Video Quality Research Articles

Query-aware multi-scale proposal network for weakly supervised temporal sentence grounding in videos

Recently, weakly supervised temporal sentence grounding in videos (TSGV) has attracted extensive attention because it does not require precise start-end time annotations during training, and it can quickly retrieve interesting segments according to user needs. In weakly supervised TSGV, query reconstruction (QR)-based methods are the current mainstream, and the quality of proposals determines their performance. QR-based methods have two problems in proposal quality. First, a multi-modal global token is usually mapped to proposals with limited duration diversity, making it difficult to capture relevant segments at varying durations in real scenarios. Additionally, Gaussian functions are typically used to generate relatively fixed weights for frames within proposals, which weigh the original video features to generate proposal-specific features. This results in query-irrelevant frames affecting the discrimination of the proposal features. In this study, we propose a query-aware multi-scale proposal network (QMN). Initially, pre-trained encoders are used to extract video and query features. Subsequently, a multi-scale proposal generation module is designed to refine video features guided by queries and diversify the duration of the proposal. This module performs multi-modal interaction and multi-scale modeling to obtain proposals of different durations. Furthermore, to extract discriminative proposal features and enhance the modeling of proposal frame correlation, a query-aware weight generator is constructed to learn frame weights to suppress query-irrelevant frame representations through contrastive learning. Finally, the masked query is reconstructed using the proposal features to select the best proposal. The effectiveness of the proposed QMN is verified through experiments on the Charades-STA and ActivityNet-Captions datasets.

Medical misinformation in social media: Representations of gastrointestinal disorders on a short video platform

Background and Objectives: Short video platforms have become one of the most common methods for disseminating medical information on social media. We analysed gastrointestinal (GI)-related content on TikTok, focusing on the creators’ background, patterns of content utilisation and overall content quality and understandability, using validated metrics to quantify the degree of misinformation. Methods: Twelve hashtags related to common GI conditions on TikTok and the 20 most engaging posts associated with each hashtag were selected for review. The ‘most engaging’ status was determined by the application’s internal algorithm. Six GI board-certified physicians evaluated the videos using two validated instruments: DISCERN quality criteria and Patient Education Materials Assessment Tool (PEMAT-A/V) survey. Results: In total, 253 videos were reviewed with 18.6 billion likes, 191,000 comments, 935,000 saves and 927,000 shares. IBS hashtag had the greatest number of views (2.2 billion), while videos on haemorrhoids had the most likes (4.3 million), comments (59,000) and shares (288,000). Most videos 177/253 (70%) were created by non-healthcare workers. The median video quality was 2/5 on DISCERN Likert-type scale with 23/75 DISCERN score. Of 186 videos, 114 (61.3%) were rated ‘very poor’; 58/186 (31.2%) ‘poor’, 13/186 (7%) ‘fair’ and only one video (0.5%) was rated ‘excellent’ on DISCERN grading system. The median PEMAT-A/V scores were 7/12 (73.9%) for understandability and 1/3 (33.3%) for actionability. Conclusion: Our study highlights significant public interest in common GI disorders. However, most posts lacked substantive information and did not have verifiable sources.

Towards fine-grained adaptive video captioning via Quality-Aware Recurrent Feedback Network

To achieve accurate video captioning, most existing solutions focus on employing auxiliary features to provide complementary generation clues, such as detected target regions, audio speech signals, and in-plane text. However, beyond introducing external information, the potential of using the generated captions has been overlooked. We argue that the intrinsic value lies in the caption text, which can faithfully reflect the video content. To this end, we propose to reuse the captions via a Quality-Aware Recurrent Feedback Network (QARFNet), a model that progressively exploits the supportive power between video features and the generated captions. Specifically, inspired by the backtracking AdaBoost algorithm, we propose to build a recurrent loop structure that recycles the obtained linguistic predictions to refine the input visual features. Referring to the greedy selective nature of the AdaBoost algorithm, we set a quality-aware gate to assess the necessity of forwarding the next loop, lessening the complexity. After the refinement loop, if the confidence score remains below the predefined threshold, we select the output with the highest confidence score to advance the subsequent transformer decoder. To facilitate feedback in each loop, a Multi-level Update Module is constructed before fusing linguistic predictions with video features. This involves extracting nouns and verbs from linguistic predictions to lighten relevant video tokens, achieving self-interactions across recurrent loops. By merging the coarse-grained and gradually refined linguistic prediction features, the video tokens are semantically closer to the desired textual representation. To alleviate the increased calculation caused by the loops, we employ a flat transformer encoder to decrease the complexity. The experimental results on several benchmark datasets confirm the effectiveness of our approach, with superior performance compared to the state-of-the-art.

Dynamic assessment of visual fatigue during video watching: Validation of dynamic rating based on post-task ratings and video features

People watching video displays for long durations experience visual fatigue and other symptoms associated with visual discomfort. Fatigue-reduction techniques are often applied but may potentially degrade the immersive experience. To appropriately adjust fatigue-reduction techniques, the changes in visual fatigue over time should be analyzed which is crucial for the appropriate adjustment of fatigue-reduction techniques. However, conventional methods used for assessing visual fatigue are inadequate because they rely entirely on post-task surveys, which cannot easily determine dynamic changes. This study employed a dynamic assessment method for evaluating visual fatigue in real-time. Using a joystick, participants continuously evaluated subjective fatigue whenever they perceived changes. A Simulator Sickness Questionnaire (SSQ) validated the results, which indicated significant correlations between dynamic assessments and the SSQ across five items associated with symptoms associated with visual discomfort. Furthermore, we explored the potential relationship between dynamic visual fatigue and objective video features, e.g., optical flow and the V-values of the hue/saturation value (HSV) color space, which represent the motion and brightness of the video. The results revealed that dynamic visual fatigue significantly correlated with both the optical flow and the V-value. Moreover, based on machine learning models, we determined that the changes in visual fatigue can be predicted based on the optical flow and V-value. Overall, the results validate that dynamic assessment methods can form a reliable baseline for real-time prediction of visual fatigue.

Unlocking TikTok’s Formula: Video Length, Tags, and Beauty Content Success

With the rapid growth of TikTok in the digital era, beauty influencers have gained significant influence over consumer decisions, particularly through makeup product reviews. TikTok’s short and interactive content style has brought attention to how video characteristics such as length and the number of tags affect engagement metrics like views, likes, and comments. This research explores the impact of these factors using data analysis from a sample of 25 TikTok videos, where the average video length was 2 minutes and 20 seconds, and the top 25% of videos had lengths exceeding 3 minutes and 30 seconds. The findings reveal that longer videos tend to foster deeper engagement by providing more comprehensive reviews, with the top 25% of videos achieving 30% higher engagement than the average. Additionally, a higher number of tags significantly increases video reach. These insights are valuable for influencers and marketers looking to optimize their content strategies, particularly in the beauty industry, where maximizing audience engagement is crucial.

S583 Viewer Engagement and Content Quality in Eosinophilic Esophagitis Videos: Evaluating Academic and Private Contributions on YouTube

S583 Viewer Engagement and Content Quality in Eosinophilic Esophagitis Videos: Evaluating Academic and Private Contributions on YouTube

Assessment of reliability and information quality of youtube videos about dietary recommendations for irritable bowel syndrome

Assessment of reliability and information quality of youtube videos about dietary recommendations for irritable bowel syndrome

YouTube videos on gingival grafting procedures: Content and quality analysis

BackgroundPeriodontal surgery for gingival defects is widely recognized by dental care professionals and researchers for its effectiveness in treating gingival recession and improving oral health outcomes. YouTube (Google LLC) is 1 of the health information sources patients and clinicians use, and assessing its content quality is crucial. The authors aimed to examine the content and quality of YouTube videos on gingival graft procedures. MethodsThe online video streaming platform YouTube was searched using the key word gingival graft. Two independent examiners analyzed a total of 120 videos; a third examiner assessed interrater reliability. Fifty videos that met the inclusion criteria were included in the study. The assessed content topics for these YouTube videos consisted of 13 different categories, and their overall quality was evaluated using the Video Information and Quality Index (VIQI). Statistical analyses were performed using SAS software, Version 9.4 (SAS Institute). ResultsThere were 23 videos in the high-quality content groups and 27 videos in the low-quality content group. Hospitals and universities uploaded most of the included videos. The most commonly discussed topics in the included videos were the patient’s condition (36 [72%]) and area of tissue graft (34 [68%]). The total VIQI score and flow had a significant impact on the overall content score (P < .05). ConclusionsThere was a direct correlation between total VIQI scores and total content scores and an inverse relationship between viewing rate and total content scores. Practical ImplicationsTo ensure patients receive accurate and up-to-date information about treatment, the authors recommend guiding them toward reliable resources by means of providing direct links to trustworthy websites, creating and sharing playlists of reliable educational videos, and offering printed materials with quick-response codes linking to verified sources. These actions will help patients easily access and trust the information they need for their treatment decisions.

S899 The Role of Video Quality in Central Reader Disagreement in Assessment of the Endoscopic Mayo Score in Ulcerative Colitis Trials

S899 The Role of Video Quality in Central Reader Disagreement in Assessment of the Endoscopic Mayo Score in Ulcerative Colitis Trials

Development and dissemination of a series of surgical skills and procedures video tutorials using a novel, low-cost, and sustainable simulation kit (GlobalSurgBox)

IntroductionSurgical simulation and video-based learning are limited in lower-resource settings. We sought to develop and assess a series of surgical tutorials using a low-cost simulator. MethodsWe created 8 surgical skills and procedures videos using low-cost equipment. We assessed video quality using the DISCERN scale and the Global Quality Scale (GQS). ResultsVideos ranged from surgical techniques to complex procedures. We uploaded these to Youtube and included them in the curriculum of a medical school in Rwanda. Excluding the cost of the kit (25 USD), production costs ranged from 2 to 5 USD. All videos scored a mean DISCERN of 2.44 ​± ​1.05 and GQS of 3.06 ​± ​0.90. Generally, these lacked points on providing additional sources of information and addressing areas of uncertainty. ConclusionsThis study addresses the demand for accessible surgical education resources. Using low-cost, standardized materials ensures consistency, democratization of training, and feasibility.

FReD-ViQ: Fuzzy Reinforcement Learning Driven Adaptive Streaming Solution for Improved Video Quality of Experience

FReD-ViQ: Fuzzy Reinforcement Learning Driven Adaptive Streaming Solution for Improved Video Quality of Experience

Qualitative analysis of Portuguese-language YouTube videos about autism spectrum disorders

BackgroundIn the digital age, social media platforms such as YouTube have become significant channels for disseminating health information, including content related to autism spectrum disorder (ASD). The quality and reliability of this information, especially when produced by healthcare professionals, are crucial for public health education and promotion. This study aims the content of Portuguese-language videos about the treatment of ASD on YouTube, produced by healthcare providers from 2019 to 2023, assessing their quality and alignment with evidence-based practices. MethodsA qualitative exploratory descriptive approach was used, with content analysis based on Bardin's method. A total of 41 videos were selected using keywords related to ASD. Transcriptions were analyzed for discussions on treatment approaches, best practices, and professional recommendations according to DSM-V and ICD-10 guidelines. The quality of information was assessed using the DISCERN questionnaire. ResultsThe analysis revealed significant variability in the quality of the information. Videos were categorized into four quality groups based on DISCERN scores: good (n = 6), moderate (n = 11), poor (n = 20), and very poor (n = 4). Good quality videos had the highest engagement metrics and overall quality scores. Common themes identified included defining and understanding ASD, ABA interventions and strategies, family and social impact, skills development, and challenges and solutions. ConclusionWhile some videos provided accurate, evidence-based information, a substantial portion did not meet minimum quality criteria. This highlights the need for improved mechanisms to ensure the dissemination of reliable health information on social media platforms.

STMixer: A One-Stage Sparse Action Detector.

Traditional video action detectors typically adopt the two-stage pipeline, where a person detector is first employed to generate actor boxes and then 3D RoIAlign is used to extract actor-specific features for action recognition. This detection paradigm requires multi-stage training and inference, and the feature sampling is only constrained inside the box, failing to effectively leverage richer context information outside. Recently, several query-based action detectors have been proposed to predict action instances in an end-to-end manner. However, they still lack adaptability in feature sampling and decoding, thus suffering from the issues of inferior performance or slower convergence. In this paper, we propose two core designs for a more flexible one-stage sparse action detector. First, we present a query-based adaptive feature sampling module, which endows the detector with the flexibility of mining a group of discriminative features from the entire spatio-temporal domain. Second, we devise a decoupled feature mixing module, which dynamically attends to and mixes video features along the spatial and temporal dimensions respectively for better feature decoding. Based on these designs, we instantiate two detection pipelines, that is, STMixer-K for keyframe action detection and STMixer-T for action tubelet detection. Without bells and whistles, our STMixer detectors obtain the state-of-the-art results on five challenging spatio-temporal action detection benchmarks for keyframe action detection or action tube detection.

Dual-referenced assistive network for action quality assessment

Action quality assessment (AQA) aims to evaluate the performing quality of a specific action. It is a challenging task as it requires to identify the subtle differences between the videos containing the same action. Most of existing AQA methods directly adopt a pretrained network designed for other tasks to extract video features, which are too coarse to describe fine-grained details of action quality. In this paper, we propose a novel Dual-Referenced Assistive (DuRA) network to polish original coarse-grained features into fine-grained quality-oriented representations. Specifically, we introduce two levels of referenced assistants to highlight the discriminative quality-related contents by comparing a target video and the referenced objects, instead of obtrusively estimating the quality score from an individual video. Firstly, we design a Rating-guided Attention module, which takes advantage of a series of semantic-level referenced assistants to acquire implicit hierarchical semantic knowledge and progressively emphasize quality-focused features embedded in original inherent information. Subsequently, we further design a couple of Consistency Preserving constraints, which introduce a set of individual-level referenced assistants to further eliminate score-unrelated information through more detailed comparisons of differences between actions. The experiments show that our proposed method achieves promising performance on the AQA-7 and MTL-AQA datasets.

Evaluating the quality and reliability of youtube videos on achilles tendinopathy: a comprehensive analysis

Aims: This study evaluates the quality, reliability, and educational value of YouTube videos on Achilles tendinopathy. Methods: A YouTube search using the keyword “Achilles tendinopathy” was conducted on June 20, 2024, using an incognito browser tab. The first 50 English-language videos were analyzed for upload date, duration, views, likes, dislikes, comments, and categorized by source and content. The DISCERN score, Global Quality Score (GQS), and Journal of the American Medical Association (JAMA) score were used to assess video quality and reliability. Statistical analyses included the Shapiro-Wilk test, Kruskal-Wallis test, Mann-Whitney U test, and Spearman test for correlations. Results: Among 50 videos, the average DISCERN score was 42.5, GQS was 3.2, and JAMA score was 2.6, indicating moderate overall quality. Academic physician videos had higher scores. Exercise training videos scored significantly higher in quality assessments. The highest Video Power Index (VPI) was also found in videos by academic physicians. Conclusion: YouTube videos on Achilles tendinopathy provide moderately sufficient information, with higher quality in videos produced by academic physicians and those focusing on exercise training. The study suggests a need for standardized, high-quality educational content on online platforms.

Evaluating the Impact of Mid-Link Bandwidth Constraints on Video Streaming Performance Using NS2

A detailed simulation study of video streaming over a simple best-effort network topology is conducted using Network Simulator 2 (NS2). The simulation involves transmitting a video stream across a simple network topology where the central link's data rate varies to simulate different cases. The network topology consists of : node 0 (sender), node 3 (receiver), and nodes 1 and 2 as intermediate nodes. The study aims to examine the impact of mid-link bandwidth variations on packet loss during video transmission. Specifically, the mid-link connecting nodes experience bandwidth reductions to 10, 8, and 5 Mbps. By adjusting the mid-link bandwidth, we simulate scenarios that typically cause packet drops and measure the resulting packet loss for each bandwidth configuration. The simulation results provide insights into how bandwidth constraints affect video streaming performance, emphasizing the correlation between reduced mid-link capacity and increased packet drop rates. The results indicate that maintaining the video encoding constant while reducing the mid-link bandwidth to half of its required capacity results in an approximately 30% rise in dropped packets and a fivefold increase in end-to-end delay. This significant packet loss severely degrades the video quality, highlighting the importance of adequate mid-link bandwidth for maintaining high-quality transmission.

Assessing quality and reliability of online videos on tachycardia: a YouTube video-based study

BackgroundConsidering the adverse clinical consequences of pathologic tachycardia and the potential anxiety caused by physiological tachycardia in some heathy individuals, it is imperative to disseminate health information related to tachycardia for promotion in early diagnosis and appropriate management. YouTube has been increasingly used to access health care information. The aim of this study is to assess the quality and reliability of English YouTube videos focusing on tachycardia and further delve into strategies to enhance the quality of online health resources.MethodsWe conducted a search using the specific key words “tachycardia” in YouTube online library on December 2, 2023. The first 150 videos, ranked by “relevance”, were initially recorded. After exclusions, a total of 113 videos were included. All videos were extracted for characteristics and categorized based on different topics, sources or contents. Two independent raters assessed the videos using Journal of American Medical Association (JAMA) benchmark criteria, Modified DISCERN (mDISCERN) tool, Global Quality Scale (GQS) and Tachycardia-Specific Scale (TSS), followed by statistical analyses. All continuous data in the study were presented as median (interquartile range).ResultsThe videos had a median JAMA score of 2.00 (1.00), mDISCERN of 3.00 (1.00), GQS of 2.00 (1.00), and TSS of 6.00 (4.50). There were significant differences in JAMA (P < 0.001), mDISCERN (P = 0.004), GQS (P = 0.001) and TSS (P < 0.001) scores among different sources. mDISCERN (P = 0.002), GQS (P < 0.001) and TSS (P = 0.030) scores significantly differed among various contents. No significant differences were observed in any of the scores among video topics. Spearman correlation analysis revealed that VPI exhibited significant correlations with quality and reliability. Multiple linear regression analysis suggested that longer video duration, sources of academics and healthcare professionals were independent predictors of higher reliability and quality, while content of ECG-specific information was an independent predictor of lower quality.ConclusionsThe reliability and educational quality of current tachycardia-related videos on YouTube are low. Longer video duration, sources of academics and healthcare professionals were closely associated with higher video reliability and quality. Improving the quality of internet medical information and optimizing online patient education necessitates collaborative efforts.

Content quality and reliablity of YouTube™ videos as a source of information about good oral hygiene practices in adults.

Good oral hygiene is crucial for preventing dental caries and periodontal diseases. However, proper and regular application of oral hygiene practices requires adequate knowledge. In recent years, the internet has become one of the most popular places to find health-related information, necessitating studies that analyze the quality of the content available online. The purpose of the present study was to analyze the content quality and reliability of YouTube™ videos on the topic of adult oral hygiene practices and to guide oral health care professionals who use this platform for patient education. A YouTube™ search was performed of the most frequent search term, 'dental hygiene'. A total of 150 videos were screened, and 51 were included in the final study. The characteristics, sources, and content of the videos were analyzed using the Global Quality Score (GQS) and DISCERN reliability indices. The IBM SPSS 25 program was used for statistical analyses. Most of the included videos were uploaded by oral health care professionals (63%). GQS revealed only 17.6% of the videos were excellent quality whereas 23.5% of them were poor quality. In the content analysis, 62.7% of the videos were deemed moderately useful. Video duration, total content score, and interaction indices were all significantly higher in the useful and very useful groups compared to the slightly useful group (p = 0.020, p < 0.001, p = 0.040). GQS had a positive, low-medium statistically significant correlation with both video duration and total content scores (r = 0.235, r = 0.517; p < 0.05). DISCERN score also had a positive, low-medium statistically significant correlation with total content score (r = 0.500; p < 0.05). The study concluded that most YouTube™ videos on oral hygiene practices for adults are moderately useful. When using YouTube™ for patient education, oral health care professionals and organizations should be aware of low-quality videos and seek out accurate, useful videos. There is also a need for quality videos with expanded oral health content.

Adaptive QP algorithm for depth range prediction and encoding output in virtual reality video encoding process.

In order to reduce the encoding complexity and stream size, improve the encoding performance and further improve the compression performance, the depth prediction partition encoding is studied in this paper. In terms of pattern selection strategy, optimization analysis is carried out based on fast strategic decision-making methods to ensure the comprehensiveness of data processing. In the design of adaptive strategies, different adaptive quantization parameter adjustment strategies are adopted for the equatorial and polar regions by considering the different levels of user attention in 360 degree virtual reality videos. The purpose is to achieve the optimal balance between distortion and stream size, thereby managing the output stream size while maintaining video quality. The results showed that this strategy achieved a maximum reduction of 2.92% in bit rate and an average reduction of 1.76%. The average coding time could be saved by 39.28%, and the average reconstruction quality was 0.043, with almost no quality loss detected by the audience. At the same time, the model demonstrated excellent performance in sequences of 4K, 6K, and 8K. The proposed deep partitioning adaptive strategy has significant improvements in video encoding quality and efficiency, which can improve encoding efficiency while ensuring video quality.

Evaluation of Videos Related to Vaginal Cone Usage on YouTube as an Online Information Source.

The aim of this study is to examine the quality and content characteristics of educational videos on the use of vaginal cones published on YouTube. Video searches were conducted on the YouTube website using the keyword "usage of vaginal cones". A total of 52 videos were included in the current study. Modified DISCERN (mDISCERN) and Journal of the American Medical Association (JAMA) scales were used to evaluate the reliability of the videos, and the Global Quality Scale (GQS) was used for quality and usefulness. As a result of the content analysis conducted, it was observed that 29 videos were classified as having "poor content" and 23 as "rich content." When we examined the sources of the videos (n = 52), it was found that the majority (58%, n = 30) were produced by nonhealth care sources (medical companies and nonhealth professionals). With statistical significance in mDISCERN and GQS (p = 0.014, p = 0.036), physiotherapists were found to have the highest average scores (4.11 ± 1.05, 3.44 ± 0.73) whereas doctors ranked second in the average standard deviation (3.09 ± 1.04, 2.82 ± 0.98). In JAMA, medical companies were found to have the highest average score with statistical significance (p = 0.015) at 3.4 ± 0.74, followed by doctors at 3 ± 1, and physiotherapists at 2.89 ± 0.78 when averages were analyzed. It is clearly evident that there is a need for higher quality and more reliable vaginal cone content database on YouTube. It is important for patients to be guided by health care professionals and informed about quality content criteria in order to access quality, reliable, and useful information.