nils-herrmann/clean_scirepeval huggingface dataset:
A no-reference perceptual blockiness metric

A novel no-reference blockiness metric that can automatically and perceptually quantify blocking artifacts of DCT coding is presented. The proposed metric is built upon the specific structure information of the artifact itself combined with the properties of the human visual system (HVS) by means of a simple and efficient model of visual masking. Investigations are conducted to reduce the additional cost introduced by the human vision model, without compromising its overall prediction ability. The proposed metric is validated through comparing its performance to state-of-the-art HVS model based blockiness metrics with respect to accuracy, reliability and computational complexity.
An efficient approach for boundary based corner detection by maximizing bending ratio and curvature

This paper introduces a novel corner detection method, which is based on the bending ratio of a moving window along with a local curvature approximation. A pre-processing step is first carried out in order to find one-pixel thin object boundaries. The proposed method traces over these boundaries, as we refer to as sub-segments and as the first step all potential corners are extracted by finding the maximum bending ratio in the moving window. The exact corner position within the window is then located accurately using the pixel-based curvature approximation. A corner factor can then be assigned to a potential corner using the maximum bending ratio and curvature values and among all potential corners, non-maximum suppression is applied to a group in close proximity and thus only the ones with the highest corner factors survive. In this way the spurious corners are significantly reduced, whilst keeping the true corners. A dedicated set of experimental results approve that the proposed method is highly accurate, computationally efficient and robust resolution and scale variations..
Visual Attention in Objective Image Quality Assessment: Based on Eye-Tracking Data

Since the human visual system (HVS) is the ultimate assessor of image quality, current research on the design of objective image quality metrics tends to include an important feature of the HVS, namely, visual attention. Different metrics for image quality prediction have been extended with a computational model of visual attention, but the resulting gain in reliability of the metrics so far was variable. To better understand the basic added value of including visual attention in the design of objective metrics, we used measured data of visual attention. To this end, we performed two eye-tracking experiments: one with a free-looking task and one with a quality assessment task. In the first experiment, 20 observers looked freely to 29 unimpaired original images, yielding us so-called natural scene saliency (NSS). In the second experiment, 20 different observers assessed the quality of distorted versions of the original images. The resulting saliency maps showed some differences with the NSS, and therefore, we applied both types of saliency to four different objective metrics predicting the quality of JPEG compressed images. For both types of saliency the performance gain of the metrics improved, but to a larger extent when adding the NSS. As a consequence, we further integrated NSS in several state-of-the-art quality metrics, including three full-reference metrics and two no-reference metrics, and evaluated their prediction performance for a larger set of distortions. By doing so, we evaluated whether and to what extent the addition of NSS is beneficial to objective quality prediction in general terms. In addition, we address some practical issues in the design of an attention-based metric. The eye-tracking data are made available to the research community .
Benchmarking state-of-the-art visual saliency models for image quality assessment

A significant current research trend in image quality assessment is to investigate the added value of visual attention aspects. Previous approaches mainly focused on adopting a specific saliency model to improve a specific image quality metric (IQM). It is still not known yet which of the existing saliency models is generally applicable in IQMs; which of the IQMs can profit most/least from the addition of saliency; and how this improvement depends on the saliency model used and the IQM targeted. In this paper, a large-scale benchmark study is conducted to assess the capabilities and limitations of the state-of-the-art saliency models in the context of IQMs. The study provides guidance for the application of saliency models in IQMs, in terms of the effect of saliency model dependency, IQM dependency, and image distortion dependency.
The Impact of Specialty Settings on the Perceived Quality of Medical Ultrasound Video

Health care professionals are increasingly viewing medical images and videos in a variety of environments. The perception of medical visual information across all specialties, career stages, and practice settings are critical to patient care and patient safety. Visual signal distortions, such as various types of noise and artifacts arising in medical imaging, affect the perceptual quality of visual content and potentially impact diagnoses. To optimize clinical practice, it is of fundamental importance to understand the way medical experts perceive visual quality. Psychophysical studies have been undertaken to evaluate the impact of visual distortions on the perceived quality of medical images and videos. However, very little research has been conducted on how speciality settings affect the perception of visual quality. In this paper, we investigate whether and how radiologists and sonographers differently perceive the quality of compressed ultrasound videos, via a dedicated subjective experiment. The findings can be used to develop useful solutions for improved visual experience and better image-based diagnoses.
Quality Assessment and Perception in Computer Graphics

The computer graphics community has successfully exploited our knowledge about the human visual system and its limitations for years. The perceptual computer graphics domain is a rich research area, and the understanding of human vision mechanisms and their integration in the computer graphics processes are becoming increasingly crucial in most applications. This special issue represents a good sample of the huge diversity of challenges in the perceptual computer graphics area: aesthetics evaluation, user gaze analysis, shape perception, and the wide scope of targeted applications.
Study of Saliency in Objective Video Quality Assessment

Reliably predicting video quality as perceived by humans remains challenging and is of high practical relevance. A significant research trend is to investigate visual saliency and its implications for video quality assessment. Fundamental problems regarding how to acquire reliable eye-tracking data for the purpose of video quality research and how saliency should be incorporated in objective video quality metrics (VQMs) are largely unsolved. In this paper, we propose a refined methodology for reliably collecting eye-tracking data, which essentially eliminates bias induced by each subject having to view multiple variations of the same scene in a conventional experiment. We performed a large-scale eye-tracking experiment that involved 160 human observers and 160 video stimuli distorted with different distortion types at various degradation levels. The measured saliency was integrated into several best known VQMs in the literature. With the assurance of the reliability of the saliency data, we thoroughly assessed the capabilities of saliency in improving the performance of VQMs, and devised a novel approach for optimal use of saliency in VQMs. We also evaluated to what extent the state-of-the-art computational saliency models can improve VQMs in comparison to the improvement achieved by using “ground truth” eye-tracking data. The eye-tracking database is made publicly available to the research community.
State of the Art: Eye-Tracking Studies in Medical Imaging

Eye-tracking—the process of measuring where people look in a visual field—has been widely used to study how humans process visual information. In medical imaging, eye-tracking has become a popular technique in many applications to reveal how visual search and recognition tasks are performed, providing information that can improve human performance. In this paper, we present a comprehensive review of eye-tracking studies conducted with medical images and videos for diverse research purposes, including the identification of the degree of expertise, development of training, and understanding and modeling of visual search patterns. In addition, we present our recent eye-tracking study that involves a large number of screening mammograms viewed by experienced breast radiologists. Based on the eye-tracking data, we evaluate the plausibility of predicting visual attention by computational models.
The Relative Impact of Ghosting and Noise on the Perceived Quality of MR Images

Magnetic resonance (MR) imaging is vulnerable to a variety of artifacts, which potentially degrade the perceived quality of MR images and, consequently, may cause inefficient and/or inaccurate diagnosis. In general, these artifacts can be classified as structured or unstructured depending on the correlation of the artifact with the original content. In addition, the artifact can be white or colored depending on the flatness of the frequency spectrum of the artifact. In current MR imaging applications, design choices allow one type of artifact to be traded off with another type of artifact. Hence, to support these design choices, the relative impact of structured versus unstructured or colored versus white artifacts on perceived image quality needs to be known. To this end, we conducted two subjective experiments. Clinical application specialists rated the quality of MR images, distorted with different types of artifacts at various levels of degradation. The results demonstrate that unstructured artifacts deteriorate quality less than structured artifacts, while colored artifacts preserve quality better than white artifacts.
A Comparative Study of Algorithms for Realtime Panoramic Video Blending

Unlike image blending algorithms, video blending algorithms have been little studied. In this paper, we investigate six popular blending algorithms—feather blending, multi-band blending, modified Poisson blending, mean value coordinate blending, multi-spline blending, and convolution pyramid blending. We consider their application to blending realtime panoramic videos, a key problem in various virtual reality tasks. To evaluate the performances and suitabilities of the six algorithms for this problem, we have created a video benchmark with several videos captured under various conditions. We analyze the time and memory needed by the above six algorithms, for both CPU and GPU implementations (where readily parallelizable). The visual quality provided by these algorithms is also evaluated both objectively and subjectively. The video benchmark and algorithm implementations are publicly available.<xref rid="fn1" ref-type="fn"><sup>1</sup></xref><fn id="fn1"><label><sup>1</sup></label><p><uri>http://cg.cs.tsinghua.edu.cn/blending/</uri></p></fn>
Towards an efficient model of visual saliency for objective image quality assessment

Based on “ground truth” eye-tracking data, earlier research  shows that adding natural scene saliency (NSS) can improve an objective metric's performance in predicting perceived image quality. To include NSS in a real-world implementation of an objective metric, a computational model instead of eye-tracking data is needed. Existing models of visual saliency are generally designed for a specific domain, and so, not applicable to image quality prediction. In this paper, we propose an efficient model for NSS, inspired by findings from our eye-tracking studies. Experimental results show that the proposed model sufficiently captures the saliency of the eye-tracking data, and applying the model to objective image quality metrics enhances their performance in the same manner as when including eye-tracking data.
Studying human behavioural responses to time-varying distortions for video quality assessment

Advances in video quality assessment have shown the added value of including temporal aspects of artifact perception in its objective metrics. The impact of quality variations over time and the associated implications for the assessment of overall video quality are so far not fully understood yet. To investigate the human behavioural responses to time-varying distortions and the relevance of such perception to overall quality judgements, a series of subjective experiments were conducted. In the major experiment, 30 human subjects scored the overall quality of 120 stimuli, which consist of 84 videos of various time-varying quality profiles and of their corresponding 36 constituent segments of spatio-temporally constant quality. Results show that the pattern of quality variations over time tends to affect the temporal summation strategy that delivers an overall quality.
Saliency in objective video quality assessment: What is the ground truth?

Finding ways to be able to objectively and reliably assess video quality as would be perceived by humans has become a pressing concern in the multimedia community. To enhance the performance of video quality metrics (VQMs), a research trend is to incorporate visual saliency aspects. Existing approaches have focused on utilizing a computational saliency model to improve a VQM. Since saliency models still remain limited in predicting where people look in videos, the benefits of inclusion of saliency in VQMs may heavily depend on the accuracy of the saliency model used. To gain an insight into the actual added value of saliency in VQMs, ground truth saliency obtained from eye-tracking instead of computational saliency is an essential prerequisite. However, collecting eye-tracking data within the context of video quality is confronted with a bias due to the involvement of massive stimulus repetition. In this paper, we introduce a new experimental methodology to alleviate such potential bias and consequently, to be able to deliver reliable intended data. We recorded eye movements from 160 human observers while they freely viewed 160 video stimuli distorted with different distortion types at various degradation levels. We analyse the extent to which ground truth saliency as well as computational saliency actually benefit existing state of the art VQMs. Our dataset opens new challenges for saliency modelling in video quality research and helps better gauge progress in developing saliency-based VQMs.
A No-Reference Metric for Perceived Ringing Artifacts in Images

A novel no-reference metric that can automatically quantify ringing annoyance in compressed images is presented. In the first step a recently proposed ringing region detection method extracts the regions which are likely to be impaired by ringing artifacts. To quantify ringing annoyance in these detected regions, the visibility of ringing artifacts is estimated, and is compared to the activity of the corresponding local background. The local annoyance score calculated for each individual ringing region is averaged over all ringing regions to yield a ringing annoyance score for the whole image. A psychovisual experiment is carried out to measure ringing annoyance subjectively and to validate the proposed metric. The performance of our metric is compared to existing alternatives in literature and shows to be highly consistent with subjective data.
Studying the added value of visual attention in objective image quality metrics based on eye movement data

Current research on image quality assessment tends to include visual attention in objective metrics to further enhance their performance. A variety of computational models of visual attention are implemented in different metrics, but their accuracy in representing human visual attention is not fully proved yet. Thus, to provide more accurate evidence on whether and to what extent visual attention can be beneficial for objective quality prediction, the use of “ground truth” visual attention data is highly desired. In this paper, the data of an eye-tracking experiment are integrated in two objective metrics well-known in literature. Experimental results demonstrate that there is indeed a gain in performance including visual attention in objective metrics. The amount of gain in performance tends to depend on the type of objective metric and image distortion.
Studying the added value of computational saliency in objective image quality assessment

Advances in image quality assessment have shown the potential added value of including visual attention aspects in objective quality metrics. Numerous models of visual saliency are implemented and integrated in different quality metrics; however, their ability of improving a metric's performance in predicting perceived image quality is not fully investigated. In this paper, we conduct an exhaustive comparison of 20 state-of-the-art saliency models in the context of image quality assessment. Experimental results show that adding computational saliency is beneficial to quality prediction in general terms. However, the amount of performance gain that can be obtained by adding saliency in quality metrics highly depends on the saliency model and on the metric.
SIQ288: A saliency dataset for image quality research

Saliency modelling for image quality research has been an active topic in multimedia over the last five years. Saliency aspects have been added to many image quality metrics (IQMs) to improve their performance in predicting perceived quality. However, challenges to optimising the performance of saliency-based IQMs remain. To make further progress, a better understanding of human attention deployment in relation to image quality through eye-tracking experimentation is indispensable. Collecting substantial eye-tracking data is often confronted with a bias due to the involvement of massive stimulus repetition that typically occurs in an image quality study. To mitigate this problem, we proposed a new experimental methodology with dedicated control mechanisms, which allows collecting more reliable eye-tracking data. We recorded 5760 trials of eye movements from 160 human observers. Our dataset consists of 288 images representing a large degree of variability in terms of scene content, distortion type as well as degradation level. We illustrate how saliency is affected by the variations of image quality. We also compare state of the art saliency models in terms of predicting where people look in both original and distorted scenes. Our dataset helps investigate the actual role saliency plays in judging image quality, and provides a benchmark for gauging saliency models in the context of image quality.
Toward a Reliable Collection of Eye-Tracking Data for Image Quality Research: Challenges, Solutions, and Applications

Image quality assessment potentially benefits from the addition of visual attention. However, incorporating aspects of visual attention in image quality models by means of a perceptually optimized strategy is largely unexplored. Fundamental challenges, such as how visual attention is affected by the concurrence of visual signals and their distortions; whether visual attention affected by distortion or that driven by the original scene only should be included in an image quality model; and how to select visual attention models for the image quality application context, remain. To shed light on the above unsolved issues, designing and performing eye-tracking experiments are essential. Collecting eye-tracking data for the purpose of image quality study is so far confronted with a bias due to the involvement of stimulus repetition. In this paper, we propose a new experimental methodology to eliminate such inherent bias. This allows obtaining reliable eye-tracking data with a large degree of stimulus variability. In fact, we first conducted 5760 eye movement trials that included 160 human observers freely viewing 288 images of varying quality. We then made use of the resulting eye-tracking data to provide insights into the optimal use of visual attention in image quality research. The new eye-tracking data are made publicly available to the research community.
A Perceptually Relevant Approach to Ringing Region Detection

An efficient approach toward a no-reference ringing metric intrinsically exists of two steps: first detecting regions in an image where ringing might occur, and second quantifying the ringing annoyance in these regions. This paper presents a novel approach toward the first step: the automatic detection of regions visually impaired by ringing artifacts in compressed images. It is a no-reference approach, taking into account the specific physical structure of ringing artifacts combined with properties of the human visual system (HVS). To maintain low complexity for real-time applications, the proposed approach adopts a perceptually relevant edge detector to capture regions in the image susceptible to ringing, and a simple yet efficient model of visual masking to determine ringing visibility. The approach is validated with the results of a psychovisual experiment, and its performance is compared to existing alternatives in literature for ringing region detection. Experimental results show that our method is promising in terms of both reliability and computational efficiency.
The quest for the integration of visual saliency models in objective image quality assessment: A distraction power compensated combination strategy

Novel research on image quality metrics (IQMs) attempts to further improve their reliability by including visual attention aspects of the human visual system. Literature so far mainly focuses on the extension of a specific IQM with a specific visual saliency model. In this paper, we quest the integration of visual saliency models in IQMs, in terms of its statistical meaningfulness and combination strategy. In the first step an exhaustive evaluation is conducted by integrating twenty state-of-the-art saliency models into eight best-known IQMs for image quality assessment. It demonstrates linearly combining saliency and IQMs yields a statistically significant gain in performance. Based on the statistics, we revisit the combination strategy of saliency and IQMs and propose a new strategy taking into account the distraction power of local distortions. Results show that the proposed combination strategy consistently outperforms the conventionally used linear combination strategy.
Research of travel agencies reputation under two-stage signaling game

The reputation information of travel agencies can provide potential tourists with effective identification signal and can also alleviate adverse selection problems caused by asymmetric information in the travel services market. Therefore, this paper establishes reputation model of two-stage signal game based on two cases that three types of travel agencies ignore the reputation and establish reputation. Next, we do a comparative analysis of travel agencies' utility of two stages in the two cases, ignoring reputation and establishing reputation. Then we can get the intuitive results from numerical experiment. The results show that travel agencies' utility of the first stage will reduce by establishing reputation. But in the second stage, the travel agencies will gain more utility, and the total utility of two-stage will be significantly greater than the total utility of the travel agencies without building reputation.
Studies of a plasma focus operated at higher D/sub 2/ filling pressure

Investigations on the plasma motion of a small Mather-type plasma focus device (20 kV, 18 kJ) operated at a higher D/sub 2/ filling pressure (4.0 Torr) than the optimum one (2.5 Torr) for neutron emission by using laser differential interferometer show that the current sheath (CS) structure and pinching dynamics are the two factors which are responsible for the low and largely deviated neutron yield at this pressure.