Types Of Video Content Research Articles

TV Stream Table of Content: A New Level in the Hierarchical Video Representation

With the rapid development of nowadays technologies, TV could keep its position as one of the most important entertainment and sometimes educative utilities in our daily life. However, keeping this position required a lot of major changes to take place in order for the TV to follow up with the digital revolution, such as, digital broadcasting, High Definition TV, TV on demand. TV-REPLAY, WebTV, etc. This evolution accompanied with many other factors such as the vast spread of communication means and the low prices of storing media have all resulted in many other indispensable technologies for video content storing, structuring, searching and retrieval. Video content can be of various types: a sequence of frames, a sequence of shots, a sequence of scenes, or a sequence of programs which is what the TV stream is usually composed of. Video content structuring would be of a great benefit to help indexing searching and retrieving information from the content efficiently. For example, structuring a soccer game into Play/Break phases facilitates later the detection of goals or summarizing the soccer video. Another example is to structure a news program into stories where each story is composed of an anchorperson segment followed by a report, which facilitates later the search of a specific story or an intelligent navigation inside the news program. However, all the existing analysis methods are dedicated for one type of video content. Such methods generate very poor results if it is applied on a TV stream that is composed of several video programs. So, it is important to detect a priori the boundaries of each program and then identify the type of each program in order to run the dedicated analysis method based on the type. For a TV viewer, a TV stream is a sequence of programs (P) and breaks (B). Programs may be separated by breaks and may include also breaks. For analysis purpose, the stream can be considered as a sequence of audio and video frames with no markers of the start and end points of the included programs or breaks. Most of TV channels that produce TV streams provide a program guide about the broadcasted programs. However, such guides usually lack precision, especially with the existence of live programs which makes the prediction of their start and end very hard. Moreover, program guides do not include any information about the breaks (i.e. commercials). Hence, one of the important steps to structure TV video content is to segment it into different programs and then choose the appropriate method to segment each program separately based on its type. The TV stream structuring consists in detecting the start and end of all the programs and breaks in the stream and later trying to annotate automatically each program by some metadata that summarizes its content or identifies its type. This step can be performed by analyzing the metadata provided with the stream (EPG or EIT), or analyzing the audio-visual stream itself. In this article, we define what we call TvToC (TV stream table of content) that adds a new level in the hierarchical video decomposition (traditional video ToC). Then, we provide a comparative study of all the methods and techniques in the domain of TV stream segmentation. Besides, a comparison of the different approaches is done to highlight the advantages and the weaknesses of each of them.

Influence of video content type on users’ virtual reality sickness perception and physiological response

Virtual Reality (VR) sickness (Cybersickness) is an affliction and a challenge, common to users of virtual environments. We therefore asked ourselves this research question: “Can video content type influence users’ VR sickness and physiological response?” We conducted a study with 26 participants, who watched two omnidirectional videos of different content types (neutral and action content) on five distinct video conditions: 2D TV screen, three generations of Oculus Rift VR HMDs (DK1, DK2 and CV1) and on the mobile Samsung GearVR HMD. The Simulator Sickness Questionnaire and the Subjective Units of Distress Scale in combination with the measurement of the physiological parameters (electrodermal activity and skin temperature, respiratory frequency and heart rate) were used to assess the VR sickness effects. The results show that video content type as well as users’ background preferences (preference to adrenaline sports) affected the users’ VR sickness perception. Considering various video conditions, significantly less VR sickness effects were reported with the TV condition than with any VR devices. The results of the subjective questionnaires were correlated with the objective physiological measurements, whereby skin conductance strongly correlated with the VR sickness effects. The effects were also more pronounced in cases of action video content type. Furthermore, we show there is a strong correlation when assessing the VR sickness effects using subjective questionnaire-based methods (the Simulator Sickness Questionnaire and the Subjective Units of Distress Scale) of various complexity, indicating the simple methods (only one question), can effectively be used as well.

Analysis and Modelling of Power Consumption in IOT with Video Quality Communication

Internet of Things applications such as environmental monitoring and healthcare may involve multimedia communications from IoT devices to humans for decision-making. Therefore, the quality of delivered multimedia should be in good perceived quality. Higher video quality results into higher energy consumptions due to encoding and decoding processes and as a result, will affect the performance of IoT devices due to their inherent energy constraints. This paper presents the impact of video encoding parameters as non-network parameters on the energy consumption of IoT devices. The experimental results from Cooja simulator show that the videos with high bitrates and low frame rates consume more power than videos with low bitrates and high frame rates. It was also found that video content type affects energy consumption. Finally, this paper proposes a power model that takes into account video parameters such as bit rate, frame rate and content types. The proposed model can play a vital role in video quality adaptation in multimedia communication over IoT devices.

Student Video Viewing Habits in an Online Mechanics of Materials Engineering Course

This paper investigated the video viewing habits of students in a sophomore-level, online Mechanics of Materials (MoM) course offered in Spring 2017 and how those habits affected student course grades. Data on student engagement and viewership were collected from a MoM course through a learning management system. This data was compared with length of videos, video content, and video types. With viewership being the focus of the study, it was determined that student engagement decreased over the semester, the content of a video affected its viewership, and viewing rates fluctuated depending on the exam. The other finding was that an increase in viewership tended to indicate an improvement in students’ grades. While the videos are an effective means of improving students’ course grade, changes could be made to improve the videos and increase engagement.

Visual Saliency Detection Using Spatiotemporal Decomposition.

We propose a novel technique for detection of visual saliency in dynamic video based on video decomposition. The decomposition obtains the sparse features in a particular orientation by exploiting the spatiotemporal discontinuities present in a video cube. A weighted sum of the sparse features along three orthogonal directions determines the salient regions in the video cubes. The weights computed using the frame correlation along three directions are based on the characteristic of human visual system that identifies the sparsest feature as the most salient feature in a video. Unlike the existing methods, which detect the salient region as blob, the proposed approach detects the exact boundaries of salient region with minimum false detection. The experimental results confirm that the detected salient regions of a video closely resemble the salient regions detected by actual tracking of human eyes. The algorithm is tested on different types of video contents and compared with the several state-of-the-art methods to establish the effectiveness of the proposed method.

A Study on the Factors Affecting Pre-Roll Advertising Avoidance by Online Video Content Types

A Study on the Factors Affecting Pre-Roll Advertising Avoidance by Online Video Content Types

The behavioural intention to use video lecture in an ODL institution

Purpose The purpose of this paper is to gain insight into learners’ behavioural intention to use the video lectures as their learning material. The behavioural intention construct is measured in terms of perceived ease of use and perceived usefulness of video lectures. It is hoped that the findings of this study will provide feedback as to learners’ intention to use as well as guidelines on how to improve the development of video lectures as the university gears to offer more courses in the fully online mode in the near future. Design/methodology/approach A total sample of 392 questionnaires were collected for this study using technology acceptance model model. Descriptive and inferential statistics are used as the main analytical tool to study the learners’ behavioural intention to use the video lectures as their learning material. The behavioural intention construct is measured in terms of two dimensions: perceived ease of use and perceived usefulness of video lectures. Findings In conclusion, the findings from this research study seem to suggest that OUM learners have a positive perception of video lectures with reference to the two dimensions of “ease of use” and “usefulness”, where ease of use is concerned, OUM learners rate content relevancy, appropriate language and viewing flexibility as the strongest points of video lectures. The aspects ranked lowest are technical (ability to play the video lecture smoothly from the beginning to the end) as well as objective of usage (video lectures are not rated high as revision material for exam preparation). Research limitations/implications Future studies can be conducted pertaining to issues on the context in which learning is taking place within higher education, various definitions of video, and ways of categorising and presenting these different types, teaching “with” and “through” video from the perspective of the lecturer and the educational institution, approaches to didactically embedding and integrating video into a course that results in effective learning and the process and support needed by the (traditional) lecturer to create and deploy various types of video content. Originality/value Over the last ten years, the production of video has gone from a complicated and technical process to one easily done by the general masses. It is now possible for anyone with a mobile phone to make a video recording. The question lies on whether the students have deeper meaning of learning via video lectures and the perception of students on using video lecture as teaching tool in the open and distance learning.

Modeling and Analysis of Power Consumption in Live Video Streaming Systems

This article develops an aggregate power consumption model for live video streaming systems, including many-to-many systems. In many-to-one streaming systems, multiple video sources (i.e., cameras and/or sensors) stream videos to a monitoring station. We model the power consumed by the video sources in the capturing, encoding, and transmission phases and then provide an overall model in terms of the main capturing and encoding parameters, including resolution, frame rate, number of reference frames, motion estimation range, and quantization. We also analyze the power consumed by the monitoring station due to receiving, decoding, and upscaling the received video streams. In addition to modeling the power consumption, we model the achieved bitrate of video encoding. We validate the developed models through extensive experiments using two types of systems and different video contents. Furthermore, we analyze many-to-one systems in terms of bitrate, video quality, and the power consumed by the sources, as well as that by the monitoring station, considering the impacts of multiple parameters simultaneously.

Fast intra coding unit decision for high efficiency video coding based on statistical information

The latest video coding compression standard is known as high–efficiency video coding (HEVC). It supports high-resolution video sequences and has better coding performance than the previous standard H.264/AVC. A quad-tree based coding unit (CU) partitioning process is one of the most efficient technologies used in an HEVC encoder. A coding tree unit (typically 64×64) can be split into smaller CUs based on rate-distortion optimization, allowing various types of video content to be adaptively compressed. In addition, intra prediction of the HEVC standard supports 35 prediction modes (planar, DC, and 33 angular modes) to improve coding efficiency. However, the computational complexity of HEVC encoder becomes a critical problem when implement with an encoder. Thus, a fast CU size decision algorithm for intra prediction of an HEVC encoder is proposed in this study. We utilize image complexity and an adaptive depth prediction for early split CU decision making. In addition, the Bayesian decision rule and quadratic discriminant analysis are used for early termination of the CU partitioning process. Experimental results show that our proposed algorithm considerably reduces encoding time by approximately 55.47% with only a small BD-BR loss (1.01%) compared to the HEVC reference software HM 16.0.

Content structure is king: An empirical study on gratifications, game genres and content type on Twitch

While video games have been widely investigated from the perspective of play, an emerging online media phenomenon is the spectating of video game play, captivating millions of users daily. This study investigates the relationship of video game genres, content type and viewer gratification in the context of live gaming. To study this phenomenon, we employ an online questionnaire study (N = 1097) to investigate six categories of gratifications: affective, information seeking, learning to play, personal integrative, social integrative & tension release motivations and their relationship with game genres and stream types. The results of this study demonstrate that “the medium is the message”, highlight the importance of archetypal structure (i.e. the type of streamed content) over content topic (i.e. the genre of games being streamed), and help to build a better understanding of user generated content and the democratization of media.

Content type and perceived multimedia quality in mobile learning

The increased usage of mobile devices for learning purposes raises several concerns regarding how this adaptation affects learning and perceived quality of educational content across different screen resolutions. This research looks into how educational content type and video adaptation affect the perceived quality of multimedia educational content on two different mobile devices. We consider seven different categories of educational content: slideshow, screencast, presentation, lab demo, interview, documentary, and animation. The results show that the participants could learn regardless of the video content type and the adapted version of the video. We found no statistical significant difference between the perceived quality of the highest quality video and the lower quality video for two of the categories (lab demo and interview) and statistical significant difference on the remaining ones. The implications of this study are also discussed.

페이스북 일상담화의 감정 탐색

The purpose of this study is to explore the emotions of Facebook. Various types of emotions are being exchanged on Facebook. The emotional reactions make the Facebook different from previous electronic bulletin board. According to previous researches, computer-mediated communication can deliver visual symbols and non-verbal cues to enhance the abundance of meanings. Data were collected from 205 Facebook users and the number of users` posts were total 10308. The contents analysis was conducted to explore emotions of the 10308 Facebook posts. The results showed that the most frequent emotion was pleasure. The emotional distributions were different according to the contents types; text, video, photo, and link. For the text content type, emotion of curiosity was apparent and for the photo content type, emotion of love was more frequent than others, and for the video content type, emotion of surprise was salient. The results of the analysis for the shared contents also revealed that pleasure and hope were more frequent emotions than other emotions.

Understanding viewer engagement of video service in Wi-Fi network

With the dramatic growth of online video services and video traffic, video service providers and network operators have keen interest in improving viewer engagement. Viewer engagement is mainly influenced by four aspects: service quality metrics (e.g., rebuffer time), network quality metrics (e.g., physical-layer data rate), video content (e.g., video length) and viewer demography. Previous works only partially consider some of these factors due to limitation of the dataset. In this paper, we develop an experimental platform with more than 50 self-deployed routers in our university campus, collecting information regarding all four aspects of engagement-related factors. Correlation and information gain analysis show that different viewer groups and video content types have different engagement patterns. Furthermore, we analyze each factor’s significance in determining viewer engagement. Finally, we propose to build personalized models to better predict viewer engagement, with bootstrapping customized models for new viewers.

Content-Based Video Quality Prediction for HEVC Encoded Videos Streamed Over Packet Networks

A new reference-free, objective, video quality prediction model that takes into account video content type to predict the quality of streamed high efficiency video coding (HEVC) encoded video sequences is proposed. Research has shown that for the same encoder settings and network quality of service (NQoS), the video quality differs for different types of video content . This indicates that, in addition to encoder settings and NQoS, there may be other key parameters that impact video quality. In this work, we hypothesized that video content type is one of the key parameters that may impact the quality of streamed videos. Based on this assertion, temporal information is extracted from the motion vector (MV) information inherent in the encoded video bitstreams and spatial information is extracted from the quantisation parameter (QP) and the number of bits (Bits) of coded intra (I) and predictive (P) frames to develop a metric that quantifies the content type of different video sequences . The content type metric is subsequently used together with encoding QP setting and network packet loss rate (PLR) to develop a reference -free objective video quality prediction model for streamed HEVC encoded video sequences. This model has an accuracy of 92% when the model predicted values of sequences not used in model derivation are compared with mean opinion score (MOS) obtained through subjective method.

Color difference weighted adaptive residual preprocessing using perceptual modeling for video compression

In this paper, we investigate a method for selectively modifying a video stream using a color contrast sensitivity model based on the human visual system. The model identifies regions of high variance with frame-to-frame differences that are visually imperceptible to a human observer. The model is based on the CIE LAB color appearance model and the CIE ΔE94 color difference formula, taking advantage of the nature of frame-based progressive video coding. The use of a color contrast sensitivity model alone was not sufficient. Therefore, it was important to also incorporate perceptual saliency and spatial activity information from the scene. The method has been implemented in the JM 18.0 H.264/AVC encoder reference software and resulted in an average gain of 37% in data compression without perceptible degradation of the video quality. As expected, the amount of compression improvement obtained is dependent on the type of video content being compressed. The imperceptibility of the changes was confirmed through psychophysical evaluation.

Broadband wireless video streaming: achieving unicast and multicast IPTV in a practical manner

This paper proposes a versatile IPTV video-streaming scheme that provides high-quality unicast with the aid of repair packets but still supports multicast without repair packets. Prior research on wireless multicast video streaming has addressed the risk of feedback implosion when providing adaptive Forward Error Correction (FEC). This approach has not been adopted by providers, who may either prefer unicast streaming or employ a sufficient level of application-layer FEC to avoid the need for adaptation. Instead in this paper, an adaptive, unicast rateless channel-coding scheme is also run in multicast mode. The paper demonstrates the method and the operating conditions for such a joint unicast/multicast service in terms of data rates and suitable video-content type. Data-partitioned source coding with gradual decoding refresh is adopted in the given scenarios, making for a practical broadband wireless streaming scheme.

Optimal Selection of Adaptive Streaming Representations

Adaptive streaming addresses the increasing and heterogeneous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each version (or representation) is characterized by a resolution and a bit rate, and it is aimed at a specific set of users, like TV or mobile phone clients. While most existing works on adaptive streaming deal with effective playout-buffer control strategies on the client side, in this article we take a providers' perspective and propose solutions to improve user satisfaction by optimizing the set of available representations. We formulate an integer linear program that maximizes users' average satisfaction, taking into account network dynamics, type of video content, and user population characteristics. The solution of the optimization is a set of encoding parameters corresponding to the representations set that maximizes user satisfaction. We evaluate this solution by simulating multiple adaptive streaming sessions characterized by realistic network statistics, showing that the proposed solution outperforms commonly used vendor recommendations, in terms of user satisfaction but also in terms of fairness and outage probability. The simulation results show that video content information as well as network constraints and users' statistics play a crucial role in selecting proper encoding parameters to provide fairness among users and to reduce network resource usage. We finally propose a few theoretical guidelines that can be used, in realistic settings, to choose the encoding parameters based on the user characteristics, the network capacity and the type of video content.

Video quality assessment in video streaming services considering user preference for video content

In video streaming service, the user's Quality of Experience (QoE) is not only related to video signal quality received at consumer's devices, the users’ subjectivity must also be considered. In this context, a video quality assessment method that takes into account the user's preference for video content is proposed in this research. In order to perform this task, the users' profiles that include their preferences were stored in a video server. Then, subjective tests of video quality assessment were conducted, in which evaluators had different video content preferences. Results show that the evaluators' QoE is highly correlated with the user's preference for video content type. Based on these experimental results, a function named Preference Factor (PF) is defined and used to adjust the quality index values obtained by an objective video quality metric running in the end user's device. The PF function also depends on video content type and quality index score. Using the PF function, the enhanced Video streaming Quality Metric (e-VsQM) is proposed and the results of its performance evaluation demonstrate that PF improves an objective video quality metric. Furthermore, e-VsQM has low complexity and can be utilized in different video services. Thus, an application scenario is presented, in which the proposed video quality metric is implemented.

A cache-aware motion estimation organization for a hardware-based H.264 encoder

The video resolution required for many types of video content has increased as technology has advanced. For the real-time encoding of the high resolutions such as full high definition (FHD), quad-FHD (QFHD) and beyond, various fast motion estimation (ME) algorithms have been researched. Caches are used for many fast MEs in a hardware-based encoder, in order to increase local memory utilization and thereby reduce external memory access. However, most previous works do not pay attention to the amount of cache access from multiple MEs. In a multi-core environment for high resolution videos, access conflicts directly affect the computation time. In this paper, various types of caches are compared in terms of the size, hit ratio, cache port conflicts and hardware overhead. To reduce the amount of cache access associated with the basic shared cache, zigzag snake scan and selective data-storage schemes are proposed for integer and fractional MEs, respectively. Additionally, the cache access arbitration hides the computation delay which arises due to a cache port conflict in a pipeline system. The proposed schemes are applicable for the existing cache design achieving a good scalability in a multi-core environment. Simulation results show that the ME computation time reduced by the proposed schemes is comparable to that of the dual-port shared cache which shows the least amount of port conflicts.

Information or Entertainment? Matching the Right Online Video Content to Your Product

As online video content becomes increasingly popular, online retailers try to follow this trend and incorporate product videos into their websites. By doing so, they hope to encourage revisit and purchase intention and, ultimately, increase sales. However, the actual effect of product videos has received little attention in marketing research. In particular, what type of video content should be matched to what type of product remains unclear. Thus, product video providers face the risk of expensively producing the wrong content and online retailers forgo potential sales increases because of poorly matched product-video content combinations. In our empirical study, we analyze the effects of product videos integrated into the product detail pages of online shops. We examine the impact of different video contents (emotional/informative) for different product types (low/high involvements). We find that emotional videos are superior for low involvement products, while informative video content should be used for high involvement products.