Subjective Quality Evaluation Research Articles

Utilization of double contrast-enhancement boost for lower-extremity CT angiography.

We aimed to compare the efficacy of the double contrast enhancement (CE)-boost technique with that of conventional methods to improve vascular contrast attenuation in lower-extremity computed tomography (CT) angiography. This retrospective study enrolled 45 patients (mean age, 70 years; range, 26 to 90 years; 30 males). To generate the CE-boost image, the degree of CE was determined by subtracting the post-contrast CT images from the pre-contrast CT images. The double CE-boost technique involves the application of this CE process twice. Both objective assessments (CT attenuation, noise level, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and image sharpness) and subjective quality evaluations were conducted on three types of images (conventional, CE-boost, and double CE-boost images). Double CE-boost images demonstrated significantly reduced noise in Hounsfield units (HUs) compared with conventional and CE-boost images ( ). CT attenuation values (HUs) were substantially higher in all different locations of the lower extremity with double CE-boost images ( ), as opposed to conventional ( ) and CE-boost images ( ). The SNR and CNR were notably improved in the double CE-boost image compared with both conventional and CE-boost images. Image sharpness analysis of the popliteal artery ( ), anterior tibial artery ( ), and dorsalis pedis artery ( ) revealed consistency across conventional, CE-boost, and double CE-boost images. Subjective image analysis indicated superior ratings for the double CE-boost compared with other types. The implementation of the double CE-boost technique improves image quality by decreasing image noise, increasing CT attenuation, and improving SNR, CNR, and subjective assessment compared with CE-boost and conventional imaging.

Minimal changes in sleep parameters during overnight ambulatory blood pressure monitoring do not affect outcomes.

Ambulatory blood pressure monitoring (ABPM) is considered the gold standard for assessing blood pressure; however, its use may potentially disrupt sleep. Previous studies have produced mixed results on the impact of ABPM on sleep parameters and used actigraphy as the evaluating tool. To date, no studies have investigated the effects of ABPM on sleep parameters evaluated through polysomnography. This study aimed to examine the effects of ABPM on objectively assessed sleep parameters. We evaluated five women and five men (age: 38.0 ± 15.0 years; BMI: 27.0 ± 3.5kg/m²) using full polysomnography over two nights in a sleep laboratory-one night with ABPM and one night without it, with nocturnal assessments every 30min. The order of the conditions was randomized, with intervals between nights ranging from 3 to 10 days. N2 sleep was significantly longer during the night with ABPM compared to the night without it (66.4 ± 12.4% vs. 57.7 ± 11.3%, p < 0.003). Conversely, the apnea-hypopnea index was higher on the night without ABPM (13.1 ± 21.2 vs. 10.5 ± 19.8 events/hour, p < 0.005). Participants did not rate the night with ABPM as worse than the night without, and no significant differences were observed in total sleep time, sleep latency, sleep efficiency, number of awakenings, or time awake after sleep onset. ABPM does not appear to adversely affect significant objective sleep parameters or subjective evaluations of sleep quality.

From Interior Aesthetics to Perceived Indoor Air Quality: A Home Halo Effect

Many odorless and invisible pollutants contribute to poor indoor air quality at home. Previous research has shown that subjective ratings of indoor air quality are biased by a home halo effect. This effect is characterized by the tendency to subjectively assess air quality based on pleasant aspects that are unrelated to its objective quality. We hypothesize that the pleasing aesthetics of a living room image will increase the perceived indoor air quality in the room and influence the intention to improve air quality. In three studies, participants ( n = 1,016) were exposed to an image of a low- or high-aesthetic living room. Perceived self-reported knowledge (SRK) of indoor air quality was surveyed. Results showed that indoor air quality was rated as better in a highly aesthetic living room. Participants with low perceived SRK were less likely to ventilate the low aesthetic living room. Practical implications for communications on indoor air risk and interior design are discussed, especially the integration of knowledge surrounding the aesthetic halo effect into the education and design processes of interior designers to heighten awareness among occupants and prioritize materials minimizing indoor air pollution. For occupants, a necessity for enhanced comprehension of indoor air quality and the adoption of proactive measures is examined through public health policies that address broader concerns surrounding indoor air quality and endorse initiatives for disseminating information related to indoor concerns.

Job Quality, Gender and Generations: A Comparative and Exploratory Analysis of European Countries

This study conducts a comparative analysis of European countries regarding job quality and its relationship with gender and generational cohorts, using data from the 2021 European Working Conditions Survey. The research focuses on the subjective evaluations of job quality by European workers and examines how gender and generations may be related with these perceptions. The study operationalizes job quality using the European Intrinsic Job Quality Index, a multidimensional measure encompassing various aspects such as autonomy, interaction, intensity, and meaningful. The results show relevant variations in Intrinsic Job Quality across European countries. The study also finds that gender and generations play a role in shaping the perception of job quality. The findings of this study contribute to the ongoing debate on the importance of job quality for wellbeing and highlight the need for targeted interventions to improve job quality, particularly taking into account specific groups. The study's results also underscore the importance of considering the subjective evaluations of job quality in policy-making and the need for more comprehensive measures of job quality that capture the diverse experiences of workers across Europe.

Flat-field methods for color image sensors

The research provides a detailed analysis of the primary sources of non-uniformity in color CMOS image sensors. It introduces the fundamental theory of traditional photon conversion and the main objective and subjective quality evaluation metrics for images. The theoretical foundations of traditional correction methods are also discussed. Based on the photon response characteristics of the color image sensor, a method for flat-field correction of color image sensors is proposed by analyzing the grayscale response curves of each pixel. This method addresses the non-linearity issue in pixel photon response caused by crosstalk between pixels and improves the non-uniformity problem in pixel response.

Evaluating audio quality ratings and scene analysis performance of hearing-impaired listeners for multi-track music.

This study assessed musical scene analysis (MSA) performance and subjective quality ratings of multi-track mixes as a function of spectral manipulations using the EQ-transform (% EQT). This transform exaggerates or reduces the spectral shape changes in a given track with respect to a relatively flat, smooth reference spectrum. Data from 30 younger normal hearing (yNH) and 23 older hearing-impaired (oHI) participants showed that MSA performance was robust to changes in % EQT. However, audio quality ratings elicited from yNH participants were more sensitive to % EQT than those of oHI participants. A significant positive correlation between MSA performance and quality ratings among oHI showed that oHI participants with better MSA performances gave higher-quality ratings, whereas there was no significant correlation for yNH listeners. Overall, these data indicate the complementary virtue of measures of MSA and audio quality ratings for assessing the suitability of music mixes for hearing-impaired listeners.

Subjective and objective quality evaluation for industrial images

Recently, the demand for ever-better image processing technique continues to grow in field of industrial scenario monitoring and industrial process inspection. The subjective and objective quality evaluation of industrial images are vital for advancing the development of industrial visual perception and enhancing the quality of industrial image/video processing applications. However, the scarcity of publicly available industrial image databases with reliable subjective scores restricts the development of industrial image quality evaluation (IIQE). In preparation for a vacancy, this article first establishes two industrial image databases (i.e., industrial scenario image dataset (ISID) and industrial process image dataset (IPID)) for assessing IIQE metrics. Furthermore, in order to avoid overwhelming industrial image nuances due to the wavelet subband summation, we then present a novel industrial application subband information fidelity standard (SIFS) evaluation method using the channel capacity of visual signals in wavelet domain. Specifically, we first build a visual signals channel model based on perception process from human eyes to brain. Second, we compute and compare the channel capacity for reference and distorted images to measure the information fidelity in each wavelet subband. Third, we sum over the subbands for information fidelity ratio to obtain the overall quality score. Finally, we fairly compare some up-to-date and our proposed image quality evaluation (IQE) methods in two novelty industrial datasets respectively. Our ISID and IPID datasets are capable of evaluating most IQE metrics comprehensively and paves the way for further research on IIQE. Our SIFS model show a remarkable performance comparing with other up-to-date IQE methods.

Effects of Modified Video-Implemented Script Training for Aphasia in the Three Variants of Primary Progressive Aphasia.

Primary progressive aphasia (PPA) is a neurodegenerative disorder characterized by worsening of speech and/or language. Script training intervention promotes automatized speech production via repeated practice of scripted content. This study evaluated the acceptability, feasibility, and effects of a modified version of Video-Implemented Script Training for Aphasia (VISTA) in the three PPA variants and compared outcomes by intervention modality (teletherapy vs. in person). Thirteen bilingual (Spanish-Catalan) participants were included (semantic variant, n = 5; logopenic variant, n = 5; nonfluent/agrammatic variant, n = 3; teletherapy, n = 7). Using a nonrandomized design, intervention was administered in participants' dominant language. Participants were trained on an individualized script twice per week, over 8 weeks. Performance on measures related to script accuracy, content, and subjective ratings of production quality was evaluated at baseline, immediately post, and at 3 and 6 months post-intervention. No significant differences were observed on the basis of intervention modality. Participants demonstrated significant improvements from pre- to post-intervention in script production, synonym production, keywords, and global quality on the trained script. Maintenance was observed when comparing performance at post-intervention relative to 3- and 6-month follow-up for script and synonym production. Significant improvement in production quality of the untrained topic was observed following intervention. Different patterns of benefit were observed by PPA variant. Modified VISTA was acceptable and effective across the three PPA variants, as evidenced by improvements on a broader array of outcome measures than those previously reported. Findings also provide further support for provision for teletherapy in individuals with PPA. https://doi.org/10.23641/asha.26999326.

Prediction and analysis of interior sound quality of high-speed trains based on XGBoost

This paper primarily investigates the objective quantification of subjective perceptions of sound quality in high-speed trains. The study measures and analyzes the sound quality in high-speed trains under high-speed operating conditions and calculates psychoacoustic parameters. A subjective evaluation of the interior sound quality is conducted using the grading scale method. An Extreme Gradient Boosting (XGBoost) algorithm is utilized to construct a predictive model for sound quality in high-speed trains and get high predictive accuracy for sound quality evaluation. Employing AI-based evaluation models to evaluate the sound quality in high-speed trains offers a more effective method for studying the interior sound quality of high-speed trains.

Sound quality evaluation in special vehicles based on multiple linear regression

The issue of interior sound quality in specialized vehicles has received widespread attention, which could directly affect the riding comfort of the vehicle. This research primarily focuses on the objective quantification of subjective perception of sound qualitySQ in special vehicles. The noise of special vehicles under different working conditions is measured and analyzed, and psychoacoustic parameters and A-weighted sound pressure levels are calculated. The subjective evaluation of interior sound quality was conducted using the grading scale method. A multiple linear regression model is used to establish the correspondence between subjective perceptions and objective parameters. It provides a reference basis for evaluating the sound quality of special vehicles.

Effects of gymnasium envelope and daylight pattern on exercisers’ feedback in a virtual reality

The daylight environment determined by the building envelope significantly influences the exercisers' well-being and perception. However, assessment methods that focus on exercisers' feedback for gymnasium envelope and associated daylight pattern have rarely been explored. Additionally, changing the detailed envelope form is time-consuming and costly in physical environment. The Cave Automated Virtual Environment (CAVE) was employed to investigate exercisers' feedback under a virtual matrix of four ratio of covered shadings on the facade (SFCR) conditions and four skylight (SL) conditions. Feedback from exercisers consisted of subjective and attention quality evaluation. The subjective evaluation was employed to measure psychosocial perception of exercisers and validity of virtual environment, including comprehensive assessment, psychological perception, the Igroup presence questionnaire (IPQ), and the Virtual Reality Sickness Questionnaire (VRSQ). Furthermore, exercisers' attention quality was also evaluated by task efficiency of the attention task and physiological responses including electrodermal activity (EDA) and heart rate variability (HRV). The results indicated that envelope form have significant impact on exercisers' feedback, suggesting that existing gymnasium envelope and irregular daylight pattern were inefficient for improving exercisers' perception. Specifically, better feedback was reported in conditions with SFCR3 (SFCR = 0.7) and SL3 (strip skylight) or SL4 (ring skylight), while the worst feedback was reported in conditions with SL2 (spot skylight), followed by conditions with SFCR1 (SFCR = 0) and SL1 (no-skylight), which were designed to replicate a real gymnasium. The novel assessment method for measuring exercisers' feedback in virtual environment has demonstrated its potential to enhance exercisers’ perception, health, and efficiency.

Dual-energy computed tomography with new virtual monoenergetic image reconstruction enhances prostate lesion image quality and improves the diagnostic efficacy for prostate cancer

BackgroundProstate cancer is one of the most common malignant tumors in middle-aged and elderly men and carries significant prognostic implications, and recent studies suggest that dual-energy computed tomography (DECT) utilizing new virtual monoenergetic images can enhance cancer detection rates. This study aimed to assess the impact of virtual monoenergetic images reconstructed from DECT arterial phase scans on the image quality of prostate lesions and their diagnostic performance for prostate cancer.MethodsWe conducted a retrospective analysis of 83 patients with prostate cancer or prostatic hyperplasia who underwent DECT scans at Meizhou People’s Hospital between July 2019 and December 2023. The variables analyzed included age, tumor diameter and serum prostate-specific antigen (PSA) levels, among others. We also compared CT values, signal-to-noise ratio (SNR), subjective image quality ratings, and contrast-to-noise ratio (CNR) between virtual monoenergetic images (40–100 keV) and conventional linear blending images. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic efficacy of virtual monoenergetic images (40 keV and 50 keV) compared to conventional images.ResultsVirtual monoenergetic images at 40 keV showed significantly higher CT values (168.19 ± 57.14) compared to conventional linear blending images (66.66 ± 15.5) for prostate cancer (P < 0.001). The 50 keV images also demonstrated elevated CT values (121.73 ± 39.21) compared to conventional images (P < 0.001). CNR values for the 40 keV (3.81 ± 2.13) and 50 keV (2.95 ± 1.50) groups were significantly higher than the conventional blending group (P < 0.001). Subjective evaluations indicated markedly better image quality scores for 40 keV (median score of 5) and 50 keV (median score of 5) images compared to conventional images (P < 0.05). ROC curve analysis revealed superior diagnostic accuracy for 40 keV (AUC: 0.910) and 50 keV (AUC: 0.910) images based on CT values compared to conventional images (AUC: 0.849).ConclusionsVirtual monoenergetic images reconstructed at 40 keV and 50 keV from DECT arterial phase scans substantially enhance the image quality of prostate lesions and improve diagnostic efficacy for prostate cancer.

The Impact of Lunch Timing on Nap Quality.

Previous research has established that food intake is a biological regulator of the human sleep-wake cycle. As such, the timing of eating relative to sleep may influence the quality of sleep, including daytime naps. Here, we examine whether the timing of lunch (1 h vs. 2 h interval between lunch and a napping opportunity) impacts the quality of an afternoon nap. Using a randomized within-subject design over two separate experimental sessions (7 days apart), participants (n = 40, mean age = 25.8 years) consumed lunch 1 h and 2 h prior to an afternoon nap opportunity. Polysomnography and subjective self-reports were used to assess sleep architecture, sleepiness levels, and nap quality. Results revealed no significant differences in subjective ratings of sleep quality and sleepiness, or in sleep architecture (total sleep time, sleep efficiency, sleep onset latency, sleep stages) between the 1 h and 2-h lunch conditions. All sleep measures were similar when napping followed eating by either 1 h or 2 h, suggesting that eating closer to nap onset may not negatively impact sleep architecture and quality. Future research should continue to identify conditions that improve nap quality, given the well-documented benefits of naps to reduce sleep pressure and improve human performance.

A noise-reduction algorithm for raw 3D point cloud data of asphalt pavement surface texture

High-precision 3D point cloud data have various analyses and application use cases. This study aimed to achieve a more precise noise reduction of the raw 3D point cloud data of asphalt pavements obtained using 3D laser scanning. Hence, a noise-reduction algorithm integrating improved Gaussian filtering and coefficient of variation was developed. A portable laser scanner was used to collect raw, high-precision 3D point cloud data of surface textures from pavement slab samples prepared with three different types of asphalt mixtures: AC-13, SMA-13, and OGFC-13, as well as asphalt from the test sections of the Yakang Expressway. An improved Gaussian filtering and Gaussian filtering that extracts noise using the coefficient of variation were used to filter out the obvious outlier noise and small-scale burr noise, respectively. Finally, the filtering effect of the proposed algorithm, Gaussian filtering, median filtering, and mean filtering on raw 3D point cloud data of pavement textures was evaluated through subjective visual quality and objective index evaluations. The results showed that the proposed algorithm filters out noise while preserving the micro-texture structure information, outperforming Gaussian filtering, median filtering, and mean filtering.

Research on a Multidimensional Digital Printing Image Quality Evaluation Method Based on MLP Neural Network Regression

High-quality printing is a longstanding objective in the printing and replication industry. However, the methods used to evaluate print quality suffer from subjectivity and multidimensionality, relying on personal preferences and subjective perceptions to assess the quality of printed images, which poses significant limitations. To address these issues, a set of evaluation metrics aimed at assessing the quality of digital printing products is proposed to achieve evaluation results consistent with human visual perception. Given the differing imaging principles of pre-press digital images and post-scan images, these images are first preprocessed to standardize them for comparison. Next, features are extracted in both spatial and frequency domains, and similarity metrics are used to quantify the differences in features between pre-press digital images and post-scan images. Finally, a multilayer perceptron (MLP) neural network regression model is trained to predict the final objective quality scores. Experimental results on two standard databases demonstrate that this metric exhibits high consistency in both subjective and objective quality evaluation metrics for printed image quality assessment and outperforms other metrics in terms of accuracy.

Analyzing the Quality of Distorted Images by the Normalized Mutual Information Measure

This research explores how different types of distorting algorithms impact the Full-Reference image quality assessment, particularly when subjective quality evaluations are incorporated. We draw upon the TID2013 database, which contains 3000 images distorted by 24 distinct algorithms, in conjunction with Mean Opinion Scores (MOS) for quality ratings. We compare the results of Normalized Mutual Information (NMI) for image quality score with W2, based on Weibull distribution, the common PSNR similarity measure and MOS. We advocate for integrating of NMI into the repertoire of image quality assessment metrics.

Investigation in image quality and immediate patient safety using pre-dual-flow injection for low-contrast dose spectral pulmonary artery CT angiography

PurposeThe patient safety of iodine contrast-enhanced pulmonary artery CT angiography (CTPA) is widely concerned. This study aimed to investigate the image quality and immediate patient safety of spectral CTPA using a lower-contrast dose pre-dual-flow injection method. MethodsThis retrospective study included 120 patients with suspected pulmonary embolisms who received spectral CTPA between February and December 2022. Patients were divided into normal contrast injection (Group A, n=60) and pre-dual-flow group (Group B, n=60). CT values of pulmonary arteries (PAs) at different levels, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), arteriovenous separation performance, and beam hardening artifact (BHA) index of two sets of images were measured or calculated. The subjective image quality and immediate patient safety were also scored using the three-point method. ResultsGroup B had a contrast dose reduction by 42.5 % (60 vs. 34.5 mL). Radiation exposure dose was not statistically different between the two groups (P>0.05). CT values of different-level PAs on group B images were higher than those on group A images (P<0.05). Group B images had higher SNR and CNR, better arteriovenous separation between PA trunk and pulmonary vein, and lower BHA index on soft tissue and PA (all P<0.05). For subjective evaluation of image quality, group B had a better score in beam hardening artifact (P<0.05). For immediate patient safety, the score in comfortability was statistically higher in group B, with P<0.05. ConclusionsComparing with the normal injection method, pre-dual-flow spectral CTPA with a lower contrast dose injected results in better image quality and shows potential in patient-safety promotion.

Long COVID-19 Enigma: Unmasking the Role of Distinctive Personality Profiles as Risk Factors.

Background: The COVID-19 (Coronavirus disease 2019) pandemic has prompted extensive research into lingering effects, especially in 'Long COVID' patients. Despite exploration, contributing factors remain elusive; Objective: This study explores the potential link between distinctive personality profiles, particularly type D personality, and an increased risk of Long COVID; Methods: A retrospective cross-sectional study at Tel-Aviv Sourasky Medical Center's Post-COVID clinic analyzed data from 373 Long COVID patients through comprehensive questionnaires covering Long COVID syndrome, Fibromyalgia criteria, personality assessments, social support, and subjective evaluations of cognitive decline, health and life quality. In total, 116 out of 373 patients completed the questionnaire, yielding a 31% participation rate; Results: Cluster analysis revealed two groups, with Cluster 1 (N = 58) exhibiting Type D personality traits while Cluster 2 (N = 56) not meeting criteria for Type D personality. In comparison to Cluster 2, Cluster 1 patients reported heightened anxiety, depression, reduced social support, increased pain symptoms, manifestations of fibromyalgia, cognitive decline, and poor sleep quality, contributing to a diminished quality-of-life perception; Conclusions: findings highlight diverse personality profiles among Long COVID patients, emphasizing the need for tailored care. This approach shows potential for improving Long COVID patient care, aligning with the evolving personalized medicine paradigm.

Deep neural network based distortion parameter estimation for blind quality measurement of stereoscopic images

Stereoscopic/3D image quality measurement (SIQM) has emerged as an active and important research branch in image processing/computer vision field. Existing methods for blind/no-reference SIQM often train machine-learning models on degraded stereoscopic images for which human subjective quality ratings have been obtained, and they are thus constrained by the fact that only a limited number of 3D image quality datasets currently exist. Although methods have been proposed to overcome this restriction by predicting distortion parameters rather than quality scores, the approach is still limited to the time-consuming, hand-crafted features extracted to train the corresponding classification/regression models as well as the rather complicated binocular fusion/rivalry models used to predict the cyclopean view. In this paper, we explore the use of deep learning to predict distortion parameters, giving rise to a more efficient opinion-unaware SIQM technique. Specifically, a deep fusion-and-excitation network which takes into account the multiple-distortion interactions is proposed to perform distortion parameter estimation, thus avoiding hand-crafted features by using convolution layers while simultaneously accelerating the algorithm by using the GPU. Moreover, we measure distortion parameter values of the cyclopean view by using support vector regression models which are trained on the data obtained from a newly-designed subjective test. In this way, the potential errors in computing the disparity map and cyclopean view can be prevented, leading to a more rapid and precise 3D-vision distortion parameter estimation. Experimental results tested on various 3D image quality datasets demonstrate that our proposed method, in most cases, offers improved predictive performance over existing state-of-the-art methods.

WELL-BEING AMONG SENIOR HIGH SCHOOL LEARNERS: A LITERATURE REVIEW

This literature review synthesizes a wide range of scholarly articles and official documents to explore the multifaceted concept of well-being, particularly in the context of education and mental health. Drawing upon theoretical frameworks such as Maslow's hierarchy of needs, ecological systems theory, and positive psychology, the review provides a comprehensive examination of the various dimensions that contribute to individual well-being. Key themes include the importance of addressing physical, social, emotional, and spiritual aspects of well-being within educational settings, as well as the role of holistic wellness programs in promoting overall health and happiness. The review highlights the significance of subjective well-being as a subjective evaluation of an individual's quality of life, emphasizing the need for comprehensive measurement tools that capture both hedonic and eudaimonic aspects of well-being. Furthermore, the review discusses the relationship between well-being and academic achievement, underscoring the reciprocal nature of this association. It also explores the impact of social support networks, emotional intelligence, and coping strategies on well-being outcomes, emphasizing the importance of fostering open communication channels and providing access to support services for students. Moreover, the review addresses emerging challenges and opportunities in the field, such as the growing recognition of mental health literacy as a public health goal and the increasing use of technology, including social media and virtual reality, to assess and enhance well-being. Finally, it discusses relevant policy initiatives, including mental health legislation and educational reforms aimed at promoting student well-being and resilience. Overall, this literature review provides valuable insights into the complex interplay between well-being, education, and mental health, offering implications for research, practice, and policy development in these critical areas.