Effect of low-degree astigmatism on the objective visual quality

AIM:To evaluate the effect of low-degree astigmatism on objective visual quality through the Optical Quality Analysis System(OQAS).METHODS:This study enrolled 46 participants(aged 23 to 30y,90 eyes)with normal or corrected-to-normal vision.The cylindrical lenses(0,0.5,0.75,1.0,and 1.25 D)were placed at the axial direction(180°,45°,90°,and 135°)in front of the eyes with the best correction to form 16 types of regular low-degree astigmatism.OQAS was used to detect the objective visual quality,recorded as the objective scattering index(OSI),OQAS values at contrasts of 100%,20%,and 9%predictive visual acuity(OV100%,OV20%,and OV9%),modulation transfer function cut-off(MTFcut-off)and Strehl ratio(SR).The mixed effect linear model was used to compare objective visual quality differences between groups and examine associations between astigmatic magnitude and objective visual quality parameters.RESULTS:Apparent negative relationships between the magnitude of low astigmatism and objective visual quality were observed.The increase of OSI per degree of astigmatism at 180°,45°,90°,and 135°axis were 0.38(95%CI:0.35,0.42),0.50(95%CI:0.46,0.53),0.49(95%CI:0.45,0.54)and 0.37(95%CI:0.34,0.41),respectively.The decrease of MTFcut-off per degree of astigmatism at 180°,45°,90°,and 135°axis were-10.30(95%CI:-11.43,-9.16),-12.73(95%CI:-13.62,-11.86),-12.75(95%CI:-13.79,-11.70),and-9.97(95%CI:-10.92,-9.03),respectively.At the same astigmatism degree,OSI at 45°and 90°axis were higher than that at 0°and 135°axis,while MTFcut-off were lower.CONCLUSION:Low astigmatism of only 0.50 D can significantly reduce the objective visual quality.

Perceptual Quality Assessment for Point Clouds:A Survey

A point cloud is considered a promising 3D representation that has achieved wide applications in several fields.However,quality degradation inevitably occurs during its acquisition and generation,communication and transmission,and rendering and display.Therefore,how to accurately perceive the visual quality of point clouds is a meaningful topic.In this survey,we first introduce the point cloud to emphasize the importance of point cloud quality assessment(PCQA).A review of subjective PCQA is followed,including common point cloud distortions,subjective experimental setups and subjective databases.Then we review and compare objective PCQA methods in terms of modelbased and projection-based.Finally,we provide evaluation criteria for objective PCQA methods and compare the performances of various methods across multiple databases.This survey provides an overview of classical methods and recent advances in PCQA.

A Sound Quality Objective Evaluation Method Based on Auditory Peripheral Simulation Model

Based on auditory peripheral simulation model, a new Sound Quality Objective Evaluation (SQOE) method is presented,which can be used to model and analyze the impacts of head, shoulder and other parts of human body on sound wave trans-mission.This method employs the artificial head technique, in which the head related transfer function was taken into account tothe outer ear simulation phase.First, a bionic artificial head was designed as the outer ear model with considering the outersound field in view of theory and physical explanations.Then the auditory peripheral simulation model was built, which mimicsthe physiological functions of the human hearing, simulating the acoustic signal transfer process and conversion mechanismsfrom the free field to the peripheral auditory system.Finally, performance comparison was made between the proposed SQOEmethod and ArtemiS software, and the verifications of subjective and objective related analysis were made.Results show thatthe proposed method was economical, simple, and with good evaluation quality.

Mapping methods for output-based objective speech quality assessment using data mining

Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.The degraded speech is firstly separated into three classes(unvoiced,voiced and silence),and then the consistency measurement between the degraded speech signal and the pre-trained reference model for each class is calculated and mapped to an objective speech quality score using data mining.Fuzzy Gaussian mixture model(GMM)is used to generate the artificial reference model trained on perceptual linear predictive(PLP)features.The mean opinion score(MOS)mapping methods including multivariate non-linear regression(MNLR),fuzzy neural network(FNN)and support vector regression(SVR)are designed and compared with the standard ITU-T P.563 method.Experimental results show that the assessment methods with data mining perform better than ITU-T P.563.Moreover,FNN and SVR are more efficient than MNLR,and FNN performs best with 14.50% increase in the correlation coefficient and 32.76% decrease in the root-mean-square MOS error.

Recent Advances and Challenges of Visual Signal Quality Assessment

While quality assessment is essential for testing, optimizing, benchmarking, monitoring, and inspecting related systems and services, it also plays an essential role in the design of virtually all visual signal processing and communication algorithms, as well as various related decision-making processes. In this paper, we first provide an overview of recently derived quality assessment approaches for traditional visual signals (i.e., 2D images/videos), with highlights for new trends (such as machine learning approaches). On the other hand, with the ongoing development of devices and multimedia services, newly emerged visual signals (e.g., mobile/3D videos) are becoming more and more popular. This work focuses on recent progresses of quality metrics, which have been reviewed for the newly emerged forms of visual signals, which include scalable and mobile videos, High Dynamic Range (HDR) images, image segmentation results, 3D images/videos, and retargeted images.

Non-Intrusive Objective Speech Quality Measurement Based on Fuzzy GMM and SVR for Narrowband Speech

Based on fuzzy Gaussian mixture model （FGMM） and support vector regression （SVR）,an improved version of non-intrusive objective measurement for assessing quality of output speech without inputting clean speech is proposed for narrowband speech.Its perceptual linear predictive （PLP） features extracted from clean speech and clustered by FGMM are used as an artificial reference model.Input speech is separated into three classes,for each a consistency parameter between each feature pair from test speech signals and its counterpart in the pre-trained FGMM reference model is calculated and mapped to an objective speech quality score using SVR method.The correlation degree between subjective mean opinion score （MOS） and objective MOS is analyzed.Experimental results show that the proposed method offers an effective technique and can give better performances than the ITU-T P.563 method under most of the test conditions for narrowband speech.

Study on Quality Objective Evaluation System of Research Institutes

Based on the experience of quality objective evaluation procedures of The Institute of Electrical Engineering, the Chinese Academy of Sciences, the methods and processes are summarized in this paper.

Realtime Object Detection Through M-ResNet in Video Surveillance System

Object detection plays a vital role in the video surveillance systems.To enhance security,surveillance cameras are now installed in public areas such as traffic signals,roadways,retail malls,train stations,and banks.However,monitor-ing the video continually at a quicker pace is a challenging job.As a consequence,security cameras are useless and need human monitoring.The primary difficulty with video surveillance is identifying abnormalities such as thefts,accidents,crimes,or other unlawful actions.The anomalous action does not occur at a high-er rate than usual occurrences.To detect the object in a video,first we analyze the images pixel by pixel.In digital image processing,segmentation is the process of segregating the individual image parts into pixels.The performance of segmenta-tion is affected by irregular illumination and/or low illumination.These factors highly affect the real-time object detection process in the video surveillance sys-tem.In this paper,a modified ResNet model(M-Resnet)is proposed to enhance the image which is affected by insufficient light.Experimental results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video stream.The proposed model shows better results in the metrics like preci-sion,recall,pixel accuracy,etc.,andfinds a reasonable improvement in the object detection.

Evaluating quality of motion for unsupervised video object segmentation

Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance and motion information without evaluating the quality of the optical flow. When poor-quality optical flow is used for the interaction with the appearance information, it introduces significant noise and leads to a decline in overall performance. To alleviate this issue, we first employ a quality evaluation module(QEM) to evaluate the optical flow. Then, we select high-quality optical flow as motion cues to fuse with the appearance information, which can prevent poor-quality optical flow from diverting the network's attention. Moreover, we design an appearance-guided fusion module(AGFM) to better integrate appearance and motion information. Extensive experiments on several widely utilized datasets, including DAVIS-16, FBMS-59, and You Tube-Objects, demonstrate that the proposed method outperforms existing methods.

Associations between the Rorschach Ego Impairment Index and Measures on Intrapsychic and Interpersonal Functioning

The Rorschach Ego Impairment Index-2 (EII-2) has shown considerable validity as a measure of personality disturbance. However, few studies have been conducted on the associations between the EII-2 and measures related to ego strength and interpersonal capacities in mood and anxiety disorder patients. This study examined the strength of associations between the EII-2 and its subcomponents with measures of psychological suitability for psychotherapy, personality functioning, and interpersonal problems. A total of 315 outpatients with mood or anxiety disorders were assessed with the Rorschach Comprehensive System (RCS), comprising the EII-2, the Suitability for Psychotherapy Scale (SPS), the Inventory of Interpersonal Problems (IIP-64), and the Quality of Object Relations Scale (QORS), as part of a pre-treatment evaluation. The relatively weak associations found in the study between the EII-2 and the other measures were mostly in the hypothesized direction and often modified by personality pathology. Of the EII-2 subcomponents, the Good Human Representation (GHR) variable was associated with the SPS. The subcomponent Critical Contents were associated with the IIP and the subcomponent WSum6 with the IIP and QORS. Further research is needed to clarify whether the EII-2 has incremental validity in predicting the treatment outcome and alliance in comparison to interview-based and self-report measures.

Long-term variation and evaluation of air quality across Hong Kong region

Study of Air Quality Objectives(AQOs)and long-term changes of air pollution plays a decisive role in formulating and refining pollution control strategies.In this study,10-year variations of six major air pollutants were analyzed at seven monitoring sites in Hong Kong region.The continuous decrease of annual averaged concentrations of NO_(2),SO_(2),CO,PM_(2.5)and PM_(10)and numbers of days with severe pollution conditions validated the efficiency of the series of air pollution control schemes implemented by the Hong Kong region government.However,there is still a big gap to meet the ultimate targets described by the World Health Organization.Besides,the concentration of O_(3)at roadside and urban stations increased by 135%±25%and 37%±18%from 2011 to 2020,respectively,meanwhile the highest 8 hr averaged O_(3)concentration was observed as 294μg/m^(3)at background station in 2020,which pointed out the increasing ozone pollution in Hong Kong region.There was a great decrease in the annual times of air quality health index(AQHI)laying in“high”,“very high”and“serious”categories from 2011 to 2020 with the decrease rate of 89.70%,91.30%and 89.74%at roadside stations,and 79.03%,95.98%and 72.73%at urban stations,respectively.Nevertheless,the number of days categorized as“high”or above at roadside station was twice more than that in the urban station during the past ten years.Thus,more policies and attentions should be given to the roadside air quality and its adverse health effect to pedestrians on street.

Objective Image Fusion Quality Evaluation Using Structural Similarity

Objective evaluations of fused images are important in comparing the performance of different image fusion algorithms. This paper describes a structural similarity metric that does not use a reference image for image fusion evaluations. The metric is based on the universal image quality index and addresses not only the similarities between the input images and the fused image, but also the similarities among the input images. The evaluation process distinguishes between complementary information and redundant information using similarities among the input images. The metric uses the information classification to estimate how much structural similarity is preserved in the fused image. Tests demonstrate that the metric correlates well with subjective evaluations of the fused images.

A RNN-based objective video quality measurement

Technology used to automatically assess video quality plays a significant role in video processing areas. Because of the complexity of video media, there are great limitations to assess video quality with only one factor. We propose a new method using artificial random neural networks （RNNs） with motion evaluation as an estimation of perceived visual distortion. The results are obtained through a nonlinear fitting procedure and well correlated with human perception. Compared with other methods, the proposed method performs more adaptable and accurate predictions.