• 한국컴퓨터산업교육학회:학술대회논문집
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• 2003.11a
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• pp.127-133
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• 2003

The data models which express all types of video information physically and logically. and the definition of spatiotemporal relationship of video data objects In This paper, we classifies meta-model for efficient management on spatiotemporal relationship between two objects in video image data, suggests meta-models based on Rambaugh's OMT technique, and expanded user model to apply the adaptive model, established from hyper-media or web agent to VOD. The proposed meta-model uses data's special physical feature: the effects of camera's and editing effects of shot, and 17 spatial relations on Allen's 13 temporal relations, topology and direction to include logical presentation of spatiotemporal relation for possible spatiotemporal reference and having unspecified applied mediocrity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1249-1265
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• 2022

Video streaming has become one of the most popular applications for mobile devices. The network bandwidth required for video streaming continues to exponentially increase as video quality increases and the user base grows. Multi-Path TCP (MPTCP), which allows devices to communicate simultaneously through multiple network interfaces, is one of the solutions for providing robust and reliable streaming of such high-definition video. However, mobile video streaming over MPTCP raises new concerns, e.g., power consumption and cellular data usage, since mobile device resources are constrained, and users prefer to minimize such costs. In this work, we propose a mobile video streaming framework over MPTCP (mDASH) to reduce the costs of energy and cellular data usage while preserving feasible streaming quality. Our evaluation results show that by utilizing knowledge about video behavior, mDASH can reduce energy consumption by up to around 20%, and cellular usage by 15% points, with minimal quality degradation.

• 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.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.169-174
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• 2002

In this paper, we suggest a new spatial information system called video GIS where video is used for spatial data construction and is integrated with map. We develop a prototype system of video GIS and apply it to roadside facility management. The main functions supported by the suggested system are data collection, coordinate calculation and conversion, data construction, analysis, searching, and browsing. The stereo images and corresponding position data are collected by a vehicle named 4S-Van that has GPS, IMU, and cameras. The 3-D coordinates of the objects in the images, such as road sign, signal lamp, and building, can be calculated and constructed from the collected data. The spatial objects are displayed on both image and map, and can be searched and browsed, which enables visual and realistic browsing and management of spatial objects. Compared to conventional field survey used in roadside facility management, the method enables faster, easier, and more efficient construction of spatial data. The suggested video GIS can be applied not only to roadside facility management but also to many similar projects of central or local governments that are related to GIS.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10c
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• pp.576-578
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• 2000

현재의 대부분의 VOD System에서는 Single Server System의 제약인 확장성과 안정적 서비스를 제공하기 위해 Multi-Server System을 사용하고 있다. Multiple Server에 Video Data를 Striping한 구조를 Parallel Video Server Architecture라 한다. 본 연구에서는 Parallel Video Server System 상에서 Data의 Striping Policy에 대해 고찰해보고 이때 발생하는 load balancing과 redundancy 문제의 해결책을 제시하였다. 또한 이를 실제 local Network 시스템에 적용하여 구현하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8C
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• pp.810-823
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• 2005

The transmission of duplicate packets provides a great loss-resilience without undue time-delay in the video transmission over packet loss networks. But this method generally deteriorates the problem of traffic congestion because of the increased bit-rate required for duplicate transmission. In this paper, we propose an efficient packetization and duplicate transmission of video packets. The proposed method transmits only the video signal with high priority for each video macroblock that is quite small in volume but very important for the reconstruction of the video. The proposed method significantly reduces the required bit-rate for duplicate transmission. An efficient packetization method is also proposed to reduce additional packet overhead which is required for transmitting the duplicate data. The duplicated high priority data of the Previous video slice is transmitted as a Piggyback to the data Packet of the current video slice. It is shown by simulations that the proposed method remarkably improves the packet loss-resilience for video transmission only with small increase of redundant duplicated data for each slice.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2031-2041
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• 1999

In this paper, we present the HVIS (Hybrid Video Information System) which bolsters up meaning retrieval of all the various users by integrating feature-based retrieval and annotation-based retrieval of unformatted formed and massive video data. HVIS divides a set of video into video document, sequence, scene and object to model the metadata and suggests the Two layered Hybrid Object-oriented Metadata Model(THOMM) which is composed of raw-data layer for physical video stream, metadata layer to support annotation-based retrieval, content-based retrieval, and similarity retrieval. Grounded on this model, we presents the video query language which make the annotation-based query, content-based query and similar query possible and Video Query Processor to process the query and query processing algorithm. Specially, We present the similarity expression to appear degree of similarity which considers interesting of user. The proposed system is implemented with Visual C++, ActiveX and ORACLE.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1399-1404
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• 2004

The development of computer technology and the advancement of the technology of information and communications spread the technology of multimedia and increased the use of multimedia data with large capacity. Users can grasp the overall video data and they are able to play wanted video back. To grasp the overall video data it is necessary to offer the list of summarized video data information. In order to search video efficiently an index process of video data is essential and it is also indispensable skill. Therefore, this thesis suggested the effective method about the cut detection of frames which will become a basis of an index based on contents of video image data. This suggested method was detected as the unchanging pixel rotor intelligence value, classified into diagonal direction. Pixel value of color detected in each frame of video data is stored as A(i, i) matrix - i is the number of frames, i is an image height of frame. By using the stored pixel value as the method of UE(Mean Square Error) I calculated a specified value difference between frames and detected cut quickly and exactly in case it is bigger than threshold value set in advance. To carry out an experiment on the cut detection of lames comprehensively, 1 experimented on many kinds of video, analyzing and comparing efficiency of the cut detection system.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.