is infinitesimal, the image obtained from each focusing plane is an in-focus slice of the optical density of the specimen. The authors show that the finite aperture of any practical microscope inevitably results in the loss of a biconic region of frequencies in the 3-D Fourier spectrum of the optical density, oriented in the direction of the optical axis. Thus, the resolution along this axis is severely reduced. Outside the missing cone of frequencies, the spectrum is distorted by a strong low-pass effect. A closed form expression is obtained for the overall distortion function using principles of geometric optics, and by assuming that the absorption of the specimen is linear and nondiffractive. Methods for restoring the 3-D images obtained through optical serial sectioning are considered, and several examples are provided.<<ETX>>
On detecting edges in speckle imagery

Methods for detecting sustained intensity changes in images corrupted by speckle are analyzed. The problem is complicated by the nature of the speckle, which is characterized by a high degree of correlation and (approximately multiplicative) signal dependence. These characteristics make the automated extraction of edges in speckle very difficult for applications requiring the location and identifications of objects (e.g., synthetic aperture radar). In particular, the Laplacian-of-a-Gaussian (LoG) edge detector for this problem is analyzed. While the LoG is found to be effective for detecting meaningful edges, use of the LoG also gives rise to numerous extraneous edges having no physical correlate. To alleviate these effects, a ratio-of-averages edge detector is used in conjunction with the LoG. The combined scheme is found to be much more effective than either of the individual edge detectors. Actual SEASAT synthetic aperture radar (SAR) images are used to demonstrate the effectiveness of each technique.
Stereoscopic 3D Visual Discomfort Prediction: A Dynamic Accommodation and Vergence Interaction Model

The human visual system perceives 3D depth following sensing via its binocular optical system, a series of massively parallel processing units, and a feedback system that controls the mechanical dynamics of eye movements and the crystalline lens. The process of accommodation (focusing of the crystalline lens) and binocular vergence is controlled simultaneously and symbiotically via cross-coupled communication between the two critical depth computation modalities. The output responses of these two subsystems, which are induced by oculomotor control, are used in the computation of a clear and stable cyclopean 3D image from the input stimuli. These subsystems operate in smooth synchronicity when one is viewing the natural world; however, conflicting responses can occur when viewing stereoscopic 3D (S3D) content on fixed displays, causing physiological discomfort. If such occurrences could be predicted, then they might also be avoided (by modifying the acquisition process) or ameliorated (by changing the relative scene depth). Toward this end, we have developed a dynamic accommodation and vergence interaction (DAVI) model that successfully predicts visual discomfort on S3D images. The DAVI model is based on the phasic and reflex responses of the fast fusional vergence mechanism. Quantitative models of accommodation and vergence mismatches are used to conduct visual discomfort prediction. Other 3D perceptual elements are included in the proposed method, including sharpness limits imposed by the depth of focus and fusion limits implied by Panum's fusional area. The DAVI predictor is created by training a support vector machine on features derived from the proposed model and on recorded subjective assessment results. The experimental results are shown to produce accurate predictions of experienced visual discomfort.
Rate Bounds on SSIM Index of Quantized Images

In this paper, we derive bounds on the structural similarity (SSIM) index as a function of quantization rate for fixed-rate uniform quantization of image discrete cosine transform (DCT) coefficients under the high-rate assumption. The space domain SSIM index is first expressed in terms of the DCT coefficients of the space domain vectors. The transform domain SSIM index is then used to derive bounds on the average SSIM index as a function of quantization rate for uniform, Gaussian, and Laplacian sources. As an illustrative example, uniform quantization of the DCT coefficients of natural images is considered. We show that the SSIM index between the reference and quantized images fall within the bounds for a large set of natural images. Further, we show using a simple example that the proposed bounds could be very useful for rate allocation problems in practical image and video coding applications.
Calibrating MS-SSIM for compression distortions using MLDS

In this paper, we describe a recently developed method for assessing perceived image quality, Maximum Likelihood Difference Scaling (MLDS), and use it to assess the performance of MS-SSIM on compression distored images. MLDS allows us to quantify supra-threshold perceptual differences between pairs of images and to examine how perceived image quality, estimated through MLDS, changes as the compression rate is increased. We show how the data collected by MLDS allows us to recalibrate MS-SSIM to improve its performance.
Image quality assessment to enhance infrared face recognition

Automatic quality evaluation of infrared images has not been researched as extensively as for images of the visible spectrum. Moreover, there is a lack of studies on the influence of degradation of image quality on the performance of computer vision tasks operating on thermal images. Here, we quantify the impact of common image distortions on infrared face recognition, and present a method for aggregating perceptual quality-aware features to improve the identification rates. We use Natural Scene Statistics (NSS) to detect degradation of infrared images, and to adapt the face recognition algorithm to the quality of the test image. The proposed approach applied to a face identification algorithm based on thermal signatures yielded an improvement of rank one recognition rates between 11% and 19%. These results confirm the relevance of image quality assessment for improving biometric identification systems that use thermal images.
Temporal Video Quality Model Accounting for Variable Frame Delay Distortions

We announce a new video quality model (VQM) that accounts for the perceptual impact of variable frame delays (VFD) in videos with demonstrated top performance on the laboratory for image and video engineering (LIVE) mobile video quality assessment (VQA) database. This model, called VQM_VFD, uses perceptual features extracted from spatialtemporal blocks spanning fixed angular extents and a long edge detection filter. VQM_VFD predicts video quality by measuring multiple frame delays using perception based parameters to track subjective quality over time. In the performance analysis of VQM_VFD, we evaluated its efficacy at predicting human opinions of visual quality. A detailed correlation analysis and statistical hypothesis testing show that VQM_VFD accurately predicts human subjective judgments and substantially outperforms top-performing image quality assessment and VQA models previously tested on the LIVE mobile VQA database. VQM_VFD achieved the best performance on the mobile and tablet studies of the LIVE mobile VQA database for simulated compression, wireless packet-loss, and rate adaptation, but not for temporal dynamics. These results validate the new model and warrant a hard release of the VQM_VFD algorithm.
Detection of object boundaries in synthetic aperture radar imagery using a human visual model

We consider the problem of automatically detecting sustained intensity changes, corresponding to object boundaries, in SAR imagery. The problem is complicated by the speckle noise found in SAR imagery, which is highly correlated and signal-dependent. In this paper, we analyze the efficacy of applying to this problem recently developed models for visual edge detection, in particular, the Marr-Hildreth Laplacian-of-a-Gaussian operator.
Referenceless perceptual image defogging

We propose a referenceless perceptual defog and visibility enhancement model based on multiscale “fog aware” statistical features. Our model operates on a single foggy image and uses a set of “fog aware” weight maps to improve the visibility of foggy regions. The proposed defog and visibility enhancer makes use of statistical regularities observed in foggy and fog-free images to extract the most visible information from three processed image results: one white balanced and two contrast enhanced images. Perceptual fog density, fog aware luminance, contrast, saturation, chrominance, and saliency weight maps smoothly blend these via a Laplacian pyramid. Evaluation on a variety of foggy images shows that the proposed model achieves better results for darker, denser foggy images as well as on standard defog test images.
Pixel-by-pixel classification of MFISH images

Multiplex Fluorescence In-Situ Hybridization (M-FISH) is a recently developed chromosome imaging method in which each chromosome is labelled with 5 fluors (dyes) and is also counterstained with DAPI. This paper proposes an automatic pixel by pixel classification algorithm for M-FISH images using a Bayes classifier. The M-FISH pixel classification was approached as a 25 class 6 feature pattern recognition problem. The classifier was trained and tested on non-overlapping data sets and an overall classification accuracy of 95% was obtained.
. Efficient DCT-domain blind measurement and reduction of blocking artifacts

Blocking artifacts continue to be among the most serious defects that occur in images and video streams compressed to low bit rates using block discrete cosine transform (DCT)-based compression standards (e.g., JPEG, MPEG, and H.263). It is of interest to be able to numerically assess the degree of blocking artifact in a visual signal, for example, in order to objectively determine the efficacy of a compression method, or to discover the quality of video content being delivered by a web server. We propose new methods for efficiently assessing, and subsequently reducing, the severity of blocking artifacts in compressed image bitstreams. The method is blind, and operates only in the DCT domain. Hence, it can be applied to unknown visual signals, and it is efficient since the signal need not be compressed or decompressed. In the algorithm, blocking artifacts are modeled as 2-D step functions. A fast DCT-domain algorithm extracts all parameters needed to detect the presence of, and estimate the amplitude of blocking artifacts, by exploiting several properties of the human vision system. Using the estimate of blockiness, a novel DCT-domain method is then developed which adaptively reduces detected blocking artifacts. Our experimental results show that the proposed method of measuring blocking artifacts is effective and stable across a wide variety of images. Moreover, the proposed blocking-artifact reduction method exhibits satisfactory performance as compared to other post-processing techniques. The proposed technique has a low computational cost hence can be used for real-time image/video quality monitoring and control, especially in applications where it is desired that the image/video data be processed directly in the DCT-domain.
The SIVA Demonstration Gallery for signal, image, and video processing education

The techniques of digital signal processing (DSP) and digital image processing (DIP) have found a myriad of applications in diverse fields of scientific, commercial, and technical endeavor. DSP and DIP education needs to cater to a wide spectrum of people from different educational backgrounds. This paper describes tools and techniques that facilitate a gentle introduction to fascinating concepts in signal and image processing. Novel LabVIEW- and MATLAB-based demonstrations are presented, which, when supplemented with Web-based class lectures, help to illustrate the power and beauty of signal and image-processing algorithms. Equipped with informative visualizations and a user-friendly interface, these modules are currently being used effectively in a classroom environment for teaching DSP and DIP at the University of Texas at Austin (UT-Austin). Most demonstrations use audio and image signals to give students a flavor of real-world applications of signal and image processing. This paper is also intended to provide a library of more than 50 visualization modules that accentuate the intuitive aspects of DSP algorithms as a free didactic tool to the broad signal and image-processing community.
A dynamic system model of time-varying subjective quality of video streams over HTTP

Newly developed HTTP-based video streaming technology enables flexible rate-adaptation in varying channel conditions. The users' Quality of Experience (QoE) of rate-adaptive HTTP video streams, however, is not well understood. Therefore, designing QoE-optimized rate-adaptive video streaming algorithms remains a challenging task. An important aspect of understanding and modeling QoE is to be able to predict the up-to-the-moment subjective quality of video as it is played. We propose a dynamic system model to predict the time-varying subjective quality (TVSQ) of rate-adaptive videos that is transported over HTTP. For this purpose, we built a video database and measured TVSQ via a subjective study. A dynamic system model is developed using the database and the measured human data. We show that the proposed model can effectively predict the TVSQ of rate-adaptive videos in an online manner, which is necessary to be able to conduct QoE-optimized online rate-adaptation for HTTP-based video streaming.
A Markov Decision Model for Adaptive Scheduling of Stored Scalable Videos

We propose two scheduling algorithms that seek to optimize the quality of scalably coded videos that have been stored at a video server before transmission. The first scheduling algorithm is derived from a Markov decision process (MDP) formulation developed here. We model the dynamics of the channel as a Markov chain and reduce the problem of dynamic video scheduling to a tractable Markov decision problem over a finite-state space. Based on the MDP formulation, a near-optimal scheduling policy is computed that minimizes the mean square error. Using insights taken from the development of the optimal MDP-based scheduling policy, the second proposed scheduling algorithm is an online scheduling method that only requires easily measurable knowledge of the channel dynamics, and is thus viable in practice. Simulation results show that the performance of both scheduling algorithms is close to a performance upper bound also derived in this paper.
New bivariate statistical model of natural image correlations

We perform bivariate statistical analysis and modeling of the joint distributions of spatially adjacent sub-band responses for both luminance/chrominance and range data in natural scenes. In particular, we introduce a multivariate generalized Gaussian distribution and an exponentiated sine function to model the underlying statistics and correlations. The experimental results show that the bivariate statistics relating spatially adjacent pixels in both 2D color images and range maps are well described by the proposed models. We validate the robustness of the proposed bivariate models using a multi-variate statistical hypothesis test, and further demonstrate their effectiveness with application to a prototype depth estimation algorithm.