nils-herrmann/clean_scirepeval huggingface dataset:
A fast and memory efficient video transcoder for low bit rate wireless communications

Wireless video is one of the important applications supported by upcoming 3G mobile communication systems. In this paper, we propose a fast and memory efficient DCT-domain video transcoder to convert a high quality MPEG2 video bit stream into a low bit rate MPEG4 stream with low spatial resolution for wireless video access. Compared to existing approaches, the proposed video transcoder can save more than 50% of required memory. Furthermore, the computational complexity of the proposed method is less than 30% of that required by existing methods. However, the video quality achieved by the proposed method and by existing methods is hardly distinguishable for target bit rates of 384 kb/s and 256 kb/s, as shown in our experimental results.
Epipolar Spaces and Optimal Sampling Strategies

If precise calibration information is unavailable, as is often the case for active binocular vision systems, the determination of epipolar lines becomes untenable. Yet, even without instantaneous knowledge of the geometry, the search for corresponding points can be restricted to areas called epipolar spaces. For each point in one image, we define the corresponding epipolar space in the other image as the union of all associated epipolar lines over all possible system geometries. Epipolar spaces eliminate the need for calibration at the cost of an increased search region. One approach to mitigate this increase is the application of a space variant sampling or foveation strategy. While the application of such strategies to stereo vision tasks is not new, only rarely has a foveation scheme been specifically tailored for a stereo vision task. In this paper we derive a foundation of theorems that provide a means for obtaining optimal sampling schemes for a given set of epipolar spaces. An optimal sampling scheme is defined as a strategy that minimizes the average area per epipolar space.
Measurement and Detection of Spiculated Lesions

The goal of computer-aided detection (CADe) systems is to aid radiologists in interpreting mammograms. Both independent studies and manufacturer reports indicate that current CADe systems often fail to detect breast cancers that present as spiculated lesions on mammography. We are developing a new evidence-based framework for the detection of spiculated lesions. By evidence-based we mean that we use measurements of the properties of spiculated lesions to guide the design and selection of parameters of our detection algorithm. When measurements made by human observers are used, it is important to understand their reliability. There are two sources of variability in measuring properties of spiculated lesions. The precise location of spicules must first be identified by the observer and then s/he must measure the quantity of interest (e.g., length). In this paper, we present a preliminary report of the observer variability segmenting and measuring spiculated lesions
Channel interactions in visible pattern analysis

The efficacy of the 2-D Gabor channel filters for segmenting images based on the division of the image into textures was established previously. In the present work, the authors extend the model for the direct estimation of the emergent spatially localized orientation/frequencies of visible patterns using a variational scheme. The most significant result of this extension is that patterns with space-varying frequency characteristics, arising from, e.g. surface deformation, can still be analyzed and segregated. Moreover, the information extracted can provide a means for developing shape-from-texture algorithms that are optimally localized. The implementation of the filtering scheme is described.<<ETX>>
Foveated Visual Search for Corners

We cast the problem of corner detection as a corner search process. We develop principles of foveated visual search and automated fixation selection to accomplish the corner search, supplying a case study of both foveated search and foveated feature detection. The result is a new algorithm for finding corners, which is also a corner-based algorithm for aiming computed foveated visual fixations. In the algorithm, long saccades move the fovea to previously unexplored areas of the image, while short saccades improve the accuracy of putative corner locations. The system is tested on two natural scenes. As an interesting comparison study, we compare fixations generated by the algorithm with those of subjects viewing the same images, whose eye movements are being recorded by an eye tracker. The comparison of fixation patterns is made using an information-theoretic measure. Results show that the algorithm is a good locater of corners, but does not correlate particularly well with human visual fixations
Shape-from-texture by wavelet-based measurement of local spectral moments

A closed-form solution to the problem of computing 3D curved-surface shape from texture cues is presented. An expression showing the dependence of localized image spectral moments on localized surface spectral moments and on local surface orientation is derived. The local image spectra are measured with wavelets, and the expression is solved for the surface orientation at each point. Because the method uses localized spectral information, it operates at a very low level in the visual hierarchy. No extraction of texture or edge elements is required. The wavelet-based computation used is biologically plausible, easily parallelized for rapid computation, and has been shown to be the basis for effective solutions to a variety of other vision tasks. The method is demonstrated on a number of real-world examples.<<ETX>>
Binocular mismatch induced by luminance discrepancies on stereoscopic images

Luminance discrepancies between image pairs occur owing to inconsistent parameters between stereoscopic camera devices and from imperfect capture conditions. Such discrepancies induce binocular mismatches and affect the visual comfort that is felt by viewers, as well as their ability to fuse stereoscopic. To better understand and observe this effect, we built a stereoscopic images database of 240 luminance discrepancy images and 30 natural images with subjective scores of visual discomfort and fusion difficulty. Two features, binocular contrast and luminance similarity were extracted to analyze the relationship between the subjective scores and the luminance discrepancies. Structural dissimilarity and average luminance are used to predict the effects of binocular mismatches. The experimental results show that the combination of binocular contrast, structural dissimilarity and average luminance exhibits high consistency with subjective scores of visual discomfort, fusion difficulty and overall binocular mismatches in terms of Spearman's Rank Ordered Correlation Coefficient.
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality

Our approach to blind image quality assessment (IQA) is based on the hypothesis that natural scenes possess certain statistical properties which are altered in the presence of distortion, rendering them un-natural; and that by characterizing this un-naturalness using scene statistics, one can identify the distortion afflicting the image and perform no-reference (NR) IQA. Based on this theory, we propose an (NR)/blind algorithm-the Distortion Identification-based Image Verity and INtegrity Evaluation (DIIVINE) index-that assesses the quality of a distorted image without need for a reference image. DIIVINE is based on a 2-stage framework involving distortion identification followed by distortion-specific quality assessment. DIIVINE is capable of assessing the quality of a distorted image across multiple distortion categories, as against most NR IQA algorithms that are distortion-specific in nature. DIIVINE is based on natural scene statistics which govern the behavior of natural images. In this paper, we detail the principles underlying DIIVINE, the statistical features extracted and their relevance to perception and thoroughly evaluate the algorithm on the popular LIVE IQA database. Further, we compare the performance of DIIVINE against leading full-reference (FR) IQA algorithms and demonstrate that DIIVINE is statistically superior to the often used measure of peak signal-to-noise ratio (PSNR) and statistically equivalent to the popular structural similarity index (SSIM). A software release of DIIVINE has been made available online: http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip for public use and evaluation.
A DCT Statistics-Based Blind Image Quality Index

The development of general-purpose no-reference approaches to image quality assessment still lags recent advances in full-reference methods. Additionally, most no-reference or blind approaches are distortion-specific, meaning they assess only a specific type of distortion assumed present in the test image (such as blockiness, blur, or ringing). This limits their application domain. Other approaches rely on training a machine learning algorithm. These methods however, are only as effective as the features used to train their learning machines. Towards ameliorating this we introduce the BLIINDS index (BLind Image Integrity Notator using DCT Statistics) which is a no-reference approach to image quality assessment that does not assume a specific type of distortion of the image. It is based on predicting image quality based on observing the statistics of local discrete cosine transform coefficients, and it requires only minimal training. The method is shown to correlate highly with human perception of quality.
Detecting spread spectrum watermarks using natural scene statistics

This paper presents novel techniques for detecting watermarks in images in a known-cover attack framework using natural scene models. Specifically, we consider a class of watermarking algorithms, popularly known as spread spectrum-based techniques. We attempt to classify images as either watermarked or distorted by common signal processing operations like compression, additive noise etc. The basic idea is that the statistical distortion introduced by spread spectrum watermarking is very different from that introduced by other common distortions. Our results are very promising and indicate that this statistical framework is effective in the steganalysis of spread spectrum watermarks.
Eccentricity effect of motion silencing on naturalistic videos

We study the effect of eccentricity on flicker visibility in naturalistic videos. A series of human studies were executed in two tasks ("gaze the fixation mark" and "follow the moving object") to understand how object motion can reduce the visibility of flicker distortions as a function of eccentricity, motion speed, video quality, and flicker frequency. We found that either large eccentricity or large, coherent object motion could reduce flicker visibility. When they are combined, flicker visibility significantly decreased. Flicker visibility remained noticeable even at large eccentricity when the object was static. Human study results and statistical analysis show that highly eccentric, coherent object motion can significantly silence the awareness of flicker distortions on naturalistic videos.
Hierarchical visual pattern image coding

A very fast high-compression, high-fidelity, image coding technique termed visual pattern image coding (VPIC) was recently introduced by the authors (1990). A hierarchical version of VPIC termed hierarchical VPIC (HVPIC) is developed here. In HVPIC, coded images of improved visual fidelity relative to VPIC are generated, with increased compressions (routinely exceeding 20:1). Significantly, the computational efficiency of simple VPIC is retained by HVPIC, which utilizes simple VPIC at each resolution level.
The Texas active vision testbed

The Texas active vision testbed (TAVT) represents the most recent stage of an ongoing design process that weighed a large number of alternatives, for both hardware and software to create a flexible and precise tool for active vision research. In this paper, we describe this platform and our ongoing research employing this tool. The platform is used in the development of depth recovery algorithms to extend conventional active vision techniques, such as variable baseline stereo and active focus control, as well as support research in the active control of a vergent stereo geometry.<<ETX>>
Targeted L1L2: Naturalness-constrained image recovery from random projections

We devise an elastic net convex program that generates natural-looking compressive sensed images by mimicking features indicative of good quality images. We use the shape parameter of divisively normalized wavelet coefficients to iteratively solve a convex program and target the distribution of each wavelet band. Using this method we are able to create better quality images than other methods as gauged by perceptually relevant quality indices (SSIM).
Least-squares order statistic filters for signal restoration in dependent noise

C.G. Boncelet's algorithm ( Sept. 1987) is used to explore the OS filter design/analysis problem. In particular, the optimal filter for restoring nonrandom signals immersed in Markov noise, using the mean square error as an optimality criterion, is studied. The noise processes are modeled either as causal first-order autoregressive Gaussian or as first-order moving-average Gaussian. Various structural signal constraints are improved on the solution by stating them as local unbiasedness constraints.<<ETX>>
Closed-Form Correlation Model of Oriented Bandpass Natural Images

Most prevalent statistical models of natural images characterize only the univariate distributions of divisively normal- ized bandpass responses or wavelet-like decompositions of them. However, the higher-order dependencies between spatially neigh- boring responses are not yet well understood. Towards filling this gap, we propose a new closed-form spatial-oriented correlation model that captures statistical regularities between perceptually decomposed natural image luminance samples. We validate the new correlation model on a variety of natural images. Experi- mental results demonstrate the robustness of the new correlation model across image content. A software release that implements the new closed-form spatial-oriented correlation model is available at http://live.ece.utexas.edu/research/3dnss/bicorr_release.zip. Index Terms—Bivariate model, closed-form, natural scene sta- tistics (NSS), spatial-oriented correlation model.
Bayesian range segmentation using focus cues

The objective of range segmentation is to partition a scene into regions with different depth ranges. We first perform range classification by combining two paradigms: focus cues and Bayesian estimation. A criterion function from focus cues provides a basic rule for measuring the ranges of a region in images. A Bayesian estimation is obtained by modeling the class field as a Markov random field (MRF). To combine these two paradigms, we define a combined energy function in terms of the cost function using the criterion function values for focus measure and the energy function of the Gibbs distribution of the class field. Then the combined energy function is minimized by a modified simulated annealing method to obtain range classification. The range classification is based on quantized ranges, and it provides an initial range segmentation. For range segmentation, we obtain interpolated range values, and perform a merging process by modeling the field of ranges as a Gaussian Markov random field. The range segmentation result gives a description of the 3-D structure of a scene.
Image information and visual quality

Measurement of image quality is crucial for many image-processing algorithms. Traditionally, image quality assessment algorithms predict visual quality by comparing a distorted image against a reference image, typically by modeling the human visual system (HVS), or by using arbitrary signal fidelity criteria. We adopt a new paradigm for image quality assessment. We propose an information fidelity criterion that quantifies the Shannon information that is shared between the reference and distorted images relative to the information contained in the reference image itself. We use natural scene statistics (NSS) modeling in concert with an image degradation model and an HVS model. We demonstrate the performance of our algorithm by testing it on a data set of 779 images, and show that our method is competitive with state of the art quality assessment methods, and outperforms them in our simulations.
Automatic Feature Extraction and Statistical Shape Model of the AIDS Virus Spike

We introduce a method to automatically extract spike features of the AIDS virus imaged through an electron microscope. The AIDS virus spike is the primary target of drug design as it is directly involved in infecting host cells. Our method detects the location of these spikes and extracts a subvolume enclosing the spike. We have achieved a sensitivity of 80% for our best operating range. The extracted spikes are further aligned and combined to build a 4-D statistical shape model, where each voxel in the shape model is assigned a probability density function. Our method is the first fully automated technique that can extract subvolumes of the AIDS virus spike and be used to build a statistical model without the need for any user supervision. We envision that this new tool will significantly enhance the overall process of shape analysis of the AIDS virus spike imaged through the electron microscope. Accurate models of the virus spike will help in the development of better drug design strategies.
An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue

The automatic segmentation of nuclei in confocal reflectance images of cervical tissue is an important goal toward developing less expensive cervical precancer detection methods. Since in vivo confocal reflectance microscopy is an emerging technology for cancer detection, no prior work has been reported on the automatic segmentation of in vivo confocal reflectance images. However, prior work has shown that nuclear size and nuclear-to-cytoplasmic ratio can determine the presence or extent of cervical precancer. Thus, segmenting nuclei in confocal images will aid in cervical precancer detection. Successful segmentation of images of any type can be significantly enhanced by the introduction of accurate image models. To enable a deeper understanding of confocal reflectance microscopy images of cervical tissue, and to supply a basis for parameter selection in a classification algorithm, we have developed a model that accounts for the properties of the imaging system and of the tissues. Using our model in conjunction with a powerful image enhancement tool (anisotropic median-diffusion), appropriate statistical image modeling of spatial interactions (Gaussian Markov random fields), and a Bayesian framework for classification-segmentation, we have developed an effective algorithm for automatically segmenting nuclei in confocal images of cervical tissue. We have applied our algorithm to an extensive set of cervical images and have found that it detects 90% of hand-segmented nuclei with an average of 6 false positives per frame.
Unequal Power Allocation for JPEG Transmission Over MIMO Systems

With the introduction of multiple transmit and receive antennas in next generation wireless systems, real-time image and video communication are expected to become quite common, since very high data rates will become available along with improved data reliability. New joint transmission and coding schemes that explore advantages of multiple antenna systems matched with source statistics are expected to be developed. Based on this idea, we present an unequal power allocation scheme for transmission of JPEG compressed images over multiple-input multiple-output systems employing spatial multiplexing. The JPEG-compressed image is divided into different quality layers, and different layers are transmitted simultaneously from different transmit antennas using unequal transmit power, with a constraint on the total transmit power during any symbol period. Results show that our unequal power allocation scheme provides significant image quality improvement as compared to different equal power allocations schemes, with the peak-signal-to-noise-ratio gain as high as 14 dB at low signal-to-noise-ratios.
Blind Picture Upscaling Ratio Prediction

Natural scene statistics are well studied in the context of picture quality assessment and have been used in a wide variety of top-performing picture quality prediction models. Upscaling artifacts have been measured with regards to quality impairment using these kinds of models. However, the assessment and classification of subtle, less discriminable upscaling artifacts remains an unsolved problem. The nearly imperceptible artifacts pertaining to the extent and type of upscaling have not been predicted using natural scene statistics (NSS)-based models. We develop an accurate model for predicting the upscaling ratio applied to any natural image. By decomposing an input image frame using an orthogonal filter bank and locally normalizing the resulting responses, we show that the local energy terms can be used to predict the upscaling ratio. In fact, a simple linear regressor can be trained on these energy measurements; hence, no hyperparameter tuning is necessary. We compare the proposed model with other no-reference models using real-world data contained in the Netflix collection.
Visually Weighted Compressive Sensing: Measurement and Reconstruction

Compressive sensing (CS) makes it possible to more naturally create compact representations of data with respect to a desired data rate. Through wavelet decomposition, smooth and piecewise smooth signals can be represented as sparse and compressible coefficients. These coefficients can then be effectively compressed via the CS. Since a wavelet transform divides image information into layered blockwise wavelet coefficients over spatial and frequency domains, visual improvement can be attained by an appropriate perceptually weighted CS scheme. We introduce such a method in this paper and compare it with the conventional CS. The resulting visual CS model is shown to deliver improved visual reconstructions.
Stereo vision using Gabor wavelets

The analysis of video images in stereo can extend machine vision to interpret the 3-D structure of a scene. Applications of stereo vision include robotics, industrial automation, autonomous land rovers and automated cartography. The simplest stereo paradigm, binocular stereo vision, provides man and many animals the capability to see the depth from two images without ambiguity. Thus, it is interesting to study the biological solution to stereopsis. In this paper, a biologically motivated model of stereopsis based on a coarse-to-fine matching algorithm using multiband Gabor wavelets is presented. This approach generates a dense disparity map by phase difference computation between stereo image pairs without complex feature extraction. Results of the algorithm for both synthetic and natural stereo images are presented.<<ETX>>
Study of the effects of stalling events on the quality of experience of mobile streaming videos

We have created a new mobile video database that models distortions caused by network impairments. In particular, we simulate stalling events and startup delays in over-the-top (OTT) mobile streaming videos. We describe the way we simulated diverse stalling events to create a corpus of distorted videos and the human study we conducted to obtain subjective scores. We also analyzed the ratings to understand the impact of several factors that influence the quality of experience (QoE). To the best of our knowledge, ours is the most comprehensive and diverse study on the effects of stalling events on QoE. We are making the database publicly available  in order to help advance state-of-the-art research on user-centric mobile network planning and management.