• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1527-1530
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• 2002

Video summary is one of the tools which can provide the fast and effective browsing fur a lengthy video. Video summary consists of many key-frames that could be defined differently depending on the video genre it belongs to. Consequently, the video summary constructed by the uniform manner might lead into inadequate result. Therefore, identifying the video genre is the important first step in generating the meaningful video summary. We propose a new method that can classify the genre of the video data in MPEG compressed bit-stream domain. Since the proposed method operates directly on the com- pressed bit-stream without decoding the frame, it has merits such as simple calculation and short processing time. In the proposed method, only the visual information is utilized through the spatial-temporal analysis to classify the video genre. Experiments are done for 6 genres of video: Cartoon, Commercial, Music Video, News, Sports, and Talk Show. Experimental result shows more than 90% of accuracy in genre classification for the well-structured video data such as Talk Show and Sports.

• Bulletin of the Korean Space Science Society
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• 2004.10b
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• pp.308-311
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• 2004

This paper describes design, implementation and functional test of on-board flight analog video system. Video system is the part of the telemetry system as a means of providing flight vehicle's image data. Adopting analog video transmission technique, we could design and test the system in a short time and implement the prototype model with simpler circuits. As results of the functional test, we have made the verification and validation of two cameras' switching in time, and frequency modulation and demodulation of NTSC color signals successfully.

• Educational Technology International
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• v.14 no.1
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• pp.1-26
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• 2013

This study is to identify whether three types of online instruction (text-based, video-based, and video-based instruction without text) and age category have a different influence on students' comprehension and motivation. Online students were randomly assigned to one of six groups, and they attended two-week online lectures via Course Management System. The comprehension test and the short form of IMMS were implemented when 114 participants accomplished two-week online lectures. The results revealed that using instructional video in online instruction is more effective instructional medium than text only in order to promote learner's motivation. Besides, older adults aged 41-60 are significantly different from younger adults (21-40 years old) in terms of students' comprehension. Furthermore, three types of online instructions are likely to be influenced by age category.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.836-845
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• 2002

Video summary is one of the tools which can provide the fast and effective browsing for a lengthy video. Video summary consists of many key-frames that could be defined differently depending on the video genre it belongs to. Consequently, the video summary constructed by the uniform manner might lead into inadequate result. Therefore, identifying the video genre is the important first step in generating the meaningful video summary. We propose a new method that can classify the genre of the video data in MPEC compressed bit-stream domain. Since the proposed method operates directly on the compressed bit-stream without decoding the frame, it has merits such as simple calculation and short processing time. In the proposed method, only the visual information is utilized through the spatial-temporal analysis to classify the video genre. Experiments are done for 6 genres of video: Cartoon, commercial, Music Video, News, Sports, and Talk Show. Experimental result shows more than 90% of accuracy in genre classification for the well -structured video data such as Talk Show and Sports.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.130-137
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• 2019

With the advent of smart media, short and fast-paced video content appeared, and conditions for viewing changed to viewing in environments where the perception is dispersed due to distracting and complicated external situations in a short period of time. Accordingly, smart media videos are quickly delivering meaning while keeping the eyes of viewers who lack patience. Our eyes and brain have a hard time accepting image information that flows through the constraints of a small screen. Our visual perception is limited in terms of acceptable visual information and, in particular, less cognition in moving images, so the production of smart media images should be directed in a way that enhances perceptual understanding. To be able to effectively communicate what you want to talk about while reducing the visual burden, intuitive image comprehension is needed by applying intuitiveness and behavioral induction to the moving images. Close-up shot, stable structure such as frontality and three-division structure, and color have such 'visual affordance' Therefore we need to use that device appropriately.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.659-665
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• 2021

Short video applications (e.g., TikTok, YouTube shorts) are growing quickly in terms of active users and usage time. Of course, advertising industry is utilizing the app as AD channel. The current study, however, argues that the effectiveness of ADs in short video apps are not articulated well and that precise research for measuring the effect is required. In this context, this study measured the effects of storytelling ad types(reality, parody, creative) and message sensation value(high vs. low level) on story transportation. The notable finding is that when creative storytelling ad type which requires more cognitive resources than other two types meets high level of message sensation value, ad viewers could reach cognitive overload state which induced low effectiveness of ad. As its result, the effectiveness of AD reduced. More specific theoretical discussion and suggestions for advertising producers are described.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.19-28
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• 2019

In this paper, an online video summarization algorithm based on multiple object detection is proposed. As crime has been on the rise due to the recent rapid urbanization, the people's appetite for safety has been growing and the installation of surveillance cameras such as a closed-circuit television(CCTV) has been increasing in many cities. However, it takes a lot of time and labor to retrieve and analyze a huge amount of video data from numerous CCTVs. As a result, there is an increasing demand for intelligent video recognition systems that can automatically detect and summarize various events occurring on CCTVs. Video summarization is a method of generating synopsis video of a long time original video so that users can watch it in a short time. The proposed video summarization method can be divided into two stages. The object extraction step detects a specific object in the video and extracts a specific object desired by the user. The video summary step creates a final synopsis video based on the objects extracted in the previous object extraction step. While the existed methods do not consider the interaction between objects from the original video when generating the synopsis video, in the proposed method, new object clustering algorithm can effectively maintain interaction between objects in original video in synopsis video. This paper also proposed an online optimization method that can efficiently summarize the large number of objects appearing in long-time videos. Finally, Experimental results show that the performance of the proposed method is superior to that of the existing video synopsis algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2444-2463
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• 2020

Significant detection of video can more rationally allocate computing resources and reduce the amount of computation to improve accuracy. Deep learning can extract the edge features of the image, providing technical support for video saliency. This paper proposes a new detection method. We combine the Convolutional Neural Network (CNN) and the Deep Bidirectional LSTM Network (DB-LSTM) to learn the spatio-temporal features by exploring the object motion information and object motion information to generate video. A continuous frame of significant images. We also analyzed the sample database and found that human attention and significant conversion are time-dependent, so we also considered the significance detection of video cross-frame. Finally, experiments show that our method is superior to other advanced methods.

• Trans-
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• v.13
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• pp.59-90
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• 2022

Short-form content, which means "short video content within 10 minutes," is rapidly emerging as a recent trend among MZ generations based on the fact that it can be viewed whenever there is a short running time, and they are physically short, colorful, and deliver a lot of information in a compressed time, showing differences in both long-form content and format. In addition, entertainment videos such as information delivery videos, eating shows, web entertainment, and dance challenges are mainly produced and distributed, so there is no need to take expertise as a creative work of video experts, and consumers often become producers by directly participating in production using low-end equipment such as smartphones. For these reasons, shortform content creates new image styles rather than general existing image forms such as long-form contents, and this study focuses on changes in editing styles. This study summarized the following five characteristic changes by analyzing the editing style of short form content that has changed compared to long form content according to the 'visual' aspect. The use of frames, memes, screen division, blue screen, and subtitles are included, and by organizing each characteristic, we identified the editing style of shortform content that has emerged as a recent trend and learned about the changes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4585-4603
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• 2015

Video streaming services make up a large proportion of Internet traffic on both fixed and mobile access throughout the world. Adaptive streaming allows for dynamical adaptation of the bitrate with varying network conditions, to guarantee the best user experience. Adaptive bitrate algorithms face a significant challenge in correctly estimating the throughput as it varies widely over time. In this paper, we first evaluate the throughput estimation techniques and show that the method that we have used offers stable response to throughput fluctuations while maintaining a stable playback buffer. Then, we propose an adaptive bitrate scheme that intelligently selects the video bitrates based on the estimated throughput and buffer occupancy. We show that the proposed scheme improves viewing experience by achieving a high video rate without taking unnecessary risks and by minimizing the frequency of changes in the video quality. Furthermore, we show that it offers a stable response to short-term fluctuations and responds swiftly to large fluctuations. We evaluate our algorithm for both constant bitrate (CBR) and variable bitrate (VBR) video content by taking into account the segment sizes and show that it significantly improves the quality of video streaming.