Video Quality Research Articles

“Not all travel videos are equal.” Examining the effects of short travel videos on travel intentions and electronic word-of-mouth

Although short-form videos are treated as an effective marketing tool for destinations to attract potential tourists’ attention, what characteristics of short travel videos stimulate potential tourists to involve in destinations and the influence of users’ inertia behavior of viewing short travel videos on its promotion efficiency remains poorly studied. Thus, this study put forward a conceptual model to investigate the power of content novelty of short travel videos and parasocial relationships on users’ involvement behavior and explored the moderating role of inertia between involvement and users’ travel intentions. Partial least squares (PLS) path modeling was applied to evaluate the linkage between variables, in which 212 valid questionnaires were collected via private messages to users who commented on or posted relevant short travel videos. Data results revealed that content novelty and parasocial relationships significantly influence users’ involvement behavior, further predicting travel intention and electronic word-of-mouth. Inertia behavior negatively moderates users’ intention to travel; their browsing behavior primarily serves as a means of passing time, but they do nothing significant. The findings provided a novel viewpoint on the existing destination marketing literature in short travel videos by testing the interactive role of inertia with involvement. Further implications will be discussed below.

Comprehensiveness and Instructional Quality of YouTube Videos on Clinical Record-Keeping Training in Medical Education

Objective: Clinical record-keeping is recognized as a core competency in medical education. This study aimed to evaluate comprehensiveness and instructional quality of videos available on the YouTube platform for teaching clinical record-keeping. Methods: YouTube was searched by using relevant keywords. Based on eligibility criteria, 59 videos were included in the study. Videos were assessed for country of origin, video length, number of likes, dislikes, comments , daily views, like ratio, and video power index. Tools to measure the quality of clinical notes, which are QNOTE and RED Checklist, have been used to assess comprehensiveness of the videos. Instructional quality was assessed using the instructional video quality checklist (IVQC). Results: The comprehensiveness score was 60.4±17.89 (out of 100), while instructional quality score was 11.19±3.61 (out of 27). IVQC scores were significantly higher in the university/professional organizations and academics compared to the others (p

Development of UroSAM: a machine learning model to automatically identify kidney stone composition from endoscopic video.

Introduction Chemical composition analysis is important in prevention counseling for kidney stone disease. Advances in laser technology have made dusting techniques more prevalent, but this offers no consistent way to collect enough material to send for chemical analysis, leading many to forgo this test. We developed a novel machine learning (ML) model to effectively assess stone composition based on intraoperative endoscopic video data. Methods Two endourologists performed ureteroscopy for kidney stones ≥ 10mm. Representative videos were recorded intraoperatively. Individual frames were extracted from the videos and the stone was outlined by human tracing. An ML model, UroSAM, was built and trained to automatically identify kidney stones in the images and predict the majority stone composition: calcium oxalate monohydrate (COM), dihydrate (COD), calcium phosphate (CAP), or uric acid (UA). UroSAM was built on top of the publicly available Segment Anything Model (SAM) and incorporated a U-Net convolutional neural network (CNN). Discussion A total of 78 ureteroscopy videos were collected; 50 were used for the model after exclusions (32 COM, 8 COD, 8 CAP, 2 UA). The ML model segmented the images with 94.77% precision. Dice coefficient (0.9135) and Intersection over Union (0.8496) confirmed good segmentation performance of the ML model. A video-wise evaluation demonstrated 60% correct classification of stone composition. Subgroup analysis showed correct classification in 84.4% of COM videos. A post-hoc adaptive threshold technique was used to mitigate biasing of the model towards COM due to data imbalance - this improved the overall correct classification to 62% while improving the classification of COD, CAP, and UA videos. Conclusions This study demonstrates the successful development of UroSAM, an ML model that precisely identifies kidney stones from natural endoscopic video data. More high quality video data will improve the performance of the model in classifying the majority stone composition.

Information literacy skills of health professions students in assessing YouTube medical education content

IntroductionYouTube is a popular social media video platform used by health professions students for medical education. YouTube videos vary in quality, and students need to be able to evaluate and select high-quality videos to supplement their learning. Evaluating the quality of YouTube videos is an essential information literacy skill, and the Association of College and Research Libraries updated the framework of information literacy to include collaborative platforms such as YouTube. Research is needed to understand and explore the information literacy proficiency of students in the health professions who are using YouTube videos as learning resources.MethodsThis exploratory expert-novice study investigated the information literacy proficiency of students in evaluating the quality of medical education YouTube videos. Students (n = 89) and experts (n = 23) evaluated three preselected medical education YouTube videos of varying quality using the Medical Quality Video Evaluation Tool (MQ-VET).ResultsResults of two-way mixed repeated-measures ANOVA found experts assigned significantly lower ratings to low- and medium-quality videos than students. In other words, students were less information proficient in selecting videos due to overrating source credibility, education quality, and production quality, along with having lower expectations and finding videos more useful.DiscussionThe tendency of students to overrate lower-quality videos raises concerns about their selection of educational content outside structured learning environments. If students select videos to watch outside the classroom, they could unintentionally learn medical skills from low-quality videos. These insights suggest a need for teachers to select only high-quality videos for students and for more focused curricular strategies to enhance students’ abilities in critically assessing YouTube resources for medical education.

Air Handwriting using AI and ML

Air Handwriting using AI and ML

Expanding-Window Zigzag Decodable Fountain Codes for Scalable Multimedia Transmission

In this paper, we present a coding method called expanding-window zigzag decodable fountain code with unequal error protection property (EWF-ZD UEP code) to achieve scalable multimedia transmission. The key idea of the EWF-ZD UEP code is to utilize bit-shift operation and expanding-window strategy to improve the decoding performance of the high-priority data without performance deterioration of the low-priority data. To provide more protection for the high-priority data, we precode the different importance level using LDPC codes of varying code rates. The generalized variable nodes of different importance levels are further grouped into several windows. Each window is associated with a selection probability and a bit-shift distribution. The combination of bit-shift and symbol exclusive-or operations is used to generate an encoded symbol. Theoretical and simulation results on input symbols of two importance levels reveal that the proposed EWF-ZD UEP code exhibits UEP property. With a small bit shift, the decoding delay for recovering high-priority input symbols is decreased without degrading the decoding performance of the low-priority input symbols. Moreover, according to the simulation results on scalable video coding, our scheme provides better basic video quality at a lower proportion of received symbols compared to three state-of-art UEP fountain codes.

Spatial–Temporal Analysis-Based Video Quality Assessment: A Two-Stream Convolutional Network Approach

In system processing, video inevitably suffers from distortion, which leads to quality degradation and affects the user experience. Therefore, it is of great importance to design an accurate and effective objective video quality assessment (VQA) method. In this paper, by considering the multi-dimensional characteristics for video and visual perceptual mechanism, a two-stream convolutional network for VQA is proposed based on spatial–temporal analysis, named TSCNN-VQA. Specifically, for feature extraction, TSCNN-VQA first extracts spatial and temporal features by two different convolutional neural network branches, respectively. After that, the spatial–temporal joint feature fusion is constructed to obtain the joint spatial–temporal features. Meanwhile, the TSCNN-VQA also integrates an attention module to guarantee that the process conforms to the mechanism that the visual system perceives video information. Finally, the overall quality is obtained by non-linear regression. The experimental results in both the LIVE and CSIQ VQA datasets show that the performance indicators obtained by TSCNN-VQA are higher than those of existing VQA methods, which demonstrates that TSCNN-VQA can accurately evaluate video quality and has better consistency with the human visual system.

Audio–visual speech recognition based on regulated transformer and spatio–temporal fusion strategy for driver assistive systems

This article presents a research methodology for audio–visual speech recognition (AVSR) in driver assistive systems. These systems necessitate ongoing interaction with drivers while driving through voice control for safety reasons. The article introduces a novel audio–visual speech command recognition transformer (AVCRFormer) specifically designed for robust AVSR. We propose (i) a multimodal fusion strategy based on spatio–temporal fusion of audio and video feature matrices, (ii) a regulated transformer based on iterative model refinement module with multiple encoders, (iii) a classifier ensemble strategy based on multiple decoders. The spatio–temporal fusion strategy preserves contextual information of both modalities and achieves their synchronization. An iterative model refinement module can bridge the gap between acoustic and visual data by leveraging their impact on speech recognition accuracy. The proposed multi-prediction strategy demonstrates superior performance compared to traditional single-prediction strategy, showcasing the model’s adaptability across diverse audio–visual contexts. The transformer proposed has achieved the highest values of speech command recognition accuracy, reaching 98.87% and 98.81% on the RUSAVIC and LRW corpora, respectively. This research has significant implications for advancing human–computer interaction. The capabilities of AVCRFormer extend beyond AVSR, making it a valuable contribution to the intersection of audio–visual processing and artificial intelligence.

Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

SummaryWith the increase in internet connectivity worldwide, people have resorted to various video streaming applications for entertainment, education, healthcare, and so on. However, this has resulted in huge internet traffic, which is predominantly occurring due to the increased transmission of videos over wireless networks to mobile devices, Transmission of ultra‐high‐definition videos can be effortlessly carried out, although the video quality perceived by the users is normally lesser than the anticipated quality. In video streaming applications, it is a pre‐requisite to provide users with high quality of experience (QoE) but this is generally affected by the dynamic variations in the network conditions. This work presents a novel QoE‐based video delivery system in a collaborative e‐learning platform. Here, a novel deep learning structure recurrent neural network‐long short term memory (RNN‐LSTM) is developed for adaptive bit rate (ABR) selection, thereby providing users with videos of high QoE. Various network, time, and buffer‐based parameters are considered while selecting the bit rate. Additionally, the proposed RNN‐LSTM is assessed for its superiority in providing high QoE videos based on measures, like QoE, buffer size, and bit rate, and is observed to attain values of 0.975, 45.654, and 90.766 b/s, respectively.

Developing an mHealth program to improve HIV care continuum outcomes among young Black gay and bisexual men

BackgroundYoung Black gay and bisexual men (YBGBM) in the United States face significant disparities in HIV care outcomes. Mobile health (mHealth) interventions have shown promise with improving outcomes for YBGBM across the HIV care continuum.MethodsWe developed an mHealth application using human-centered design (HCD) from 2019–2021 in collaboration with YBGBM living with HIV and with HIV service providers. Our HCD process began with six focus groups with 50 YBGBM and interviews with 12 providers. These insights were used to inform rapid prototyping, which involved iterative testing and refining of program features and content, with 31 YBGBM and 12 providers. We then collected user feedback via an online survey with 200 YBGBM nationwide and usability testing of a functional prototype with 21 YBGBM.ResultsFocus groups and interviews illuminated challenges faced by YBGBM living with HIV, including coping with an HIV diagnosis, stigma, need for social support, and a dearth of suitable information sources. YBGBM desired a holistic approach that could meet the needs of those newly diagnosed as well as those who have been living with HIV for many years. Program preferences included video-based content where users could learn from peers and experts, a range of topics, a community of people living with HIV, and tools to support their health and well-being. Providers expressed enthusiasm for an mHealth program to improve HIV care outcomes and help them serve clients. Rapid prototyping resulted in a list of content topics, resources, video characteristics, community features, and mHealth tools to support adherence, retention, goal setting, and laboratory results tracking, as well as tools to help organization staff to support clients. Online survey and usability testing confirmed the feasibility, acceptability, and usability of the content, tools, and features.ConclusionsThis study demonstrates the potential of a video-based mHealth program to address the unique needs of YBGBM living with HIV, offering support and comprehensive information through a user-friendly interface and videos of peers living with HIV and of experts. The HCD approach allowed for continuous improvements to the concept to maximize cultural appropriateness, utility, and potential effectiveness for both YBGBM and HIV service organizations.

Uncertainty model-based edge detection technology in badminton

As virtual reality technology develops, the analysis and processing of video content have become hot spots in the field of computer vision. Video Action Detection aims to locate features in network video, and its research spans many fields, such as computer vision and spatial prediction. In view of the problem of low-efficiency classification models and inaccurate localization of small-scale targets in complex scenes, we propose a novel method to generate candidate intervals for action detection. The action recognition model is adopted to generate the action score sequence on the video time series. We also propose the uncertainty model of the descending pose detection algorithm. The pre-reaction phase generates a candidate list in the form of concatenated videos containing exactly the same pose to detect action poses that are not identical and of non-maximum duration. Experiments with traditional target detection and multiple deep learning models show that the proposed Non-Maximum Suppression algorithm has a strong ability to extract neural network features. Furthermore, compared with traditional ATSS and Faster R-CNN methods, the detection quality and performance are improved by more than 15.2% and 7.8%, respectively. Our method can fully utilize perception information to improve the quality of decision planning and plays a connecting role between perception fusion and decision planning.

Assessment of the Quality of Sinus Elevation with Lateral Window Approach Procedure Videos on YouTube: A Content-quality Analysis.

Although the lateral window approach allows for greater graft material delivery and bone formation, it is more challenging and invasive, prompting keen interest among dentists to master this method. YouTube is increasingly used for medical training; however, concerns regarding the quality of instructional videos exist. This study proposes new criteria for evaluating YouTube videos on maxillary sinus elevation with the aim of establishing standards for assessing instructional content in the field. We sourced 100 maxillary sinus elevation videos from YouTube and, following exclusion criteria, analyzed 65 remaining videos. The video characteristics, content quality, and newly developed criteria were evaluated. Statistical analyses, employing ordinal logistic regression, identified the factors influencing the quality of instructional videos and evaluated the significance of our new criteria. Although video interaction and view rate exhibited positive relations to content quality, they were not significant (P=0.818 and 0.826, respectively). Notably, videos of fair and poor quality showed a significant negative relation (P<0.001). Audio commentary, written commentary, and descriptions of preoperative data displayed positive but statistically insignificant relationships (P=0.088, 0.228, and 0.612, respectively). The comparison of video evaluation results based on the developed criteria with content quality scores revealed significant negative relationships for good, fair, and poor videos (P<0.001, Exp(B)=-4.306, -7.853, -10.722, respectively). Among the various video characteristics, only image quality showed a significant relationship with content quality. Importantly, our newly developed criteria demonstrated a significant relationship with video content quality, providing valuable insights for assessing instructional videos on maxillary sinus elevation and laying the foundation for robust standards.

Online video resources pertaining to cerebral palsy: A YouTube-based quality control study

Online video resources pertaining to cerebral palsy: A YouTube-based quality control study

Characteristics, reliability, and quality of YouTube videos on meralgia paresthetica: a descriptive cross-sectional study.

The purpose of this study was to evaluate YouTube videos on meralgia paresthetica (MP) for reliability, quality, and differences between quality levels. We analyzed 59 videos related to MP. We evaluated several video characteristics, including views, likes, dislikes, duration, and speaker profile. We used view ratio, like ratio, Video Power Index (VPI), Global Quality Scale (GQS), JAMA criteria, and modified DISCERN (mDISCERN) to assess viewer engagement, popularity, educational quality, and reliability. The videos received a total of 4,009,141 views (average 67,951.54), with 25.4% focused on exercise training and 23.7% focused on disease information. Mean scores were mDISCERN 2.4, GQS 2.8, and JAMA 2.1. Physician-led videos had higher mDISCERN scores, while allied health worker-led videos had more views, likes, dislikes, view ratios, and VPI. Poor and high-quality videos differed in views, likes, view ratio, VPI, and duration. Positive correlations existed among mDISCERN, JAMA, and GQS scores, with video duration positively correlated with GQS. The content of YouTube videos discussing diseases significantly influences viewer engagement and popularity. To enhance the availability of valuable content on YouTube, which lacks a peer review process, medical professionals must contribute high-quality educational materials tailored to their target audience.

AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatment

BackgroundSeveral pelvic area cancers exhibit high incidence rates, and their surgical treatment can result in adverse effects such as urinary and fecal incontinence, significantly impacting patients' quality of life. Post-surgery incontinence is a significant concern, with prevalence rates ranging from 25 to 45% for urinary incontinence and 9–68% for fecal incontinence. Cancer survivors are increasingly turning to YouTube as a platform to connect with others, yet caution is warranted as misinformation is prevalent. ObjectiveThis study aims to evaluate the information quality in YouTube videos about post-surgical incontinence after pelvic area cancer surgery. MethodsA YouTube search for "Incontinence after cancer surgery" yielded 108 videos, which were subsequently analyzed. To evaluate these videos, several quality assessment tools were utilized, including DISCERN, GQS, JAMA, PEMAT, and MQ-VET. Statistical analyses, such as descriptive statistics and intercorrelation tests, were employed to assess various video attributes, including characteristics, popularity, educational value, quality, and reliability. Also, artificial intelligence techniques like PCA, t-SNE, and UMAP were used for data analysis. HeatMap and Hierarchical Clustering Dendrogram techniques validated the Machine Learning results. ResultsThe quality scales presented a high level of correlation one with each other (p < 0.01) and the Artificial Intelligence-based techniques presented clear clustering representations of the dataset samples, which were reinforced by the Heat Map and Hierarchical Clustering Dendrogram. ConclusionsYouTube videos on "Incontinence after Cancer Surgery" present a "High" quality across multiple scales. The use of AI tools, like PCA, t-SNE, and UMAP, is highlighted for clustering large health datasets, improving data visualization, pattern recognition, and complex healthcare analysis.

YouTube Videos Are a Moderately Comprehensive, Reliable, and Quality Option to Learn About “Multiple Sclerosis and Sexuality”

Objectives This study aimed to evaluate the performance, comprehensiveness, reliability, and quality of English-language YouTube videos addressing the subject of multiple sclerosis and sexuality. Methods In August 2023, a search was conducted on a computer using the keywords “multiple sclerosis and sexuality,” “multiple sclerosis and sexual health,” “multiple sclerosis and sexual health problems,” and “multiple sclerosis and sexual dysfunction” for this descriptive study. According to predetermined inclusion and exclusion criteria, 38 videos that met the research purpose were examined. The related URLs were recorded. For each video, the following information was collected: content producers, performance with YouTube statistics, comprehensiveness with a form developed by researchers, reliability with Singh’s Reliability Evaluation Form, and quality with Global Quality Scale. Two researchers independently evaluated the videos. Results Eighty-nine and a half percent of the videos contained information presented by professionals. The average number of views was 2699.132 ± 3382.848, the comprehensiveness score was 4.2 ± 1.711, the reliability score was 3.184 ± 1.182, and the quality score was 3.421 ± 1.2. Nearly half (42.2%) contained good and useful information for viewers, and half (50%) had high video quality. The reliability and quality scores of videos containing each item in terms of comprehensiveness were higher compared to videos that did not include that item (p < 0.05). In addition, the videos with higher comprehensiveness scores had higher quality and reliability scores (p < 0.001). Conclusion These results underscore the constrained performance attributes of YouTube videos addressing multiple sclerosis and sexuality, with their content exhibiting a moderate level of comprehensiveness, reliability, and quality. These results may provide a basis for increasing the effectiveness of YouTube videos on multiple sclerosis and sexuality.

Inference Analysis of Video Quality of Experience in Relation with Face Emotion, Video Advertisement, and ITU-T P.1203

This study introduces an FER-based machine learning framework for real-time QoE assessment in video streaming. This study’s aim is to address the challenges posed by end-to-end encryption and video advertisement while enhancing user QoE. Our proposed framework significantly outperforms the base reference, ITU-T P.1203, by up to 37.1% in terms of accuracy and 21.74% after attribute selection. Our study contributes to the field in two ways. First, we offer a promising solution to enhance user satisfaction in video streaming services via real-time user emotion and user feedback integration, providing a more holistic understanding of user experience. Second, high-quality data collection and insights are offered by collecting real data from diverse regions to minimize any potential biases and provide advertisement placement suggestions.

Evaluation of TikTok videos on acute pancreatitis: content quality and reliability analysis

BackgroundAcute pancreatitis (AP) is a common acute digestive system disorder, with patients often turning to TikTok for AP-related information. However, the platform’s video quality on AP has not been thoroughly investigated.ObjectiveThe main purpose of this study is to evaluate the quality of videos about AP on TikTok, and the secondary purpose is to study the related factors of video quality.MethodsThis study involved retrieving AP-related videos from TikTok, determining, and analyzing them based on predefined inclusion and exclusion criteria. Relevant data were extracted and compiled for evaluation. Video quality was scored using the DISCERN instrument and the Health on the Net (HONcode) score, complemented by introducing the Acute Pancreatitis Content Score (APCS). Pearson correlation analysis was used to assess the correlation between video quality scores and user engagement metrics such as likes, comments, favorites, retweets, and video duration.ResultsA total of 111 TikTok videos were included for analysis, and video publishers were composed of physicians (89.18%), news media organizations (13.51%), individual users (5.41%), and medical institutions (0.9%). The majority of videos focused on AP-related educational content (64.87%), followed by physicians’ diagnostic and treatment records (15.32%), and personal experiences (19.81%). The mean scores for DISCERN, HONcode, and APCS were 33.05 ± 7.87, 3.09 ± 0.93, and 1.86 ± 1.30, respectively. The highest video scores were those posted by physicians (35.17 ± 7.02 for DISCERN, 3.31 ± 0.56 for HONcode, and 1.94 ± 1.34 for APCS, respectively). According to the APCS, the main contents focused on etiology (n = 55, 49.5%) and clinical presentations (n = 36, 32.4%), followed by treatment (n = 24, 21.6%), severity (n = 20, 18.0%), prevention (n = 19, 17.1%), pathophysiology (n = 17, 15.3%), definitions (n = 13, 11.7%), examinations (n = 10, 9%), and other related content. There was no correlation between the scores of the three evaluation tools and the number of followers, likes, comments, favorites, and retweets of the video. However, DISCERN (r = 0.309) and APCS (r = 0.407) showed a significant positive correlation with video duration, while HONcode showed no correlation with the duration of the video.ConclusionsThe general quality of TikTok videos related to AP is poor; however, the content posted by medical professionals shows relatively higher quality, predominantly focusing on clinical presentations and etiologies. There is a discernible correlation between video duration and quality ratings, indicating that a combined approach incorporating the guideline can comprehensively evaluate AP-related content on TikTok.

Aligning Correlation Information for Domain Adaptation in Action Recognition.

Domain adaptation (DA) approaches address domain shift and enable networks to be applied to different scenarios. Although various image DA approaches have been proposed in recent years, there is limited research toward video DA. This is partly due to the complexity in adapting the different modalities of features in videos, which includes the correlation features extracted as long-range dependencies of pixels across spatiotemporal dimensions. The correlation features are highly associated with action classes and proven their effectiveness in accurate video feature extraction through the supervised action recognition task. Yet correlation features of the same action would differ across domains due to domain shift. Therefore, we propose a novel adversarial correlation adaptation network (ACAN) to align action videos by aligning pixel correlations. ACAN aims to minimize the distribution of correlation information, termed as pixel correlation discrepancy (PCD). Additionally, video DA research is also limited by the lack of cross-domain video datasets with larger domain shifts. We, therefore, introduce a novel HMDB-ARID dataset with a larger domain shift caused by a larger statistical difference between domains. This dataset is built in an effort to leverage current datasets for dark video classification. Empirical results demonstrate the state-of-the-art performance of our proposed ACAN for both existing and the new video DA datasets.

Objective Video Quality Assessment Method for Object Recognition Tasks

In the field of video quality assessment for object recognition tasks, accurately predicting the impact of different quality factors on recognition algorithms remains a significant challenge. Our study introduces a novel evaluation framework designed to address this gap by focussing on machine vision rather than human perceptual quality metrics. We used advanced machine learning models and custom Video Quality Indicators to enhance the predictive accuracy of object recognition performance under various conditions. Our results indicate a model performance, achieving a mean square error (MSE) of 672.4 and a correlation coefficient of 0.77, which underscores the effectiveness of our approach in real-world scenarios. These findings highlight not only the robustness of our methodology but also its potential applicability in critical areas such as surveillance and telemedicine.