nils-herrmann/clean_scirepeval huggingface dataset:
Augmented Reality Based Bee Drift Analysis: A User Study

We present HoloBee, a system for visual analytics of bee drift data using a head-mounted augmented reality interface (Microsoft HoloLens). HoloBee facilitates interactive manipulation and exploration of the bee activity data on 3D geo- spatial terrains to gain insights into the drifting behaviour of honey bees in their natural habitat. We conducted a user study to compare the task performance and user experience of HoloBee with a conventional desktop application. The results show that HoloBee allows for solving tasks in a similar time and accuracy compared to the desktop interface, despite participants' having significantly more experience interacting with a desktop interface. We received positive feedback from a subset of participants on the natural and intuitive interactions that HoloBee provides, while others preferred using the desktop application. We discuss the implications and limitations of these results and provide guidance for future work in this direction.
Visual attention modelling for subjective image quality databases

The modelling of perceptual image quality metrics has experienced increased effort in recent years. In order to allow for model design, validation, and comparison, a number of subjective image quality databases has been made available to the research community. Most metrics that were designed using these databases assess the quality uniformly over the whole image, not taking into account stronger attention to salient regions of an image. In order to facilitate incorporation of visual attention (VA) into objective metric design we have conducted an eye tracking experiment. The experiment and its outcomes will be explained in detail in this paper. The actual gaze data recorded in the experiment is publicly available in order to facilitate and encourage VA modelling for image quality metrics.
Perceptual-based Quality Metrics for Image and Video Services: A Survey

The accurate prediction of quality from an end-user perspective has received increased attention with the growing demand for compression and communication of digital image and video services over wired and wireless networks. The existing quality assessment methods and metrics have a vast reach from computational and memory efficient numerical methods to highly complex models incorporating aspects of the human visual system. It is hence crucial to classify these methods in order to find the favorable approach for an intended application. In this paper a survey and classification of contemporary image and video quality metrics is therefore presented along with the favorable quality assessment methodologies. Emphasis is given to those metrics that can be related to the quality as perceived by the end-user. As such, these perceptual-based image and video quality metrics may build a bridge between the assessment of quality as experienced by the end-user and the quality of service parameters that are usually deployed to quantify service integrity.
Error sensitivity analysis for wireless JPEG2000 using perceptual quality metrics

Quality assessment of mobile and wireless multimedia services including image and video applications has gained increased attention in recent years as a means of facilitating efficient radio resource management. In particular, approaches that utilize perceptual-based metrics are becoming more dominant, as conventional fidelity metrics such as the peak signal-to-noise ratio (PSNR) may not correlate well with quality as perceived by the human observer. In this paper, we focus on the error sensitivity analysis for images given in the wireless JPEG2000 (JPWL) format using perceptual quality metrics. Specifically, the perceptual quality improvements obtained by progressively decoding an increasing number of image packets are examined. It is shown that the considered perceptual quality metrics exploiting structural image features may accompany or replace the PSNR-based error sensitivity description (ESD) marker segment in the wireless JPEG2000 standard. This addition will increase the effectiveness of the ESD marker segment as it facilitates the communication of reduced-reference information about the image quality from the transmitter to the receiver. In addition, the proposed approach can be used to guide the design of preferential error control coding schemes, link adaptation techniques, and selective retransmission of packets with respect to their contribution to overall quality as perceived by humans.
Visual Attention in Quality Assessment

Perceptual quality metrics are widely deployed in image and video processing systems. These metrics aim to emulate the integral mechanisms of the human visual system (HVS) to correlate well with visual perception of quality. One integral property of the HVS is, however, often neglected: visual attention (VA) . The essential mechanisms associated with VA consist mainly of higher cognitive processing, deployed to reduce the complexity of scene analysis. For this purpose, a subset of the visual information is selected by shifting the focus of attention across the visual scene to the most relevant objects. By neglecting VA, perceptual quality models inherently assume that all objects draw the attention of the viewer to the same degree.
Modelling saliency awareness for objective video quality assessment

Existing video quality metrics do usually not take into consideration that spatial regions in video frames are of varying saliency and thus, differently attract the viewer's attention. This paper proposes a model of saliency awareness to complement existing video quality metrics, with the aim to improve the agreement of objectively predicted quality with subjectively rated quality. For this purpose, we conducted a subjective experiment in which human observers rated the annoyance of videos with transmission distortions appearing either in a salient region or in a non-salient region. The mean opinion scores confirm that distortions in salient regions are perceived much more annoying. It is shown that application of the saliency awareness model to two video quality metrics considerably improves their quality prediction performance.
Addressing RFID Misreadings to Better Infer Bee Hive Activity

This paper proposes a method to address misreadings and consequent inadequacy of radio-frequency identification data for social insect monitoring. Six-month worth field experiment data were collected to demonstrate the application of the method. The data are transformed into a linear combination of the Gaussian model and curve-fitted using an evolutionary algorithm. This results show that the proposed method allows us to improve the quality of data that infer honey bee behavior at the colony level.
Visual attention modeling: Region-of-interest versus fixation patterns

Visual attention (VA) is an integral property of the human visual system. The deployment of a VA model can be beneficial for many image and video applications, such as, compression, transmission, and quality assessment. However, the design of a VA model is highly dependent on the subjective VA data used as ground truth and the application that the model is intended for. In this paper, we discuss two ways of obtaining subjective VA data that can subsequently be used to develop VA models; selective regions-of-interest and visual fixation patterns. The feasibility of both methods will be discussed, in particular, with respect to visual quality assessment.
Quality Assessment of an Adaptive Filter for Artifact Reduction in Mobile Video Sequences

In this paper, we examine an adaptive deblocking deringing filter for mobile video sequences in H.263 format. The considered filter has been designed with reference to the constraints of computational complexity and working memory of mobile terminals. The post filter suggested by the International Telecommunications Union (ITU) in Recommendation H.263 App. III is also included as a reference. Given that fidelity metrics such as the peak signal-to-noise ratio (PSNR) do not necessarily correlate well with video quality as experienced by the user, we consider in this paper objective quality metrics that can incorporate knowledge about the user's perception into the quality assessment. Guidelines for choosing filter parameters in relation to user-perceived video quality are obtained from the numerical results
Visual Assessment of Spatial Data Interpolation

Spatial data is typically inferred between reference points using interpolation techniques and communicated to end users through visualisation. It is not well understood yet how different interpolation techniques perform visually and what visualisation attributes impact on the visual communication of spatial maps. In this paper, we present a study to address these issues. We performed a dedicated experiment in which observers judged visual similarity between interpolated maps and reference maps. We could clearly identify the superior interpolation techniques amongst a set of techniques under consideration. We further found a significant effect for the colour map used for visualisation. No interaction, however, was found between the colour maps and specific interpolation technique comparisons. Response times were recorded as a proxy for judging difficulty and were found to be significantly larger for comparisons amongst the best and worst interpolation techniques.
Varying Spread Fuzzy Regression for Affective Quality Estimation

Design of preferred products requires affective quality information which relates to human emotional satisfaction. However, it is expensive and time consuming to conduct a full survey to investigate affective qualities regarding all objective features of a product. Therefore, developing a prediction model is essential in order to understand affective qualities on a product. This paper proposes a novel fuzzy regression method in order to predict affective quality and estimate fuzziness in human assessment, when objective features are given. The proposed fuzzy regression also improves on traditional fuzzy regression that simulate only a single characteristic with the resulting limitation that the amount of fuzziness is linear correlated with the independent and dependent variables. The proposed method uses a varying spread to simulate nonlinear and nonsymmetrical fuzziness caused by affective quality assessment. The effectiveness of the proposed method is evaluated by two very different case studies, affective design of an electric iron and image quality assessment, which involve different amounts of data, varying fuzziness, and discrete and continuous data. The results obtained by the proposed method are compared with those obtained by the state of art and the recently developed fuzzy regression methods. The results show that the proposed method can generate better prediction models in terms of three fuzzy criteria, which address both predictions of magnitudes and fuzziness.
Subjective quality of SVC-coded videos with different error-patterns concealed using spatial scalability

Degradation of network performance during video transmission may lead to disturbing visual artifacts. Some packets might be lost, corrupted or delayed, making it impossible to properly decode the video data on time at the receiver. The quality of the error-concealment technique, as well as the spatial and temporal position of the artifacts have a large impact on the perceived quality after decoding. In this paper, we use the spatial scalability feature of Scalable Video Coding (SVC) for error-concealment. This enables the transmission of a lower resolution video with a higher robustness, for example using unequal error protection. Under the assumption that only the higher resolution video would be affected, we evaluated the visual impact of packet losses in a large scale subjective video quality experiment using the Absolute Category Rating method. The number of impairments, the duration, and the interval between impairments as well the quality of the encoded lower resolution video are varied in a systematic evaluation. This allows for analyzing the influence of each factor both independently and jointly.
Agile Visual Analytics in Data Science Systems

The practice of data science is ad hoc and agile, where needs and requirements evolve continuously and are resolved through collaboration among stakeholders. To support such practice visual analytics systems need to evolve as the needs and requirements in terms of data, users, tasks, medium, visualization, and interaction capabilities change continuously. In this paper we present a case study and illustrate several dimensions of the requirements in visual analytics. We put forward a vision of a dynamic agile visual analytics process and system model in support of data science, in which the user and system can cooperate to facilitate discovery while requirements change on demand. We argue that such a system needs an underlying language and algebra that defines not only operands and operators for performing visual analytics but also specifies guidelines that take them into account and produce useful visual analytics transformations leading to a specific insight. Our intent is not to present a fully developed system but rather a vision, illustrated through a use case. While the algebra presented here is sufficiently defined to illustrate our viewpoints, further refinement and completion is necessary to facilitate application in a visual analytics system.
Towards an inter-observer analysis framework for multimedia quality assessment

In multimedia quality assessment, observer ratings are typically averaged into mean opinion scores (MOS) to obtain a subjective ground truth for a set of stimuli. Valuable information about individual observer rating behaviour and inter-observer differences is lost that can be useful to improve subjective experiment procedures and quality of experience prediction models. In this paper, we present an inter-observer analysis framework that addresses quality assessment from a different angle, setting the focus on observer differences rather than stimuli differences. The framework consists of procedures for inter-observer analysis as well as the necessary considerations during preparation and post-processing. The aim of this paper is to raise awareness that consideration of MOS alone simplifies quality assessment too much in the context of multimedia quality assessment.
Multiobjective optimization of multiple scale visual quality processing

In this paper, multiobjective optimization is applied to determine the optimal feature weights for a multi-resolution image quality metric. The optimization is conducted with respect to two aims; maximization of quality prediction accuracy and generalisation ability to unknown images. A thresholding of the optimal weights is applied to further reduce computational complexity of the quality metric. A detailed discussion of the trade-off between prediction performance and complexity with respect to the threshold is provided. The metric design and evaluation is supported using mean opinion scores from subjective quality experiments that we conducted in two independent laboratories. Comparison to other state of the art quality metrics reveals the ability of the proposed metric to accurately predict subjective image quality perception.
Perceptual Relevance Based Image Retargeting

The impact of perceptual relevance information on content aware image retargeting is investigated. We integrated fixation density maps and region-of-interest maps into a contemporary image retargeting algorithm to test the hypothesis that the latter result in superior performance given their object level representation. We performed an experimental study with human participants to evaluate the performance gains relative to benchmark conditions. The experiment revealed that the kind of perceptual relevance information, image content, and retargeting ratio all have a strong impact on the overall performance. Recorded response times provided further insight into the difficulty that people experienced when performing the assessment task. These findings are instrumental for the image retargeting research community to further improve their algorithms by augmenting content awareness with perceptual relevance.
Analysing inter-observer saliency variations in task-free viewing of natural images

We analyse variations of saliency between human observers when viewing natural image content. For this purpose, we conducted an eye tracking experiment to record gaze patterns of humans when viewing natural images. The resulting gaze patterns were used to create saliency maps for each observer and image. A correlation analysis is then conducted with the aim to quantify the variability of saliency between the observers and the different images. The results are considered to be a first step towards gaining a better understanding regarding the reliability of saliency maps for natural images.
Comparing two eye-tracking databases: The effect of experimental setup and image presentation time on the creation of saliency maps

Visual attention models are typically designed based on human gaze patterns recorded through eye tracking. In this paper, two similar eye tracking experiments from independent laboratories are presented, in which humans observed natural images under task-free condition. The resulting saliency maps are analysed with respect to two criteria; the consistency between the experiments and the impact of the image presentation time. It is shown, that the saliency maps between the experiments are strongly correlated independent of presentation time. It is further revealed that the presentation time can be reduced without substantially sacrificing the accuracy of the convergent saliency map. The results provide valuable insight into the similarity of saliency maps from independent laboratories and are highly beneficial for the creation of converging saliency maps at reduced experimental time and cost.
Regional attention to structural degradations for perceptual image quality metric design

In this paper, regional attention to structural degradations in images is analyzed to improve perceptual quality prediction performance of objective image quality metrics. Subjective experiments were conducted to identify regions-of-interest for a set of natural images. A region-selective metric design is then applied to four objective image quality metrics which were trained and validated with respect to quality prediction accuracy and generalization to unknown images. For this purpose, data is used from subjective quality experiments conducted at two independent laboratories. It is shown that the region-selective design is highly beneficial for the considered objective image quality metrics, in particular, prediction accuracy can be significantly increased.
Quality Evaluation in Wireless Imaging Using Feature-Based Objective Metrics

This paper addresses the evaluation of image quality in the context of wireless systems using feature-based objective metrics. The considered metrics comprise of a weighted combination of feature values that are used to quantify the extend by which the related artifacts are present in a processed image. In view of imaging applications in mobile radio and wireless communication systems, reduced-reference objective quality metrics are investigated for quantifying user-perceived quality. The examined feature-based objective metrics provide options for controlling the processing complexity and implementation resources required for performing the quality evaluation. Furthermore, the metrics support the scaling of overhead needed for communicating the image features over the wireless link and the accuracy for predicting the image quality
[POSTER] HoloBee: Augmented Reality Based Bee Drift Analysis

In this paper, we introduce HoloBee, a visual analytics system that enables end users to analyse bee drift data using HoloLens as a head mounted augmented reality interface. HoloBee was designed to allow for interactive exploration of bee behaviour and specifically bee drift: the phenomenon of a bee leaving a hive and returning to another hive. For exploration in the bees' natural environment, bee drift is visualised as arcs connecting bee hives positioned in 3D geospatial maps. We describe in detail the design aspects of HoloBee, including command language, system feedback, and information display. We discuss the unique capabilities and limitations of the HoloLens as a guide to other researchers who intend to use it for visual analytics of spatio-temporal scientific data.
Psychophysiology-Based QoE Assessment: A Survey

We present a survey of psychophysiology-based assessment for quality of experience (QoE) in advanced multimedia technologies. We provide a classification of methods relevant to QoE and describe related psychological processes, experimental design considerations, and signal analysis techniques. We summarize multimodal techniques and discuss several important aspects of psychophysiology-based QoE assessment, including the synergies with psychophysical assessment and the need for standardized experimental design. This survey is not considered to be exhaustive but serves as a guideline for those interested to further explore this emerging field of research.
Unequal error protection for wireless multimedia transmission in decode-and-forward relay networks

In this paper, we propose an unequal error protection (UEP) scheme for multimedia transmission over wireless decode-and-forward (DF) relay networks. We prove that relay selection combining (R-SC) mode yields a better error rate performance than all-participate relays, i.e., distributed space-time codes (DSTC) and orthogonal transmission with maximum ratio combining (OT-MRC) at the destination. Hence, we apply the R-SC scheme for transmitting bit sequence with high priority while the cooperative spatial multiplexing scheme is employed for the bit-stream with low priority. With this strategy, we achieve the maximum spatial diversity gain for high priority data and a high spatial multiplexing gain for low priority data. Monte-Carlo simulations and analytical results are shown to validate the proposed UEP scheme.
Perceptual-based quality assessment of error protection schemes for wireless JPEG2000

Designing wireless imaging services with acceptable quality imposes great challenges due to the severe impairments induced by wireless channels, high data rate demands and limited transmission bandwidth. In this paper, we examine different error protection schemes for the JPEG2000 codestream using the error control codes offered by the wireless JPEG2000 (JPWL) standard. In particular, two unequal error protection (UEP) schemes are considered. In the first scheme, only the initial and more important packets of the JPWL codestream are protected while the remaining packets are left without protection. In contrast, the second scheme uses a strong code for the initial packets while a weaker code is used for the tail packets. As quality of visual services may not be captured adequately with traditional link layer metrics, code selection and their performance assessment is based on perceptual quality metrics. Specifically, the LP-norm, structural similarity (SSIM) index and visual information fidelity (VIF) criterion are utilized. Numerical results are provided for a range of system conditions under which UEP outperforms conventional equal error protection in terms of perceptual quality metrics.