• The KIPS Transactions:PartB
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• v.9B no.1
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• pp.105-112
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• 2002

In order to process video data effectively, we need to save its content on database and a content-based retrieval method which processes various queries of all users is required. In this paper, we present VRS(Video Retrieval System) which provides similarity query, SQBE(Scene Query By Example) query, and content-based retrieval by combining the feature-based retrieval and the annotation-based retrieval. The SQBE query makes it possible for a user to retrieve scones more exactly by inserting and deleting objects based on a retrieved scene. We proposed query language and query processing algorithm for SQBE query, and carried out performance evaluation on similarity retrieval. The proposed system is implemented with Visual C++ and Oracle.

• Journal of information and communication convergence engineering
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• v.11 no.1
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• pp.62-68
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• 2013

This paper proposes a method for identification of harmful video images based on the degree of harmfulness in the video content. To extract harmful candidate frames from the video effectively, we used a video color extraction method applying a projection map. The procedure for identifying the harmful video has five steps, first, extract the I-frames from the video and map them onto projection map. Next, calculate the similarity and select the potentially harmful, then identify the harmful images by comparing the similarity measurement value. The method estimates similarity between the extracted frames and normative images using the critical value of the projection map. Based on our experimental test, we propose how the harmful candidate frames are extracted and compared with normative images. The various experimental data proved that the image identification method based on the 2-dimensional projection map is superior to using the color histogram technique in harmful image detection performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.129-137
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• 2019

New technology leads the fast changes to cope with the fourth industrial revolution in the modern society. To meet the changes of the times, not only daily life but also cultural life of the modern people are changing, and these changed the times when we watch the cultural performances such as drama, musical, and opera that we should appreciate onsite into the times to see them whenever and wherever we want to see upon digitalizing them. However, studies have not been actively performed on the performance videos unlike other areas probably since they expect better image quality and services. Hence, considering the expectations from performance video contents by consumers as benefit, convenience, and innovation, this study was conducted to review the effectiveness of these attributes on the trust and satisfaction level. Also, upon reviewing the effectiveness of these on the word of mouth effect, expansion potential of the performance video contents was investigated to deduct the meaningful implications. Study results showed that benefit and convenience affected trust and satisfaction positively while innovation did not affect them at all. Yet, trust and satisfaction showed the positive influence on the word of mouth effect.

• Journal of Digital Convergence
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• v.12 no.2
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• pp.315-320
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• 2014

Recently, although the requirement of quality of VOD content has been transferred upto the class of HD, conventional management systems characterized by OS dependency are truly limited in quality of video image, stability, and compatibility of network environments. In addition most of the content management systems realize very limited capabilities for the real affairs of content management and distribution services in such an OS dependent environment. In this paper, we propose a new scheme of HD-Class VOD Content Management System to solve these problems. We design and implement the proposed system based on open sources by using H.264 video compression method. The proposed system offers high quality content management method based on opened systems and independent on-line distribution method so that it can be realized as an integrated management scheme for VOD contents. Moreover, our system solves the problems of occasional cutting-down video, small screen, and poor image quality that exist in the conventional wmv-type CMS. According to the result of performance evaluation, our system maintains sufficient performance and tolerence for the case of large scale HD content operations or fabrications. We expect that the proposed integrated DB scheme will especially be effective when the content management applications are changed from Internet Web environments to mobile terminal environments.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.18-30
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• 2009

Recently, although the requirement of quality of VOD content has been transferred upto the class of HD, conventional management systems characterized by OS dependency are truly limited in quality of video image, stability, and compatibility of network environments. In addition most of the content management systems realize very limited capabilities for the real affairs of content management and distribution services in such an OS dependent environment. In this paper, we propose a new scheme of HD-Class VOD Content Management System to solve these problems. We design and implement the proposed system based on open sources by using H.264 video compression method. The proposed system offers high quality content management method based on opened systems and independent on-line distribution method so that it can be realized as an integrated management scheme for VOD contents. Moreover, our system solves the problems of occasional cutting-down video, small screen, and poor image quality that exist in the conventional wmv-type CMS. According to the result of performance evaluation, our system maintains sufficient performance and tolerence for the case of large scale HD content operations or fabrications. We expect that the proposed integrated DB scheme will especially be effective when the content management applications are changed from Internet Web environments to mobile terminal environments.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.1
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• pp.47-54
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• 2012

Video contents in the mobile environment are processed with the low bit-rate relative to normal video contents due to the consideration of network traffic; hence, it is necessary to protect the copyright of the low bit-rate video contents. The algorithm for watermarking appropriate for the mobile environment should be developed because the performance of the mobile devices is much lower than that of personal computers. This paper suggested the invisible spread spectrum watermarking method to the low bit-rate video contents, considering the low performance of the mobile device in the M-Commerce environment; it also enables to track down illegal users of the video contents to protect the copyright. The robustness of the contents with watermark is expressed with the correlation of extraction algorithm from watermark removed or distorted contents. The results of our experiment showed that we could extract the innate frequencies of M-Sequence when we extracted M-Sequence after compressing the contents with watermark easily. Therefore, illegal users of the contents can be tracked down because watermark can be extracted from the low bit-rate video contents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.308-324
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• 2018

HTTP adaptive streaming (HAS) is a promising technology for delivering video content over the Internet. HAS-based video streaming solutions rely on bandwidth estimation to select the appropriate video bitrate. Video streaming solutions that consider network conditions provide users with seamless video playback. However, when multiple clients compete for a common bottleneck link, conventional bandwidth estimation schemes that consider only one client overestimate the network bandwidth due to the ON-OFF traffic pattern. The bandwidth overestimation can cause Quality of Experience (QoE) degradation, such as unnecessary changes in video quality, and unfairness of video quality. In this paper, we propose a client-side bandwidth estimation scheme to obtain a better QoE of HAS in the multiple-client environment. The proposed scheme differentiates the client buffer status according to the buffer occupancy, and then estimates the available network bandwidth based on the buffer status and segment throughput. We evaluate the performance of HAS implemented in the ns-3 network simulator. Simulation results show that compared with the conventional schemes, the proposed scheme can enhance the QoE.

• Journal of the Korea Society of Computer and Information
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• v.21 no.2
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• pp.89-96
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• 2016

This study analyzed with the case analysis of a series of processes from job analysis survey. And results analysis, and academic achievement in order to transform the curriculum of existing courses into the curriculum of NCS-based courses. Also this study analysed of the existing curriculum. Also analyzed the trend of workforce trends and needs of the broadcasting content industry. Through a needs analysis for the industry and alumni and students, video graphics, video editing and video directing were selected. In this paper, it dealt mainly with respect to the video graphics in a dual job. Modeling capability into the unit through a job analysis, animation, effects and lighting were chosen accordingly based introduction of 3D Graphics. Application of 3D Graphics were derived two courses and selected profiles and performance criteria. This training according to the NCS curriculum for students was evaluated based on the student's job was to investigate the learning ability. Academic achievement were the result satisfaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.677-687
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• 2022

We propose a detection algorithm based on tree-structured convolutional neural networks (TsCNNs) that finds pornography, propaganda, or other inappropriate content on a social media network. The algorithm sequentially applies the typical convolutional neural network (CNN) algorithm in a tree-like structure to minimize classification errors in similar classes, and thus improves accuracy. We implemented the detection system and conducted experiments on a data set comprised of 6 ordinary classes and 11 inappropriate classes collected from the Korean military social network. Each model of the proposed algorithm was trained, and the performance was then evaluated according to the images and videos identified. Experimental results with 20,005 new images showed that the overall accuracy in image identification achieved a high-performance level of 99.51%, and the effectiveness of the algorithm reduced identification errors by the typical CNN algorithm by 64.87 %. By reducing false alarms in video identification from the domain, the TsCNNs achieved optimal performance of 98.11% when using 10 minutes frame-sampling intervals. This indicates that classification through proper sampling contributes to the reduction of computational burden and false alarms.