• Journal of Korea Multimedia Society
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• v.23 no.6
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• pp.792-801
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• 2020

Today, the media environment has changed dramatically with the development of Internet transmission technology, computer performance, and various media production technologies. In particular, video content is widely used every day through mobile device or computer connected to the Internet. In addition, the ability to produce video content today is more important than ever, as the use of video content is also increasing in the area of information delivery and communication over the Internet. In this study, it was developed a liberal education curriculum in universities for the production of video content. The developed curriculum was composed of a combination of an online curriculum for theory and an offline curriculum for practicum. In the online curriculum, a 54 DOOR contents were developed to allow students to learn the theoretical knowledge and basic making techniques related to the production of video content. In an offline 15-week practicum curriculum, it covered the practical training contents of video content production that should be acquired every week. This developed liberal education curriculum aims to give non-major students in universities the opportunity to learn the technology of producing video content and to foster the ability to communicate in the media environment by using the video content produced.

• International Journal of Contents
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• v.12 no.1
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• pp.1-5
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• 2016

To manipulate large video contents, effective video indexing and retrieval are required. A large number of video indexing and retrieval algorithms have been presented for frame-wise user query or video content query whereas a relatively few video sequence matching algorithms have been proposed for video sequence query. In this paper, we propose an efficient algorithm that extracts key frames using color histograms and matches the video sequences using edge features. To effectively match video sequences with a low computational load, we make use of the key frames extracted by the cumulative measure and the distance between key frames, and compare two sets of key frames using the modified Hausdorff distance. Experimental results with real sequence show that the proposed video sequence matching algorithm using edge features yields the higher accuracy and performance than conventional methods such as histogram difference, Euclidean metric, Battachaya distance, and directed divergence methods.

• International Journal of Contents
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• v.6 no.3
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• pp.5-9
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• 2010

To manage large database system with video, effective video indexing and retrieval are required. A large number of video retrieval algorithms have been presented for frame-wise user query or video content query, whereas a few video identification algorithms have been proposed for video sequence query. In this paper, we propose an effective video identification algorithm for video sequence query that employs the Cauchy function of histograms between successive frames and the modified Hausdorff distance. To effectively match the video sequences with a low computational load, we make use of the key frames extracted by the cumulative Cauchy function and compare the set of key frames using the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed algorithm for video identification yields remarkably higher performance than conventional algorithms such as Euclidean metric, and directed divergence methods.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.86-91
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• 2024

In the field of artistic creation, the iterative development of AI-generated video software has pushed the boundaries of multimedia content creation and provided powerful creative tools for non-professionals. This paper extensively examines two leading AI-generated video software, PIKA Labs and RUNWAY, discussing their functions, performance differences, and application scopes in the video generation domain. Through detailed operational examples, a comparative analysis of their functionalities, as well as the advantages and limitations of each in generating video content, is presented. By comparison, it can be found that PIKA Labs and RUNWAY have excellent performance in stability and creativity. Therefore, the purpose of this study is to comprehensively elucidate the operating mechanisms of these two AI software, in order to intuitively demonstrate the advantages of each software. Simultaneously, this study provides valuable references for professionals and creators in the video production field, assisting them in selecting the most suitable tools for different scenarios, thereby advancing the application and development of AI-generated video software in multimedia content creation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.834-839
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• 2007

The superimposed text in video brings important semantic clues into content analysis. In this paper, we present the new and fast superimposed text localization method in video segments. We detect the superimposed text by using temporal information contained in the video. To detect the superimposed text fast, we have minimized the candidate region of localizing superimposed texts by using the difference between consecutive frames. Experimental results are presented to demonstrate the good performance of the new superimposed text localization algorithm.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.15 no.2
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• pp.97-115
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• 2004

The paper examined the concepts and features of content-based Image and Video retrieval systems. It then analyzed the retrieval performance of on five content_based retrieval systems in terms of usability and retrieval features. The results showed that the combination of content_based retrieval techniques and meta-data based retrieval will be able to improve the retrieval effectiveness.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.96-102
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• 2003

Video copy detection is a complementary approach to watermarking. As opposed to watermarking, which relies on inserting a distinct pattern into the video stream, video copy detection techniques match content-based signatures to detect copies of video. Existing typical content-based copy detection schemes have relied on image matching. This paper proposes two new sequence matching techniques for copy detection and compares the performance with color techniques that is the existing techniques. Motion, intensity and color-based signatures are compared in the context of copy detection. Comparison of experimental results are reported on detecting copies of movie clips.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.1
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• pp.10-14
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• 2015

Video Content owners seek to squeeze the maximum amount of revenue from their assets via distribution into more territories. Digital Cinema Package(DCP), trusted solution to distribute protected content to theaters, caters to relatively small user base, which limits revenue. With the growth of the Internet & other digital media, the economics of media content has changed dramatically. Security remains main concern to deliver content to millions of consumers using intelligent digital display devices like Tablets, Smartphones, Smart TVs, Desktop & Laptop. By making the video content available to this segment securely, content owners will benefit from increased revenue. Through this paper we propose Open Source HomeDCP model to distribute the content to home users for offline viewing. We propose to include other open source CODEC than JPEG2000/MPEG2, which are specifically designed for theatrical performance. Final image size will be further reduced considering the display device resolution where video will be finally played. Key Delivery Message(KDM) system to be altered to suit new devices. This will be a big boost to Content Economy as content owners would be able to distribute the content securely to the wider audience & ensure more revenue.

• Journal of Korea Multimedia Society
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• v.17 no.9
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• pp.1070-1075
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• 2014

Due to a number of illegal copied videos, many video content markets have been threatened. Since this copied videos have intercepted the profits of the content holders, content developers lose the will to generate new contents. Therefore, video copy detection approaches have been developed to protect the copyrights of video contents. However, many illegal uploader who generate copied videos used video transformations to avoid video copy detection systems. Among of the video transformations, rotation and flipping did not distorted the quality of video contents. Thus, these two video transformations were adopt to generate copied video. In order to detect rotated or flipping copy videos, rotation and flipping robust region binary pattern (RFR) recently was proposed. But, this RFR has a weakness according to rotated angles. Therefore, in order to overcome this problem, multi region binary patterns are proposed in this paper. The proposed method has the similar performance with the original RFR. But, it showed much higher efficiency for memory spaces.

• ETRI Journal
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• v.30 no.1
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• pp.101-112
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• 2008

In this paper, we present a method to customize the ground color in outdoor sports video to provide TV viewers with a better viewing experience or subjective satisfaction. This issue, related to content personalization, is becoming critical with the advent of mobile TV and interactive TV. In outdoor sports video, such as soccer video, it is sometimes observed that the ground color is not satisfactory to viewers. In this work, the proposed algorithm is focused on customizing the ground color to deliver a better viewing experience for viewers. The algorithm comprises three modules: ground detection, shot classification, and ground color customization. We customize the ground color by considering the difference between ground colors from both input video and the target ground patch. Experimental results show that the proposed scheme offers useful tools to provide a more comfortable viewing experience and that it is amenable to real-time performance, even in a software-based implementation.