nils-herrmann/clean_scirepeval huggingface dataset:
Perceptual Annoyance Models for Videos With Combinations of Spatial and Temporal Artifacts

Understanding the perceptual impact of compression artifacts in video is one of the keys for designing better coding schemes and appropriate visual quality control chains. Although compression and transmission artifacts, such as blockiness, blurriness, and packet-loss, appear simultaneously in digital videos, traditionally they have been studied in isolation. In this paper, we report the results of three subjective quality assessment experiments aimed at studying perceptual characteristics of a set of artifacts common in digital videos. With this goal, first, we study the annoyance of each of three artifacts (blockiness, blurriness, and packet-loss) in isolation and then in combination. Based on the subjective evaluations, we design several models of the annoyance caused by the joint presence of these three artifacts on digital video.
A Reliable Methodology to Collect Ground Truth Data of Image Aesthetic Appeal

Recognizing what makes an image aesthetically pleasing is crucial to the effectiveness of many multimedia systems. Several works have attempted to build image aesthetic appeal predictors, and created their own set of ground truth data for the purpose, either by using rated images from photo sharing websites, or by asking a pool of users to rate images in lab or crowdsourcing experiments. Literature has shown that the way these experiments are conducted can influence their results: poor experimental setup can result in poorly reliable outcomes (i.e., highly imprecise aesthetic appeal measures). A question then arises whether the different choices made to collect ground truth of aesthetic appeal data are appropriate. In this paper, we propose a systematic study that looks into how different experimental environments and rating scales used to collect image aesthetic appeal ground truth data influence the reliability and repeatability of aesthetic appeal assessments. Our findings show that discrete and continuous scales with five-point absolute category rating labels yield more reliable results, with the continuous scale being more reliable for abstract images. We also show that image aesthetic appeal assessments could be repeatable across different experimental environments (i.e., lab and crowdsourcing). We finally formulate concrete recommendations to guide the collection of large sets of ground truth data for training models of aesthetic appeal appreciation.
Journey through the crowd: Best practices and recommendations for crowdsourced QoE

Crowdsourcing-based QoE testing is easy to implement and gives quick results at low costs; as a result crowdsourced QoE data is nowadays popular. However, there are common mistakes and pitfalls which are to be kept in mind for ensuring the reliability of such data. In this paper we summarize best practices and recommendations learned through a 4 year journey in Qualinet which have been collected in the Qualinet recommendation paper on crowdsourcing .
The role of visual attention in the aesthetic appeal of consumer images: A preliminary study

Predicting the aesthetic appeal of images is of great interest for a number of applications, from image retrieval to visual quality optimization. In this paper, we report a preliminary study on the relationship between visual attention deployment and aesthetic appeal judgment. In particular, we seek to validate through a scientific approach those simplicity and compositional rules of thumb that have been applied by photographers and modeled by computer vision scientists in computational aesthetics algorithms. Our results provide a confirmation that both simplicity and composition matter for aesthetic appeal of images, and indicate effective ways to compute them directly from the saliency distribution of an image.
The aesthetic appeal of depth of field in photographs

We report here how depth of field (DOF) affects the aesthetic appeal of photographs for different content categories. 339 photographs spanning eight categories were selected from Flickr, Google+, and personal collections. First, we classified the 339 photographs into three levels of depth of field: small, medium, and large. Then, we asked participants to rate the aesthetic appeal of these photographs in random order. We found that aesthetic appeal is only influenced significantly by the content category and by depth of field for animal and sport related photographs. Therefore, we conclude that depth of field should not be regarded as a common criterion for judging aesthetic appeal in different semantic content categories.
Video quality ruler: A new experimental methodology for assessing video quality

In this paper, we propose a subjective video quality assessment method called video quality ruler (VQR) that can be employed to determine the perceived quality of video sequences. The described method is an extension of the ISO 20462, which is a method to assess image quality. The VQR method provides an interface with a set of pictures. The subjects assess the video using these pictures as a scale and compare the subjective perceived video quality with their perceived quality. The pictures are calibrated to form a numerical scale in units of just noticeable differences (JNDs), which allows to analyze and compare both subjective video and image stimuli. To evaluate the effectiveness of the proposed method, we compare the VQR method with a well-used single stimulus (SS) method. The results show that proposed method can be used to quantify the overall video quality with higher efficiency and with a less biased results than the SS method.
On the impact of packet-loss impairments on visual attention mechanisms

This paper reports the results of a psychometric experiment aimed at investigating the visibility and annoyance of packet-loss artifacts, with a focus on understanding to what extent their presence influences viewing behavior. The rationale behind this study is that packet loss artifacts might “distract” visual attention, creating visual saliency themselves, thereby becoming more visible. In turn, higher artifact visibility might impact their annoyance. Our study involved seven videos, compressed at a very high bitrate (thus free of spatial artifacts), impaired by discarding packets at different packet loss ratios. We tracked the observers' eye-movements while (1) they assessed the annoyance of impaired videos and (2) looked freely at pristine videos. Our results show that the viewing behavior significantly changes from pristine to impaired videos. This change is related to both properties of the video and to the annoyance of the artifacts presented.
Blind Image Quality Assessment via Vector Regression and Object Oriented Pooling

This paper presents an effective method based on vector regression and object oriented pooling for blind image quality assessment. Unlike previous models that map the extracted features directly to a quality score, the proposed vector regression framework yields a vector of belief scores for the input image. We explore the uncertainty factors in quality assessment and design the belief scores to measure the confidences of an image to be assigned to the corresponding quality grades. Moreover, we propose an object oriented pooling strategy to further improve the performance by incorporating semantic information of image contents. According to this strategy, regions occupied by objects will be assigned more weights in the pooling phase, leading to a more accurate quality assessment. Extensive experiments on benchmark datasets demonstrate that our approach achieves state-of-the-art performance and shows a great generalization ability.
Predicting Mood from Punctual Emotion Annotations on Videos

A smart environment designed to adapt to a user's affective state should be able to decipher unobtrusively that user's underlying mood. Great effort has been devoted to automatic punctual emotion recognition from visual input. Conversely, little has been done to recognize longer-lasting affective states, such as mood. Taking for granted the effectiveness of emotion recognition algorithms, we propose a model for estimating mood from a known sequence of punctual emotions. To validate our model experimentally, we rely on the human annotations of two well-established databases: the VAM and the HUMAINE. We perform two analyses: the first serves as a proof of concept and tests whether punctual emotions cluster around the mood in the emotion space. The results indicate that emotion annotations, continuous in time and value, facilitate mood estimation, as opposed to discrete emotion annotations scattered randomly within the video timespan. The second analysis explores factors that account for the mood recognition from emotions, by examining how individual human coders perceive the underlying mood of a person. A moving average function with exponential discount of the past emotions achieves mood prediction accuracy above 60 percent, which is higher than the chance level and higher than mutual human agreement.
Augmenting Blind Image Quality Assessment Using Image Semantics

Blind image quality assessment aims at predicting the perceptual quality of a distorted image without using information from its pristine version. So far, quality prediction has been mostly approached by modeling processes underlying human sensitivity to visual impairments, assuming quality to be depending on impairment visibility only. This assumption is limiting, as it does not adopt a holistic view of image viewing experience, which involves understanding and interpreting of content, in addition to perception. We propose to integrate blind image quality metrics with semantic information to account for the relationship between impairments and content recognition and understanding in image viewing experience. We first report on a subjective study that shows that image semantics influences perceptual quality. We then integrate several existing blind image quality metrics with semantic information, and show that this brings significant improvement in their accuracy in predicting perceptual quality.
Image quality and visual attention interactions: Towards a more reliable analysis in the saliency space

Understanding bottom-up and top-down visual attention mechanisms related to visual quality perception can be greatly beneficial for the design of effective objective quality metrics. Subjective studies based on eye-movement tracking have been recently published that try to get more insight in these interactions. However, it is still not easy to find coherence across their results, also due to the different methodologies adopted to analyze eye-tracking data. In this paper we propose a robust methodology to measure differences between eye-tracking data collected under different experimental conditions. The proposed method takes into account inter-observer variability and content effects, producing results that give an accurate insight in attention variations.
An efficient no-reference metric for perceived blur

This paper presents an efficient no-reference metric that quantifies perceived image quality induced by blur. Instead of explicitly simulating the human visual perception of blur, it calculates the local edge blur in a cost-effective way, and applies an adaptive neural network to empirically learn the highly nonlinear relationship between the local values and the overall image quality. Evaluation of the proposed metric using the LIVE blur database shows its high prediction accuracy at a largely reduced computational cost. To further validate the performance of the blur metric on its robustness against different image content, two additional quality perception experiments were conducted: one with highly textured natural images and one with images with an intentionally blurred background1. Experimental results demonstrate that the proposed blur metric is promising for real-world applications both in terms of computational efficiency and practical reliability.
Annoyance models for videos with spatio-temporal artifacts

Although compression and transmission artifacts are likely to appear simultaneously in digital videos, their annoyance has been traditionally studied and modeled in isolation. So, while blockiness, blurriness, and packet-loss metrics exist, hardly any attempt has been made at modeling their joint impact on visual perception. In this paper, we evaluate the perceptual impact of those three artifacts on video quality. Based on data from three different experiments, in which a pool of participants evaluated videos impaired with packet-loss, blurriness and blockiness (in isolation and in combination), we analyze how the different artifacts combine to produce annoyance and propose several models for predicting the annoyance of videos impaired with combinations of packet-loss, blurriness and blockiness.
Channel estimation and optimal pilot signals for universal filtered multi-carrier (UFMC) systems

We propose channel estimation algorithms and pilot signal optimization for the universal filtered multi-carrier (UFMC) system based on the comb-type pilot pattern. By considering the least square linear interpolation (LSLI), discrete Fourier transform (DFT), minimum mean square error (MMSE) and relaxed MMSE (RMMSE) channel estimators, we formulate the pilot signals optimization problem by minimizing the estimation MSE subject to the power constraint on pilot tones. The closed-form optimal solutions and minimum MSE are derived for LSLI, DFT, MMSE and RMMSE estimators.
An 8-channel phased array for carotid artery magnetic resonance imaging at 3T

In order to obtain high resolution image of vessel wall of carotid artery, an 8-channel carotid coil is fabricated for 3T magnetic resonance imaging in this study. The phantom experiments and in-vivo studies are implemented in order to validate the performance of the fabricated 8-channel carotid coil. The high resolution images are obtained with the fabricated 8-channel carotid coil in clinical examination for in vivo experiment, showing its potential for the clinical diagnosis.
3D total variation hyperspectral compressive sensing using unmixing

To reduce the huge resource consumption in the hyperspectral imaging and transmission, this paper proposes a high-performance compression method. Specially, a novel 3D total variation prior is imposed on abundance fractions of end-members. In this method, compressed data is obtained by a random observation matrix in a compressive sensing way. Based on the hyperspectral linear mixed model and known endmembers, abundance fractions are estimated by an augmented Lagrangian method with the devised prior and then the original data is reconstructed. Extensive experimental results demonstrate the superiority of the proposed method to several state-of-art methods.
An Overview of Mobile Devices Security Issues and Countermeasures

Mobile security draws more attention while the mobile device gains its popularity. Malwares just like viruses, botnet and worms, become concerns since the frequently leakage of personal information. This paper investigates malicious attacks through Bluetooth and malwares in different operating systems of mobile devices such as Blackberry OS, iOS, Android OS and Windows Phone. Besides, countermeasures of vulnerability are also discussed to protect the security and privacy of mobile devices.
A comparative study on quality assessment of high resolution fingerprint images

High resolution fingerprint images have been increasingly used in fingerprint recognition. They can provide more fine features (e.g. pores) than standard fingerprint images to improve the recognition accuracy. It is however still an open issue whether or not existing quality assessment methods are suitable for high resolution fingerprint images. This paper compares some typical quality indexes by analyzing the correlation between them and their prediction ability on minutia-based and pore-based high resolution fingerprint recognition accuracy. Experimental results show that the indexes based on ridge orientation are more effective for high resolution fingerprint recognition systems.
A novel signal reconstructing method for radar targets

In this paper, a novel signal reconstructing method for radar targets is proposed based on the attributed scattering center model. By extracting the attributed parameters, the large amount of target data can be represented by small amounts of attributed parameters. In this way, the data amount has been compressed sharply, which releases the computer memory for storage. After extraction, a target discriminating method is presented by applying a CFAR threshold to the energy of extracted attributed scattering centers, by which, weak distributed scattering centers with relatively high energy in total can be discriminated from noise under low SNRs. Experimental results validate the effectiveness of the signal reconstructing capability of the proposal.
An Improved Position Sensor-Less Estimator for Vector Control of IPMSM

In this paper, a full order state observer fit for the speed and rotor position is proposed. It can obtain the good tracking performance in the full speed region. In the low speed region, it adopts the high frequency injection method, thus obtains the corresponding high frequency current response which includes the rotor position information, after the band-pass filter and low-pass filter, the signal direct proportional to the position error can be acquired, and get the observed position and speed values in the low speed region. In the mid and high speed region, it uses the improved H8 flux observer to observe the rotor position. In contrast to the scheme proposed in , the model proposed in the paper is simple, the computation is relatively small and the range of application is much wider.
3D Digital City Constructing Methods for People Behavior Study

Based on the concept of human-oriented geographic information, we propose a digital city constructing framework for people behavior study in this paper. Two factors, the digital human and the digital city that they live in should be considered when constructing a digital city. It’s very important to make the digital human and the digital city spatially matched; they should be aware of the behavior of each other and take responses appropriately. Around this framework, we focus our research on the methodology and key technologies to model the digital city and the digital human based on real data. We also do some preliminary explorations in the field of mutual responses between the digital city and the digital human. Keywordspeople behavior; digital city; Agent; Virtual Reality; GIS
An Improved Ant Colony Algorithm for Path Optimization in Emergency Rescue

The ant colony algorithm is widely applied to optimize the complex problems in many fields with its features of being robust, parallel, flexible, demanding no artificial interference, and accurate. This paper discusses the application of the colony algorithm in the path search of the earthquake emergency rescue. We first construct a mathematical model for emergency rescue based on the earthquake disasters. Then we propose an improved ant colony algorithm for the rescue path searching optimization according to the specific characteristics of the mode, and the global iterative update strategy is used with limiting the rescue entries. Experiments show that the proposed algorithm can overcome the shortcomings of conventional algorithms such as slow convergence, easily trapped in local optimum, and demonstrating high and flexibility performance.
Differences Between Collection 4 and 5 MODIS Ice Cloud Optical/Microphysical Products and Their Impact on Radiative Forcing Simulations

This paper reports on the comparison of two latest versions (collections 4 and 5) of ice cloud products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. The differences between the bulk optical properties of ice clouds used in collections 4 and 5 and the relevant impact on simulating the correlation of the bidirectional reflection functions at two MODIS bands centered at 0.65 (or 0.86) and 2.13 mum are investigated. The level-3 MODIS ice cloud properties (specifically, ice cloud fraction, optical thickness, and effective particle size in this paper) from the collection 4 and 5 datasets are compared for a tropical belt of 30deg S-30deg N. Furthermore, the impact of the differences between the MODIS collection 4 and 5 ice cloud products on the simulation of the radiative forcing of these clouds is investigated. Over the tropics, the averaged ice cloud fraction from collection 5 is 1.1% more than the collection 4 counterpart, the averaged optical thickness from collection 5 is 1.2 larger than the collection 4 counterpart, and the averaged effective particle radius from collection 5 is 1.8 mum smaller than the collection 4 counterpart. Moreover, the magnitude of the differences between collection 5 and 4 ice cloud properties also depends on the surface characteristics, i.e., over land or over ocean. The differences of these two datasets (collections 4 and 5) of cloud properties can have a significant impact on the simulation of the radiative forcing of ice clouds. In terms of total (longwave plus shortwave) cloud radiative forcing, the differences between the collection 5 and 4 results are distributed primarily between -60 and 20 W ldr m-2 but peak at 0 W ldr m-2.
A 5-GHz inductor-noise cancelling receiver with 1.8 dB noise figure in 65nm LP CMOS

In this paper, a novel receiver architecture with inductor-noise cancellation technique is presented. The proposed receiver employs two separate down-conversion paths driven by I/Q LOs respectively, and the noise of on-chip gate inductor of common-source LNA is cancelled at the baseband output, without additional penalty on power consumption, while the signal is in-phase and strengthened. The noise figure is therefore significantly improved versus prior arts. A demo 5-GHz receiver employing the proposed architecture is designed and implemented in a 65-nm low power CMOS process. Measured result shows a noise figure of 1.8 dB at 5 GHz band, while consuming only 95 mW of power from a 1.2 V supply.
Alamouti Code Assisted V-BLAST (ACAV): A New Space-Time Architecture

We present Alamouti code assisted V-BLAST (ACAV), a new space-time architecture for fading multiple- input multiple-output (MIMO) channels. The transmission design is simple and flexible: two antennas are used to transmit signals the same as Alamouti code, while the other antennas are used to transmit signals similar to the popular V-BLAST. For maximum-likelihood decoding, the analysis shows that ACAV can achieve a certain coding gain over V-BLAST. To reduce the receiver complexity, the (ordered) successive interference cancellation decoding is introduced: the receiver first decodes the two data streams related to Alamouti code, and then decode the other data streams successively. The estimation for the first two decoded data streams is reliable enough to decrease the error propagation to the next decoding stages, resulting in much higher diversity order and lower symbol error rate with about the same computational complexity compared to V-BLAST. The simulation results reveal that ACAV significantly outperforms V-BLAST.