Common Video Research Articles

An Overview of Underwater Vision Enhancement: From Traditional Methods to Recent Deep Learning

Underwater video images, as the primary carriers of underwater information, play a vital role in human exploration and development of the ocean. Due to the optical characteristics of water bodies, underwater video images generally have problems such as color bias and unclear image quality, and image quality degradation is severe. Degenerated images have adverse effects on the visual tasks of underwater vehicles, such as recognition and detection. Therefore, it is vital to obtain high-quality underwater video images. Firstly, this paper analyzes the imaging principle of underwater images and the reasons for their decline in quality and briefly classifies various existing methods. Secondly, it focuses on the current popular deep learning technology in underwater image enhancement, and the underwater video enhancement technologies are also mentioned. It also introduces some standard underwater data sets, common video image evaluation indexes and underwater image specific indexes. Finally, this paper discusses possible future developments in this area.

YouTube as a source of information for water treatments.

The purpose of the study was to investigate the quality and reliability of YouTube videos as a source of information in water treatments. We searched videos on YouTube (www.youtube.com) using the following keywords: “health resort medicine,” “spa treatment,” “spa therapy,” “hydrotherapy,” “thermal medicine,” “balneology,” and “balneotherapy” on June 17th, 2021. The global quality scale (GQS) was used to evaluate the quality of the videos. The assessment of reliability was evaluated using the modified DISCERN tool. Some other video parameters and sources of the videos were also recorded. One hundred twenty-one (121) videos were analyzed. The most common video source was advertisement (46.3%). GQS and modified DISCERN median scores were generally low. They were superior for “hydrotherapy” and “balneotherapy” and were also higher in videos uploaded by health-related persons or organizations (physicians, health-related professionals, and health-related websites). A statistically significant positive correlation was found between investigated parameters (like view ratio, number of likes, video power index, video length) and GQS. Only video length was correlated with modified DISCERN for investigated parameters. The median video power index scores were statistically higher for “spa therapy” and “spa treatment.” The YouTube content linked with water treatments has poor quality and reliability most of time. The hydrotherapy and balneotherapy keywords have the best quality and reliability.We think that designers of water treatment videos should involve health professionals more often so that the content of their video will better explain the details of medical conditions or interventions.The scientific experts should ensure a consensus in terminology to straighten the awareness of water treatments for patients and physicians.

An Exploration of e-Cigarette-Related Search Items on YouTube: Network Analysis.

Backgrounde-Cigarette use among youth is high, which may be due in part to pro–e-cigarette content on social media such as YouTube. YouTube is also a valuable resource for learning about e-cigarette use, trends, marketing, and e-cigarette user perceptions. However, there is a lack of understanding on how similar e-cigarette–related search items result in similar or relatively mutually exclusive search results. This study uses novel methods to evaluate the relationship between e-cigarette–related search items and results.ObjectiveThe aim of this study is to apply network modeling and rule-based classification to characterize the relationships between e-cigarette–related search items on YouTube and gauge the level of importance of each search item as part of an e-cigarette information network on YouTube.MethodsWe used 16 fictitious YouTube profiles to retrieve 4201 distinct videos from 18 keywords related to e-cigarettes. We used network modeling to represent the relationships between the search items. Moreover, we developed a rule-based classification approach to classify videos. We used betweenness centrality (BC) and correlations between nodes (ie, search items) to help us gain knowledge of the underlying structure of the information network.ResultsBy modeling search items and videos as a network, we observed that broad search items such as e-cig had the most connections to other search items, and specific search items such as cigalike had the least connections. Search items with similar words (eg, vape and vaping) and search items with similar meaning (eg, e-liquid and e-juice) yielded a high degree of connectedness. We also found that each node had 18 (SD 34.8) connections (common videos) on average. BC indicated that general search items such as electronic cigarette and vaping had high importance in the network (BC=0.00836). Our rule-based classification sorted videos into four categories: e-cigarette devices (34%-57%), cannabis vaping (16%-28%), e-liquid (14%-37%), and other (8%-22%).ConclusionsOur findings indicate that search items on YouTube have unique relationships that vary in strength and importance. Our methods can not only be used to successfully identify the important, overlapping, and unique e-cigarette–related search items but also help determine which search items are more likely to act as a gateway to e-cigarette–related content.

Video frame deletion detection based on time–frequency analysis

With the emergence of diverse multimedia editing software, a great number of edited or tampered video resources appear on the Internet, some of which can mix with the genuine ones. Digital video authenticity is an important step to make the best use of these video resources. As a common video forgery operation, frame tampering can change the video content and confuse viewers by removing or inserting some specific frames. In this paper, we explore the traces created by compression process and propose a new method to detect frame tampering based on the high-frequency features of reconstructed DCT coefficients in the tampered sequences. Experimental results demonstrate that our proposed method can effectively detect frame tampering operation, and accurately locate the breakpoint of frame tampering in the streams.

Influence of laser beam oscillation on the cutting front geometry investigated by high-speed 3D-measurements

3D measurements of the cutting fronts during laser fusion cutting were performed with and without beam oscillation. The used 3D measurement equipment offers up to 75.000 frames per second and a local resolution less 50 µm. This allows high-speed recording of dynamic changes at the cutting front geometry, followed by a three-dimensional reconstruction. Out of an extensive study was picked out a parameter set for 8 kW laser power and a feed rate of 4.5 m/min cutting 10 mm stainless steel (AISI 304), compared to a cut with additional longitudinal beam oscillation by a frequency of 600 Hz and an amplitude of 0.15 mm. The large bright spots of common high speed videos can be interpreted as melt accumulations, pushed down by the gas pressure. They leave behind cold spots on the cutting front, which are very steep or even have an undercut. The influence of beam oscillation on the cutting front is also clearly visible. In the places where the laser beam is not currently located, the melt and the resulting structures appear to solidify. As a result, melt is not continuously ejected, but rather melt bursts are created with the oscillation frequency.

Neural Reference Synthesis for Inter Frame Coding

This work proposes the neural reference synthesis (NRS) to generate high-fidelity reference block for motion estimation and motion compensation (MEMC) in inter frame coding. The NRS is comprised of two submodules: one for reconstruction enhancement and the other for reference generation. Although numerous methods have been developed in the past for these two submodules using either handcrafted rules or deep convolutional neural network (CNN) models, they basically deal with them separately, resulting in limited coding gains. By contrast, the NRS proposes to optimize them collaboratively. It first develops two CNN-based models, namely EnhNet and GenNet. The EnhNet only uses spatial correlations within the current frame for reconstruction enhancement and the GenNet is then augmented by further aggregating temporal correlations across multiple frames for reference synthesis. However, a direct concatenation of EnhNet and GenNet without considering the complex temporal reference dependency across inter frames would implicitly induce iterative CNN processing and cause the data overfitting problem, leading to visually-disturbing artifacts and oversmoothed pixels. To tackle this problem, the NRS applies a new training strategy to coordinate the EnhNet and GenNet for more robust and generalizable models, and also devises a lightweight multi-level R-D (rate-distortion) selection policy for the encoder to adaptively choose reference blocks generated from the proposed NRS model or conventional coding process. Our NRS not only offers state-of-the-art coding gains, e.g., >10% BD-Rate (Bjøntegaard Delta Rate) reduction against the High Efficiency Video Coding (HEVC) anchor for a variety of common test video sequences encoded at a wide bit range in both low-delay and random access settings, but also greatly reduces the complexity relative to existing learning-based methods by utilizing more lightweight DNNs. All models are made publicly accessible at https://github.com/IVC-Projects/NRS for reproducible research.

News Video Classification Model Based on ResNet-2 and Transfer Learning

A large amount of useful information is included in the news video, and how to classify the news video information has become an important research topic in the field of multimedia technology. News videos are enormously informative, and employing manual classification methods is too time-consuming and vulnerable to subjective judgment. Therefore, developing an automated news video analysis and retrieval method becomes one of the most important research contents in the current multimedia information system. Therefore, this paper proposes a news video classification model based on ResNet-2 and transfer learning. First, a model-based transfer method was adopted to transfer the commonality knowledge of the pretrained model of the Inception-ResNet-v2 network on ImageNet, and a news video classification model was constructed. Then, a momentum update rule is introduced on the basis of the Adam algorithm, and an improved gradient descent method is proposed in order to obtain an optimal solution of the local minima of the function in the learning process. The experimental results show that the improved Adam algorithm can iteratively update the network weights through the adaptive learning rate to reach the fastest convergence. Compared with other convolutional neural network models, the modified Inception-ResNet-v2 network model achieves 91.47% classification accuracy for common news video datasets.

Using TikTok in recovery from substance use disorder

BackgroundThere are many effective treatment options for substance use disorder (SUD), yet most individuals with SUD do not seek formal treatment services. Given the rising popularity of TikTok and need to foster innovative means through which to attract and engage individuals with SUD with treatment, we sought to characterize how TikTok users in SUD recovery are using this platform to bolster their recovery support and/or give hope to others who are struggling with substance use. MethodsOur sample consisted of 82 of the most liked TikTok videos related to attempts to cut down on or abstain from substances and/or strengthen SUD recovery. We employed an iterative process to codebook development resulting in codes for demographics, user-sentiment, video type, and mechanisms of recovery-related behavior change. Videos were independently double-coded and evaluated for inter-rater reliability. ResultsVideo in this sample were heavily viewed, accounting for over 2 million views per video and 325,000 likes on average. Most common video themes were sharing a journey from active SUD to recovery (40.2%) and sharing/celebrating a recovery milestone (37.8%), followed by recurrence of substance use (12.2%). Commonly exemplified mechanisms of recovery-related behavior change included embracing a strong social identity as a person in recovery (81.7%), social support (45.1%), and participation in rewarding alternative activities (39.0%). ConclusionTikTok SUD recovery-focused videos can potentially reach millions with portrayed themes similar to established therapeutic mobilizers and mechanisms. More research is needed to better understand whether digital recovery narratives can effectively normalize experiences of addiction and help-seeking behaviors.

Video summary generation by visual shielding compressed sensing coding and double-layer affinity propagation

Video summary technology based on keyframe extraction is an effective means to rapidly access video content. Traditional video summary generation technology requires high video resolution, which poses a problem as most existing studies have no targeted solutions for videos that are subject to privacy protection. We propose a novel keyframe extraction algorithm for video data in the visual shielding domain, named visual shielding compressed sensing coding and double-layer affinity propagation (VSCS-DAP). VSCS-DAP involves three main steps. First, the video is compressed by compressed sensing technology to provide a visual shielding effect (protecting the privacy of monitored figures), while the data volume is significantly reduced. Then, pyramid histogram of oriented gradients (PHOG) features are extracted from the compressed video to be clustered by the first step affinity propagation (AP) to gain the summaries of the first stage. Finally, the PHOG and Hist fusion features are extracted from the keyframes of the first stage, and they cluster the fused PHOG-Hist features by the second step AP algorithm to obtain the final output summaries. Experimental results obtained on two common video datasets show that our method exhibits advantages including low redundancy and few missing frames, low computational complexity, strong real-time performance, and robustness to vision-shielded video.

Path independent stereo digital image correlation with high speed and analysis resolution

Path independent strategy is introduced into stereo digital image correlation, in which high accuracy is attained through the inverse compositional Gauss-Newton (IC-GN) algorithm with the second and the first order shape functions for stereo matching and temporal matching, respectively. To gain strong adaptibility to large and complex deformation or considerable parallax betweent the two views, two robust methods are developped to estimate the initial guess for the iterative IC-GN algorithm. One is aided by scale invariant feature transform (SIFT) features and the other is aided by epipolar constraint. Powered by parallel computing on GPU, the proposed stereo DIC demonstrates high computation efficiency. Real-time stereo DIC is realized and used to measure the large deformation at common video recording rate (∼24 fps) with high ananlysis resolution (∼6300 points of interest in region of interest).

GlideScope® Versus C-MAC® Video Laryngoscopy in Pediatric Intubation. Does Time Matter?

Background: The emergence of video laryngoscopy in the management of pediatric airways has been invaluable as it has been known that these patients are prone to airway complications. Video laryngoscopes are proven to improve glottic view in both normal and difficult airways in pediatric patients. The time taken to intubate using these devices is inconsistent. Objectives: This study was designed to compare the time to intubate using two common video laryngoscopes, C-MAC®, and GlideScope®, aimed at pediatric patients age 3 - 12 years old. Methods: A Randomized controlled trial was conducted in 65 ASA I or II patients, aged 3 - 12 years old who underwent elective surgery using endotracheal tube. They were divided into group 1 patients who were intubated using C-MAC® video laryngoscope versus group 2 patients who were intubated with GlideScope® video laryngoscope. Laryngoscopists were all anesthetists with experience in both C-MAC® and GlideScope® intubation. Time to intubate and intubation attempts were measured. Any extra maneuver, airway complications, and laryngoscopist satisfaction scores were also recorded. Results: Total time to intubate was significantly longer in GlideScope® group than in C-MAC® group (P < 0.001). Both devices managed to achieve excellent glottic views. The first pass attempt success rate was similar between both devices. There was no difference between requirement of extra maneuvers to assist intubations. There were also no adverse events associated with all the intubations. The satisfaction score of anesthetists was comparable to each other. Conclusions: Even though intubation time using GlideScope® is longer, both devices give excellent glottic view, comparable success intubation, and anesthetists satisfaction score.

Development of 360° virtual reality videos and common videos for cardiac arrest recognition : A sequential mixed methods study

Development of 360° virtual reality videos and common videos for cardiac arrest recognition : A sequential mixed methods study

3dRS, a Web-Based Tool to Share Interactive Representations of 3D Biomolecular Structures and Molecular Dynamics Trajectories.

3D Representation Sharing (3dRS) is a web-based tool designed to share biomolecular structure representations, including 4D ensembles derived from Molecular Dynamics (MD) trajectories. The server offers a team working in different locations a single URL to share and discuss structural data in an interactive fashion, with the possibility to use it as a live figure for scientific papers. The web tool allows an easy upload of structures and trajectories in different formats. The 3D representation, powered by NGL viewer, offers an interactive display with smooth visualization in modern web browsers. Multiple structures can be loaded and superposed in the same scene. 1D sequences from the loaded structures are presented and linked to the 3D representation. Multiple, pre-defined 3D molecular representations are available. The powerful NGL selection syntax allows the definition of molecular regions that can be then displayed using different representations. Important descriptors such as distances or interactions can be easily added into the representation. Trajectory frames can be explored using a common video player control panel. Trajectories are efficiently stored and transferred to the NGL viewer thanks to an MDsrv-based data streaming. The server design offers all functionalities in one single web page, with a curated user experience, involving a minimum learning curve. Extended documentation is available, including a gallery with a collection of scenes. The server requires no registration and is available at https://mmb.irbbarcelona.org/3dRS.

Quality of YouTube Videos on Laparoscopic Pyeloplasty in Children: An Independent Assessment by Two Pediatric Surgeons.

BackgroundYouTube (YT) is the most common video platform accessed by surgical trainees for the preparation of surgery. However, the quality of the YT videos has been questioned time and again. This study was performed to comprehensively assess the quality of the available YT videos on pediatric laparoscopic pyeloplasty (LP).Materials and MethodsThe term “laparoscopic pyeloplasty in children” was searched in YT on June 3, 2021, and ten most-viewed videos on LP were included. The percentage video power index (%VPI), the Journal of American Medical Association (JAMA) benchmark criteria, and the laparoscopic surgery video educational guidelines (LAP-VEGaS) video assessment tool were used to assess the video popularity, the quality of medical information, and the overall quality of the included videos respectively. Videos were defined as acceptable (score of 11 or more) or poor quality (score <11) based on LAP-VEGaS scores. The inter-observer agreement, in terms of the LAP-VEGaS scoring, was observed among two surgeons using the kappa statistics.ResultsThe median values of the %VPI and JAMA scores of the included YT videos were 68.1 (range 0-13570) and 2 (range 1-2) respectively. The median LAP-VEGaS score of these videos was 6.75 (range 2-16.5) with only two videos having acceptable quality. The quality of these videos was poor in 7/9 domains of the LAP-VEGaS tool. A moderate inter-observer agreement (kappa=0.542) was observed in terms of the LAP-VEGaS scores assigned to the videos (p<0.0001).ConclusionA comprehensive assessment of the ten most-viewed YT videos on pediatric LP revealed poor overall quality. The included videos depicted sub-optimal presentation of the medical information and weak conformity to the LAP-VEGaS guidelines.

On the Calculation and Usage of HDR Static Content Metadata

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HDR10 is a common platform-agnostic video format that is widely used for ultrahigh-definition (UHD-1)/4K content distribution. HDR10 includes 10-bit video data formatted for the perceptual quantization (PQ) electro-optical transfer function (EOTF) using color encoding primaries corresponding to International Telecommunication Union - Radiocommunication (ITU-R) BT.2020 (and BT.2100) and includes optional “static metadata.” HDR10 static metadata typically comprises two different types of metadata, mastering display metadata and content metadata. This paper discusses the meaningful calculation and usage of static content metadata for the display and processing of HDR10 content. Outlier values in the high-dynamic-range (HDR) pixel values can exist in content being mastered today and a simple statistical approach is presented to reduce the outlier’s influence on the static content metadata values. Additionally, this paper describes how a display or process can utilize good and reliable maximum content light level (MaxCLL) values to improve the reproduction of creative intent</i> .

La culture vidéoludique au Québec

Comment parle-t-on des jeux vidéo au fil du temps dans la Belle Province? Peut-on parler d’une culture vidéoludique spécifiquement québécoise? Voilà les deux questions auxquelles nous tentons de répondre dans cet article. Prenant notre départ dans le catalogue général de Bibliothèque et Archives nationales du Québec (BAnQ), nous recensons les documents sur le jeu vidéo, nous attachant aux publications ou discours produits par des Québécois à l’intention d’un public québécois entre 1978 et 2018. Au fil de l’argumentation, nous signalons certaines limites du catalogue de BAnQ, discutons de l’accessibilité à la culture du jeu vidéo au Québec et des problèmes que pose sa préservation, et analysons quelques-uns des documents trouvés par le moteur de recherche de BAnQ.

Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations

Audio-visual quality assessment remains as a complex research field. A great effort is being made to understand how visual and auditory domains are integrated and processed by humans. In this work, we analyzed and compared the results of three psychophisical experiments that collected quality and content scores given by a pool of subjects. The experiments include diverse content audio-visual material, e.g., Sports, TV Commercials, Interviews, Music, Documentaries and Cartoons, impaired with several visual (bitrate compression, packet-loss, and frame-freezing) and auditory (background noise, echo, clip, chop) distortions. Each experiment explores a particular domain. In Experiment 1, the video component was degraded with visual artifacts, meanwhile, the audio component did not suffer any type of degradation. In Experiment 2, the audio component was degraded while the video component remained untouched. Finally, in Experiment 3 both audio and video components were degraded. As expected, results confirmed a dominance of the visual component in the overall audio-visual quality. However, a detailed analysis showed that, for certain types of audio distortions, the audio component played a more important role in the construction of the overall perceived quality.

A Systematic Assessment of YouTube Content on Femoroacetabular Impingement: An Updated Review.

Background:Online video-sharing platforms such as YouTube have become popular sources of medical information for patients. However, concern exists regarding the quality of such non–peer reviewed content. In fact, a previous investigation found the majority of YouTube information related to femoroacetabular impingement (FAI) to be of poor quality.Purpose:To provide an updated assessment of the quality of FAI-related videos available on YouTube.Study Design:Cross-sectional study.Methods:The terms FAI, femoroacetabular impingement, and hip impingement were searched on YouTube, and exclusion criteria were applied to the first 100 results for each term. The diagnostic and treatment content of each video was graded and assigned a quality assessment rating based on a previously used rubric. Video characteristics (e.g. duration, views, “likes”) were compared using both quality assessment rating and video source.Results:A total of 142 videos were included in the final analysis. The most common video source was educational (48.6%), followed by physician-sponsored (30.3%). The majority of videos were graded as “somewhat useful” for both diagnostic and treatment content (59.4% and 61.6%, respectively); however, treatment content was rated “not useful” more often than diagnostic information (20.3% vs. 8.7%, respectively). Videos rated as “somewhat useful” received the most views per day on average, while educational videos were the most viewed by source (views and views per day). Educational videos had more views and likes on average than physician-sponsored videos (P < .05), but all other comparisons of video characteristics by source were not significant. Video duration was the only characteristic found to vary significantly by quality assessment rating (P < .001 for both diagnostic and treatment analyses), with higher-quality videos tending to be longer. Videos rated as “excellent” and “very useful” had mean durations >30 minutes but were viewed the least.Conclusion:The overall quality of FAI-related content on YouTube remains low. Clinicians should be familiar with medical information available to patients on the internet, as it can influence patients’ perspectives and shared decision-making processes. This review substantiates the need for more publicly available, high-quality video content regarding the diagnosis and treatment of FAI.

Multi-View Collaborative Network Embedding

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked, if they have common favorite videos in one view, then they can also be linked in another view if they share common subscribers. Unlike traditional single-view networks, multiple views maintain different semantics to complement each other. In this article, we propose M ulti-view coll A borative N etwork E mbedding (MANE), a multi-view network embedding approach to learn low-dimensional representations. Similar to existing studies, MANE hinges on diversity and collaboration—while diversity enables views to maintain their individual semantics, collaboration enables views to work together. However, we also discover a novel form of second-order collaboration that has not been explored previously, and further unify it into our framework to attain superior node representations. Furthermore, as each view often has varying importance w.r.t. different nodes, we propose MANE , an attention -based extension of MANE, to model node-wise view importance. Finally, we conduct comprehensive experiments on three public, real-world multi-view networks, and the results demonstrate that our models consistently outperform state-of-the-art approaches.

Frame Duplication Forgery Detection and Localization Algorithm Based on the Improved Levenshtein Distance

In this digital era of technology and software development tools, low-cost digital cameras and powerful video editing software (such as Adobe Premiere, Microsoft Movie Maker, and Magix Vegas) have become available for any common user. Through these softwares, editing the contents of digital videos became very easy. Frame duplication is a common video forgery attack which can be done by copying and pasting a sequence of frames within the same video in order to hide or replicate some events from the video. Many algorithms have been proposed in the literature to detect such forgeries from the video sequences through analyzing the spatial and temporal correlations. However, most of them are suffering from low efficiency and accuracy rates and high computational complexity. In this paper, we are proposing an efficient and robust frame duplication detection algorithm to detect duplicated frames from the video sequence based on the improved Levenshtein distance. Extensive experiments were performed on some selected video sequences captured by stationary and moving cameras. In the experimental results, the proposed algorithm showed efficacy compared with the state-of-the-art techniques.