Video Playback Research Articles

The effect of social information from live demonstrators compared to video playback on blue tit foraging decisions.

Video playback provides a promising method to study social interactions, and the number of video playback experiments has been growing in recent years. Using videos has advantages over live individuals as it increases the repeatability of demonstrations, and enables researchers to manipulate the features of the presented stimulus. How observers respond to video playback might, however, differ among species, and the efficacy of video playback should be validated by investigating if individuals’ responses to videos are comparable to their responses to live demonstrators. Here, we use a novel foraging task to compare blue tits’ (Cyanistes caeruleus) responses to social information from a live conspecific vs video playback. Birds first received social information about the location of food, and were then presented with a three-choice foraging task where they could search for food from locations marked with different symbols (cross, square, plain white). Two control groups saw only a foraging tray with similar symbols but no information about the location of food. We predicted that socially educated birds would prefer the same location where a demonstrator had foraged, but we found no evidence that birds copied a demonstrator’s choice, regardless of how social information was presented. Social information, however, had an influence on blue tits’ foraging choices, as socially educated birds seemed to form a stronger preference for a square symbol (against two other options, cross and plain white) than the control birds. Our results suggest that blue tits respond to video playback of a conspecific similarly as to a live bird, but how they use this social information in their foraging decisions, remains unclear.

NOMA-Enabled Layered Video Multicast in Wireless-Powered Relay Systems

This letter proposes a novel layered video multicast scheme for wireless-powered relay systems, where the multi-antenna base station multicasts layered video signals over non-orthogonal multiple access (NOMA) to heterogeneous user groups with the help of a dedicated relay. To minimize the average transmission power of the base station, an optimization problem is formulated to jointly design the energy signal power and layered video multicast beamformers, while guaranteeing all the users’ quality of experience (QoE), i.e., fluent video playback and successful decoding of video signals. Since the formulated optimization problem is non-convex, a low-complexity algorithm is developed to obtain a local minimum, and a novel initial feasible point search algorithm is proposed to initialize the solving process. Simulation results reveal that the proposed scheme works well and can greatly reduce the power consumption.

Fast surveillance video indexing & retrieval with WiFi MAC address tagging

<span>Conventional public safety surveillance video camera systems required 24/7 monitoring of security officers with video wall display installed in the control room. When a crime or incident is reported, all the recorded surveillance video streams nearby the incident area are playback simultaneously on video wall to help locate the target person. The security officers can fast forward the video playback to speed up the video search, but it requires massive manpower if there are hundreds of video streams required to be examined on the video wall. One of the possible solutions is through a suitable video indexing and retrieval technique to prioritize the video frames that need to be processed. This paper presents a WiFi sniffer enabled surveillance camera, with 3-stage WiFi frame inspection filter and the use of collected WiFi signal strength for filtering, to tag the collected WiFi MAC addresses to the surveillance video frames according to the time of the MAC address is sniffed. Additional metadata (WiFi MAC address of smartphone) collected during the occurrence of the incident can be used to prioritize the retrieving of surveillance video frames for subsequent image processing. </span>

Self-Assessment of Surgical Skills: A Systematic Review

Self-assessment is fundamental in surgical training to enhance learning in the absence of trainer feedback. The primary objective of this review was to assess the factors that influence accuracy of self-assessment at technical skills across all surgical specialties. The secondary objective was to assess whether there are any innate factors or attributes to predict those that will carry out effective self-assessment. A systematic review was carried out in accordance with PRISMA guidelines. A search strategy encompassing MEDLINE, EMBASE, ERIC, WHO, and the Cochrane database was conducted to identify studies investigating self-assessment at any surgical task. Quality was assessed using the Newcastle-Ottawa scale. A summary table was created to describe specialty, participants, task, setting, assessment tool, and correlation coefficient between self and expert assessment. The review protocol was registered in PROSPERO. Of 24,638 citations, 40 met inclusion criteria. In total 1753 participants performed 68 procedures. Twenty-six studies investigated skills in general surgery with the remaining 14 in various other surgical specialties. Accuracy of self-assessment is superior in those with greater experience and age, and with use of retrospective video playback. Accuracy tends to be reflected by overestimation of performance. Stressful environments reduce accuracy. There is limited evidence in the literature regarding predicting traits for those who will carry out accurate self-assessment. The ability to perform accurate self-assessment is an important skill in surgical training, with accuracy being influenced by a multitude of factors. The use of self-assessment from retrospective video playback may be of benefit in surgical training curricula to enhance learning of technical skills. Further studies are required to define predictors of good self-assessment, which will strengthen recruitment and mentoring to assist trainee learning.

Color Image Compressor using Discrete Wavelet Transform and Block Based Image Coding

In the present time of sight and sound, the necessity of picture/video stockpiling and video playback, transmission for video conferencing, picture and video recovery and so forth are expanding exponentially. Thus, the need of better pressure innovation is consistently popular. Present day applications, notwithstanding high pressure proportion, additionally interest for proficient encoding and deciphering forms, with the goal that computational imperative of some continuous applications is fulfilled. Two broadly utilized spatial space pressure systems are discrete wavelet transform and multi-level block truncation coding (BTC). DWT method is used to stationary and non-stationary images and applied to all average pixel value of image. Muli-level BTC is a kind of lossy picture pressure strategy for greyscale pictures. In this,the unique picture is partitioned into squares and after that uses a quantizer to decrease the quantity of dark levels in each square while keeping up a similar mean and standard deviation. In this paper is studied of Multi-level BTC and DWT technique for gray and color image.

Light Ears

Modern Internet-enabled smart lights promise energy efficiency and many additional capabilities over traditional lamps. However, these connected lights also create a new attack surface, which can be maliciously used to violate users' privacy and security. In this paper, we design and evaluate novel attacks that take advantage of light emitted by modern smart bulbs, in order to infer users' private data and preferences. The first two attacks are designed to infer users' audio and video playback by a systematic observation and analysis of the multimedia-visualization functionality of smart light bulbs. The third attack utilizes the infrared capabilities of such smart light bulbs to create a covert-channel, which can be used as a gateway to exfiltrate user's private data out of their secured home or office network. A comprehensive evaluation of these attacks in various real-life settings confirms their feasibility and affirms the need for new privacy protection mechanisms.

On the Minimum Perceptual Temporal Video Sampling Rate and Its Application to Adaptive Frame Skipping

Media technology, in particular video recording and playback, keeps improving to provide users with high-quality real and virtual visual content. In recent years, increasing the temporal sampling rate of videos and the refresh rate of displays has become one focus of technical innovation. This raises the question, how high the sampling and refresh rates should be? To answer this question, we determine the minimum temporal sampling rate at which a video should be presented to make temporal sampling imperceptible to viewers. Through a psychophysical study, we find that this minimum sampling rate depends on both the speed of the objects in the image plane and the exposure time of the recording camera. We propose a model to compute the required minimum sampling rate based on these two parameters. In addition, state-of-the-art video codecs employ motion vectors from which the local object movement speed can be inferred. Therefore, we present a procedure to compute the minimum sampling rate given an encoded video and camera exposure time. Since the object motion speed in a video may vary, the corresponding minimum frame rate is also varying. This is why the results of this paper are particularly applicable when used together with adaptive frame rate computer generated graphics or novel video communication solutions that drop insignificant frames. In our experiments, we show that videos played back at the minimum adaptive frame rate achieve an average bit rate reduction of 26% compared to constant frame rate playback, while perceptually no difference can be observed.

IPTV video quality assessment model based on neural network

In recent years, with the continuous development of network science and technology and the continuous promotion of “three networks convergence”, IPTV (Interactive Network Television) has shown a rapid development trend. IPTV is different from the traditional one-way broadcasting mode of television, it can achieve interaction with the audience, and provide more personalized and diversified videos. With the rapid development of new media, massive video resources and a large number of video-related information are sweeping in. The evaluation of video can no longer be limited to the ratings of traditional platforms. Video playback on new media platforms, network impact, video content and other related data are also important indicators affecting video evaluation. Video quality is closely related to video content characteristics. Different videos have different sensitivity to the same packet loss rate. The IPTV video quality evaluation model based on content features considers the video content characteristics. Firstly, the QP, bit rate and motion vector (MV) information of the video is obtained by analyzing the video stream. Then, the time complexity of the video is calculated by using the obtained MV, and the spatial complexity of the video is calculated by quantization parameters and bit rate. Videos are classified by clustering analysis. On this basis, the BP neural network is used to establish the model and evaluate the video quality, so as to better reflect the visual perception of the human eye. The model has low computational complexity and is suitable for IPTV video quality assessment with certain computing power in network nodes.

Generating a Cylindrical Panorama from a Forward-Looking Borehole Video for Borehole Condition Analysis

Geological exploration plays a fundamental and crucial role in geological engineering. The most frequently used method is to obtain borehole videos using an axial view borehole camera system (AVBCS) in a pre-drilled borehole. This approach to surveying the internal structure of a borehole is based on the video playback and video screenshot analysis. One of the drawbacks of AVBCS is that it provides only a qualitative description of borehole information with a forward-looking borehole video, but quantitative analysis of the borehole data, such as the width and dip angle of fracture, are unavailable. In this paper, we proposed a new approach to create a whole borehole-wall cylindrical panorama from the borehole video acquired by AVBCS, which provides a possibility for further analysis of borehole information. Firstly, based on the Otsu and region labeling algorithms, a borehole center location algorithm is proposed to extract the borehole center of each video image automatically. Afterwards, based on coordinate mapping (CM), a virtual coordinate graph (VCG) is designed in the unwrapping process of the front view borehole-wall image sequence, generating the corresponding unfolded image sequence and reducing the computational cost. Subsequently, based on the sum of absolute difference (SAD), a projection transformation SAD (PTSAD), which considers the gray level similarity of candidate images, is proposed to achieve the matching of the unfolded image sequence. Finally, an image filtering module is introduced to filter the invalid frames and the remaining frames are stitched into a complete cylindrical panorama. Experiments on two real-world borehole videos demonstrate that the proposed method can generate panoramic borehole-wall unfolded images from videos with satisfying visual effect for follow up geological condition analysis. From the resulting image, borehole information, including the rock mechanical properties, distribution and width of fracture, fault distribution and seam thickness, can be further obtained and analyzed.

Emotional Intelligence Predicts Accurate Self-Assessment of Surgical Quality: A Pilot Study

BackgroundSelf-assessment is fundamental in surgical training. Accuracy of self-assessment is superior with greater age, experience, and the use of video playback. Presently, there is scarce evidence in the literature regarding predictors for a surgical trainee's aptitude for self-assessment. The objective of this study was to investigate whether emotional intelligence or visual-spatial aptitude can predict effective self-assessment among novice surgeons performing laparoscopic appendectomy (LA). Materials and methodsEighteen novice trainees performed a simulated LA, and two aptitude measures were evaluated: (1) emotional intelligence questionnaire and (2) visual spatial ability test. Self-assessment of their performance was conducted using the Objective Assessment of Surgical and Technical Skills global rating scale and ranking five subtasks of the procedure in order of quality of performance after watching a playback of their LA. Two blinded experts (senior consultant surgeons, performed >100 LAs) assessed surgical quality using the same scoring system. Candidates were ranked into higher and lower aptitude groups for the two aptitude measures. Spearman's rank correlation coefficient was calculated to identify if either of the two groups demonstrated greater agreement between self and expert assessment in relation to the two aptitude measures. ResultsParticipants with a higher degree of emotional intelligence demonstrated significant agreement with expert assessment (r = 0.73, P = 0.031). ConclusionsEmotional intelligence can predict better self-assessment of surgical quality after performing a simulated LA. This may facilitate early identification of individuals who might require mentoring or guidance with self-assessment as well as contribute to selection criteria.

Performance analysis of video on demand in an IEEE 802.11p-based vehicular network

We consider a VoD (Video on-Demand) platform designed for vehicles traveling on a highway or other major roadway. Typically, cars or buses would subscribe to this delivery service so that their passengers get access to a catalog of movies and series stored on a back-end server. The network infrastructure comprises IEEE 802.11p RSUs (Road Side Units) that are deployed along the highway and deliver video content to traveling vehicles. In this paper, we propose a simple analytical and yet accurate solution to estimate two key performance parameters for a VoD platform: (i) the average download data rate experienced by vehicles over their journey and (ii) the average “interruption time”, which corresponds to the fraction of time the video playback of a given vehicle is interrupted because of an empty buffer. Through multiple examples, we investigate the influence of several parameters (e.g., the video bit rate, the number of vehicles, the distance between RSUs, the vehicle velocity) on these two performance parameters whose outcome may help the sizing of an IEEE 802.11p-based VoD platform.

Acceptable Wait Time Models at Transit Bus Stops

This study aimed at determining patrons’ acceptable wait times beyond the bus scheduled arrival time at bus stops in Washington, DC and to develop accompanying prediction models to provide decision-makers with additional tools to improve patronage. The research primarily relied on a combination of manual and video-based data collection efforts. Manual field data collection was used for surveying patrons to obtain their suggested acceptable wait times at bus stops, while video-based data collection was used to obtain bus stop characteristics and operations. In all, 3,388 bus patrons at 71 selected bus stops were surveyed. Also, operational data for 2,070 bus arrival events on 226 routes were extracted via video playback. Data were collected for AM peak, PM peak and mid-day periods of nine-month duration from May 2018 through January 2019. The results of the survey showed that the minimum acceptable wait time beyond the scheduled arrival time was reported to be 1 minute, while the maximum acceptable wait time was reported to be 20 minutes. Regression analyses were conducted to develop models to predict the maximum acceptable wait time based on factors including temperature, presence of shelter at the bus stops, average headway of buses, and patrons’ knowledge of bus arrival times. The models were developed for A.M., P.M. and mid-day periods. The F-Statistics for the models were determined to be statistically significant with p values

Interactive and automatic navigation for 360° video playback

A common way to view a 360° video on a 2D display is to crop and render a part of the video as a normal field-of-view (NFoV) video. While users can enjoy natural-looking NFoV videos using this approach, they need to constantly make manual adjustment of the viewing direction not to miss interesting events in the video. In this paper, we propose an interactive and automatic navigation system for comfortable 360° video playback. Our system finds a virtual camera path that shows the most salient areas through the video, generates a NFoV video based on the path, and plays it in an online manner. A user can interactively change the viewing direction while watching a video, and the system instantly updates the path reflecting the intention of the user. To enable online processing, we design our system consisting of an offline pre-processing step, and an online 360° video navigation step. The pre-processing step computes optical flow and saliency scores for an input video. Based on these, the online video navigation step computes an optimal camera path reflecting user interaction, and plays a NFoV video in an online manner. For improved user experience, we also introduce optical flow-based camera path planning, saliency-aware path update, and adaptive control of the temporal window size. Our experimental results including user studies show that our system provides more pleasant experience of watching 360° videos than existing approaches.

Social information use about novel aposematic prey is not influenced by a predator's previous experience with toxins

Abstract Aposematism is an effective antipredator strategy. However, the initial evolution and maintenance of aposematism are paradoxical because conspicuous prey are vulnerable to attack by naïve predators. Consequently, the evolution of aposematic signal mimicry is also difficult to explain. The cost of conspicuousness can be reduced if predators learn about novel aposematic prey by observing another predator's response to that same prey. On the other hand, observing positive foraging events might also inform predators about the presence of undefended mimics, accelerating predation on both mimics and their defended models. It is currently unknown, however, how personal and social information combines to affect the fitness of aposematic prey. For example, does social information become more useful when predators have already ingested toxins and need to minimize further consumption? We investigated how toxin load influences great tits' (Parus major) likelihood to use social information about novel aposematic prey, and how it alters predation risk for undefended mimics. Birds were first provided with mealworms injected with bitter‐tasting chloroquine (or a water‐injected control), before information about a novel unpalatable prey phenotype was provided via video playback (either prey alone, or of a great tit tasting the noxious prey). An experimentally increased toxin load made great tits warier to attack prey, but only if they lacked social information about unpalatable prey. Socially educated birds consumed fewer aposematic prey relative to a cryptic phenotype, regardless of toxin load. In contrast, after personally experiencing aposematic prey, birds ignored social information about palatable mimics and were hesitant to sample them. Our results suggest that social information use by predators could be a powerful force in facilitating the evolution of aposematism as it reduces predation pressure on aposematic prey, regardless of a predator's toxin load. Nevertheless, observing foraging events of others is unlikely to alter frequency‐dependent dynamics among models and mimics, although this may depend on predators having recent personal experience of the model's unpalatability. A free Plain Language Summary can be found within the Supporting Information of this article.

Role of playback videolaryngoscope footage in improved patient care.

Despite the frequency of videolaryngoscope use, to the authors’ knowledge, there are no publications exploring the role of playback video recording in real time to improve patient care. We present a patient with a difficult airway complicated by trauma during GlideScope® (Verathon Inc., Bothell, WA, USA) intubation. The injury was not recognised by any of the anaesthetic staff observing the intubation, and was only identified when playback video footage was viewed. This provided the opportunity for early ear, nose and throat surgical review prior to extubation. Airway trauma can be missed by the anaesthetist. Reviewing video playback footage at an appropriate time during the case may enable more timely recognition of airway injuries and, ultimately, improved patient care.

AutoTune: Game-Based Adaptive Bitrate Streaming in Cloud-Based Hybrid VoD Systems

Hybrid peer-to-peer assisted cloud-based video-on-demand (VoD) systems augment cloud-based VoD systems with P2P networks to improve scalability and save bandwidth costs in the cloud. In these systems, the VoD service provider (e.g., NetFlix) relies on the cloud to deliver videos to users and pays for the cloud bandwidth consumption. The users can download videos from both the cloud and peers in the P2P network. It is important for the VoD service provider to i) minimize the cloud bandwidth consumption, and ii) guarantee users’ satisfaction (i.e., quality-of-experience). Though previous adaptive bitrate streaming (ABR) methods improve video playback smoothness, they cannot achieve these two goals simultaneously. To tackle this challenge, we propose AutoTune, a game-based adaptive bitrate streaming method. In AutoTune, we formulate the bitrate adaptation problem in ABR as a noncooperative Stackelberg game, where VoD service provider and the users are players. The VoD service provider acts as a leader and it decides the VoD service price for users with the objective of minimizing cloud bandwidth consumption while ensuring users’ participation. In response to the VoD service price, the users select video bitrates that lead to maximum utility (defined as a function of its satisfaction minus associated VoD service fee). Finally, the Stackelberg equilibrium is reached in which the cloud bandwidth consumption is minimized while users are satisfied with selected video bitrates. To enhance the performance of AutoTune, we further propose the reputation-based incentive scheme and the popularity-based cache management scheme. Experimental results from the PeerSim simulator and the PlanetLab real-world testbed show that compared to existing methods, AutoTune can provide high user satisfaction and save cloud bandwidth consumption. Also, the proposed enhancement schemes are effective in improving the performance of AutoTune.

Retracted: Smart city framework based on intelligent sensor network and visual surveillance

SummaryIn the field of urban development, the construction of new smart cities has received the attention of the government and society. In the process of promoting urban intelligence, urban road lighting, intelligent lighting, etc, have gradually received attention, while the lighting source and control methods have been greatly improved. Smart cities portray a very good vision for development for human beings. Traditional video surveillance only provides simple functions such as video capture, storage, and playback, which are difficult to use as an early warning and alarm. To ensure that abnormal behaviors are monitored in real time and effective measures are taken in a timely manner, monitoring personnel are required to monitor the video at all times. A huge monitoring network will generate massive video data. How to efficiently extract useful information from these massive data is a problem to be solved by intelligent video surveillance technology. This paper starts with the concept of top‐level design of smart city construction and analyzes the meaning and content of top‐level design. This paper summarizes some of the misunderstandings in the current top‐level design process and provides suggestions for the top‐level design.

Optimization of video steganography with additional compression and encryption

Currently, data such as text, images and video are very important. Therefore, data must be secured from unauthorized parties. In this paper, we propose a number of security levels, first using compression techniques on the data that will be hidden to reduce the size of the data, second using encryption techniques on data that has been compressed so that data is more secure, third using video steganography techniques on compressed and encrypted data so that unauthorized parties are increasingly difficult to extract data. Measurement of differences in quality of cover-video and stego-video using MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), evolution histogram of video tags, and video playback. The results show that the proposed algorithm gives better results than the previous algorithm which has a smaller MSE, larger PSNR, smaller histogram evolution of video tags, and play video without distortion.

Multivessel Computation Offloading in Maritime Mobile Edge Computing Network

With the development of the maritime networks, the data of vessel users is growing exponentially, and more and more resource intensive tasks, such as multimedia applications, high-definition video playback and games, appear in the daily demands. These changes have greatly increased the energy consumption and bandwidth requirements of vessel terminals and networks. In order to meet the requirements of high bandwidth and low delay for the high-speed development of mobile network, and reduce the network load, the concept of mobile edge computing (MEC) is proposed and has been widely supported by the academia and industry. It is considered to be one of the key technologies of the next generation networks. Inspired by this idea, this paper introduces computing offloading technology to maritime mobile cloud networks. Maritime mobile cloud network is the product of the continuous development of cloud computing technology and mobile Internet technology. In this paper, we studied the issue of computation task offloading for vessel terminals, focusing on minimizing the energy consumption of vessel terminals and the execution delay of computation task. First, it determines that whether if it should be offloaded to the cloud server. Second, the server should be selected to run the computation task. The goal of the optimization is to minimize the energy consumption of vessel terminals and the execution delay of computation task taking into account of different weights. To reduce the execution latency and device energy consumption, we proposed a multivessel computation offloading algorithm based on improved Hungarian algorithm in maritime MEC network. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Dynamic Adaptive Streaming based on Deep Reinforcement Learning

Dynamic adaptive streaming over HTTP (DASH) is the dominant technology of multimedia delivery over the Internet. In DASH system, adaptive bitrate (ABR) algorithms running on client-side video player are the key to improve user quality of experience (QoE). However, most existing ABR algorithms employ fixed control rules to make bitrate decisions based on throughput, playback buffer size, or a combination of the two. As a result, their performance in the complicated and fluctuant network environment is incompetent. In this paper, we propose QRL, a bitrate adaptation approach based on deep reinforcement learning. QRL uses double Q-Learning, an enhanced Q-Learning method. After training the neural network model, the algorithm can select proper bitrates for future video segments based on all the information collected by client during the video playback process. Simulation results show that QRL achieves better performance than other algorithms.