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研究生:連健琳
研究生(外文):Chien-Lin Lian
論文名稱:隨選視訊故事單元擷取及影片摘要瀏覽之研究
論文名稱(外文):Video Summary and Browsing Based on Story-Unit for Video-on-Demand Service
指導教授:李素瑛李素瑛引用關係
指導教授(外文):Suh-Yin Lee
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1999
畢業學年度:87
語文別:英文
論文頁數:70
中文關鍵詞:視訊摘要視訊瀏覽場景變化偵測視訊叢聚視訊特徵辨認
外文關鍵詞:Video SummaryVideo BrowsingScene Change DetectionVideo ClusteringVideo Characterization
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隨選視訊服務是最熱門的多媒體應用之一。不管在什麼時間或地點,使用者都可以透過隨選視訊服務看到電視影片、電影及新聞等。然而,隨著影片的數量達上千個小時,在查詢後我們仍會得到許多符合的影片。為了要在短時間內挑選出我們最想要的影片,我們需要一個有效率的影片摘要方法以萃取影片的概要內容。
考慮到影片是由一些故事單元所組成,我們提出一個影片摘要方法,從影片的每個故事單元中擷取出最重要的影片片段,並把這些片段合成一個影片摘要。這個影片摘要大約只有原來影片長度的十分之一,但重要的是,使用者在看過這段影片摘要後,便能快速的瞭解到原來影片的大致內容及架構。為了開發這個影片摘要系統,我們提出一些視訊處理的技術,包括先將影片做切割的視訊切割方法,將視訊片段叢聚成故事單元的視訊叢聚方法及從故事單元中擷取重要片段的方法。我們提出一個以GOP為基礎的視訊切割方法,這個方法比一般以frame為基礎的視訊切割方法節省相當多時間。在視訊叢聚上,我們的演算法可以有效率的將影片片段叢聚成故事單元。最後我們也定義了重要影片片段以及從故事單元中擷取這些片段的演算法。另外,我們也提供了一個整合的影片摘要環境,包括視訊影音分離系統、視訊編輯系統、影片摘要系統和視訊影音整合系統。系統的全部工作都是自動的不需人工介入,而且透過實驗,我們也得到令人滿意的結果。

Video on Demand ( VOD ) service is one of the most popular multimedia applications. By VOD services people can watch TV programs, movies, or news whenever they want or somewhere far away from server. However, with the volume of videos growing to thousands of hours, there may be lots of matched videos in the query result. For getting the most desired video in a short time, we need an effective approach to summarize the video for providing the overall view of videos.
Considering that the video is composed of story units, we propose a video summary system which extracts the significant segments from each story unit of the video and combines them into an abstracted video. This abstracted video is about one-tenth of the original video in time-length and more importantly, presents the critical information such that users can rapidly realize the synopsis and have an overall view of the original video. To implement the video summary system, some techniques of video processing have been proposed in our system, including segmenting the video as shots, clustering the shots into story-units and selecting the significant shots from each story-unit. In shot segmentation, we propose GOP-based scene change detection approach, which saves more processing time than scene change detection frame by frame. In shot clustering, we design a shot clustering algorithm to effectively cluster shots into story units. We also define the significant shots and present the algorithms to extract the significant shots in a video. In addition, we provide an integrated environment for video summary system, such as audio-video splitter, audio-video editor, video summary and audio-video combiner. All tasks in this system are done automatically and the results of experiment prove to be justifiable and satisfactory.

Abstract in Chinese……………………………………………………………………i
Abstract……………………………………………………………………………….iii
Acknowledgement……………………………………………………………………v
Table of Contents……………………………………………………………………..vi
List of Figures……………………………………………………………………….viii
List of Tables………………………………………………………………………..x
List of Algorithms………………………………………………………………xi
Chapter 1 Introduction……………………………………………......1
1.1 Motivation……………………………………………………………………1
1.2 Organization………………………………………………………………….3
Chapter 2 Background…………………………………………………………………5
2.1 Overview of MPEG-II standard……………………………………………...5
2.1.1 Structure of coded video data…………………………………………6
2.1.2 Picture layer of coded video data……………………………………..7
2.2 Scene change detection method……………………………………………...9
2.2.1 Pixel-domain scene change detection…………………………………9
2.2.2 Histogram comparison.……………………………………………...10
2.2.3 DCT coefficients and motion-vector based approach……………….11
2.2.4 Feature-based approach……………………………………………...12
2.3 Clustering method……………………………………………………...13
2.4 Other video summary method………………………………………………16
Chapter 3 Video summary based on story-unit………………………………………20
3.1 Overview of video summary based on story-unit…………………………..20
3.2 Shot segmentation…………………………………………………………..22
3.2.1 Non-sequential scene change detection approach…………………..22
3.2.2 GOP-based scene change detection approach………………………23
3.2.2.1 Detecting the possible scene change by the unit of GOP……25
3.2.2.2 Finding out the actual scene change in the GOP…………….27
3.2.3 Evaluation of GOP-based scene change detection approach………..30
3.3 Shot clustering………………………………………………………….32
3.3.1 Definition in shot clustering……………………………………….33
3.3.2 Algorithm of shot clustering……………………………………….35
3.4 Shot selection……………………………………………………………….39
3.4.1 Shot characterization………………………………………………...39
3.4.1.1 Detecting dialogue shots..…..………………………………40
3.4.1.2 Detecting action shots………………………………………42
3.4.1.3 Detecting still shots…………………………………………43
3.4.2 Shot selection rule…………………………………………………...44
Chapter 4 System architecture and experiment……………………………………..46
4.1 Overview of video summary system………………………………………46
4.2 Audio-Video splitter module………………………………………………49
4.3 Audio-Video combiner module……………………………………………50
4.4 Video summary module……………………………………………………51
4.4.1 Component of video segmentation ………………………………….51
4.4.2 Component of video clustering .…………………………………53
4.4.3 Components of video characterization and representative selection ..53
4.5 Audio-Video editor module………………………………………………56
4.5.1 Video editor module…………………………………………………56
4.5.2 Audio editor module…………………………………………………57
4.6 Experiment and analysis…………………………………………………….59
Chapter 5 Conclusion and future work……………...……………………………….66
Bibliography………………………………………………………………………….68

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