• Journal of KIISE:Computer Systems and Theory
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• v.31 no.12
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• pp.673-681
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• 2004

As the use of digital videos is getting popular, there is an increasing demand for efficient browsing and retrieval of video. To support such operations, effective video indexing should be incorporated. One of the most fundamental steps in video indexing is to parse video stream into shots and scenes. Generally, it takes long time to parse a video due to the huge amount of computation in a traditional single computing environment. Previous studies had widely used Round Robin scheduling which basically allocates tasks to each slave for a time interval of one quantum. This scheduling is difficult to adapt in a heterogeneous environment. In this paper, we propose two different parallel parsing algorithms which are Size-Adaptive Round Robin and Dynamic Size-Adaptive Round Robin for the heterogeneous distributed computing environments. In order to show their performance, we perform several experiments and show some of the results.

• The Magazine of the IEIE
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• v.28 no.2
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• pp.117-117
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• 2001

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.868-876
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• 1999

In this paper, an efficient automatic video parsing technique on MPEG-compressed video that is fundamental for content-based indexing is described. The proposed method detects scene changes, regardless of IPB picture composition. To detect abrupt changes, the difference measure based on the dc coefficient in I picture and the macroblock reference feature in P and B pictures are utilized. For gradual scene changes, we use the macroblock reference information in P and B pictures. the process of scene change detection can be efficiently handled by extracting necessary data without full decoding of MPEG sequence. The performance of the proposed algorithm is analyzed based on precision and recall. the experimental results verified the effectiveness of the method for detecting scene changes of various MPEG sequences.

• Journal of KIISE:Software and Applications
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• v.27 no.1
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• pp.74-83
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• 2000

There is a rapid increase in the use of digital video information in recent years. Especially, user requires the environment which retrieves video from passive access to active access, to be more efficiently. we need to implement video retrieval system including video parsing, clustering, and browsing to satisfy user's requirement. In this paper, we first divide video sequence to shots which are primary unit for automatic indexing, using a hybrid method with mixing histogram method and pixel-based method. After the shot boundaries are detected, corresponding key frames can be extracted. Key frames are very important portion because they help to understand overall contents of video. In this paper, we first analyze camera operation in video and then select different number of key frames depend on shot complexity. At last, we compose panorama images from shots which are containing panning or tilting in order to provide more useful and understandable browsing environment to users.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.109-112
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• 2001

This paper proposes an efficient algorithm of extracting anchor blocks for a semantic structure of a news video. We define the FRFD to calculate the frame difference of anchor face position rather than simply uses the general frame difference. Since, The FRFD value is sensitive to existing face in frame, anchor block can be efficiently extracted. In this paper, an algorithm to extract a face position using partial decoded MPEG data is also proposed. In this way a news video can be structured semantically using the extracted anchor blocks.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.552-557
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• 2017

Wire, wireless digital broadcasting has sharply expanded with the birth of high definition TV since 2010, the use of duplex contents as well as simplex contents has rapidly increased. Currently, our satellite communications system adopted DVB by European digital broadcasting standardization organization as a standard of domestic data broadcasting, the method how to use selective contents has been studied variously according to the development of IPTV. Digital broadcasting utilizes the method using Transport Stream Packet(TSP) by the way of multiplexing of information in order to send multimedia information such as video, audio and data of MPEG-2, this streams include detail information on TV guide and program as well as video and audio information. In order to understand these data broadcasting system, this study realized TS analyzer that divides transport stream (TS) by packet in Linux environment, analyzes and prints by function, it can help the understanding of TS, the enhancement of stream parsing ability.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.635-638
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• 2008

Unlike traditional analog broadcasting, digital broadcasting provides users with various additional services that we have never seen before. To receive these kind of services. data broadcasting includes not only audio, video signal, but also additional data associated with the program. In this paper, we present the efficient parsing and caching mechianism for data carousel in digital broadcasting set-top box. In order to speed up the process of parsing, we use the Message Pool that stores elementary_pid syntax of DSM-CC message packets.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.346-352
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• 2020

Versatile Video Coding (VVC), a next-generation video coding standard that is in the final stage of standardization, has adopted various techniques to achieve more than twice the compression performance of HEVC (High-Efficiency Video Coding). VVC adopted Bi-prediction with CU-level Weight (BCW), which generates the final prediction signal with the weighted combination of bi-predictions with various weights, to enhance the performance of the bi-predictive inter prediction. The syntax element of the BCW index is adaptively coded according to the value of NoBackwardPredFlag which indicates if there is no future picture in the display order among the reference pictures. Such syntax structure for signaling the BCW index could violate the flexibility of video codec and cause the dependency issue at the stage of bitstream parsing. To address these issues, this paper proposes an efficient BCW weight signaling method which enables all weights and parsing without any condition check. The performance of the proposed method was evaluated with various weight searching methods in the encoder. The experimental results show that the proposed method gives negligible BD-rate losses and minor gains for 3 weights searching and 5 weights searching, respectively, while resolving the issues.

• 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.

• Journal of Information Technology Applications and Management
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• v.9 no.3
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• pp.1-12
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• 2002

Due to the fast development in computer and communication technologies, a video is now being more widely used than ever in many areas. The current information analyzing systems are originally built to process text-based data. Thus, it has little bits problems when it needs to correctly represent the ambiguity of a video, when it has to process a large amount of comments, or when it lacks the objectivity that the jobs require. We would like to purpose an algorithm that is capable of analyze a large amount of video efficiently. In a video, divided areas use a region growing and region merging techniques. To sample the color, we translate the color from RGB to HSI and use the information that matches with the representative colors. To sample the shape information, we use improved moment invariants(IMI) so that we can solve many problems of histogram intersection caused by current IMI and Jain. Sampled information on characteristics of the streaming media will be used to find similar frames.