• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.911-921
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• 2020

Recently, MPEG-I (Immersive) has been exploring compression performance through standardization projects for immersive video. The MPEG Immersion Video (MIV) standard technology is intended to provide limited 6DoF based on depth map-based image rendering (DIBR). MIV is a model that processes the Basic View and the residual information into an Additional View, which is a collection of patches. Atlases have the unique characteristics depending on the kind of the view they are included, requiring consideration of the compression efficiency. In this paper, the performance comparison analysis of screen content coding tools such as intra block copy (IBC) is conducted, based on the pattern of various views and patches repetition. It is demonstrated that the proposed method improves coding performance around -15.74% BD-rate reduction in the MIV.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.29-35
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• 2023

This paper makes it possible to search videos based on sentence by improving the previous research which automatically generates rich metadata from videos and searches videos by key words. For search by sentence, morphemes are analyzed for each sentence, keywords are extracted, weights are assigned to each keyword, and some videos are recommended by applying a ranking algorithm developed in the previous research. In order to evaluate performance of video search in this paper, a sufficient amount of videos and sufficient number of user experiences are re required. However, in the current situation where these are insufficient, three indirect evaluation methods were used: evaluation of overall user satisfaction, comparison of recommendation scores and user satisfaction, and evaluation of user satisfaction by video categories. As a result of performance evaluation, it was shown that the rich metadata construction and video recommendation implementation in this paper give users high search satisfaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.728-741
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• 2015

Stereo vision has become an important technical issue in the field of 3D imaging, machine vision, robotics, image analysis, and so on. The depth map extraction from stereo video is a key technology of stereoscopic 3D video requiring stereo correspondence algorithms. This is the matching process of the similarity measure for each disparity value, followed by an aggregation and optimization step. Since it requires a lot of computational power, there are significant speed-performance advantages when exploiting parallel processing available on processors. In this situation, multi-core CPU may allow many parallel programming technologies to be realized in users computing devices. This paper proposes parallel implementations for calculating disparity map using a shared memory programming and exploiting the streaming SIMD extension technology. By doing so, we can take advantage both of the hardware and software features of multi-core processor. For the performance evaluation, we implemented a parallel SAD algorithm with OpenMP and SSE2. Their processing speeds are compared with non parallel version on stereoscopic streaming video. The experimental results show that both technologies have a significant effect on the performance and achieve great improvements on processing speed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.9
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• pp.2053-2061
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• 1997

The motion comensated video coding technology is very improtant to compress video signal since it reduces the temporal redundancies in successive frames. But the computational complexity of the motion estimation(ME) is too enormous to use in the area of real-time and/or resolution video processing applications. To reduce the complexity of ME, fast search algoritjms and hardware design methods are developed. Especially, the three step search(TSS) is well known method which shows stable performance in various video sequences. And other variations of TSS are developed to get better performance andto reduce the complexity. In this paepr, we present the modified TSS using neighboring block's motion vectors to determine first step motion vector in TSS. The presented method uses the correlation of the adjacent blocks with same motion field. The simualtion resutls show that it has a good MAE performance and low complexity comparing with original TSS.

• International Journal of Contents
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• v.15 no.2
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• pp.20-28
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• 2019

This study was conducted with a non-equivalent control group experimental design to investigate the effects of a video-based infection control program through the application of the social cognitive theory on caregivers. Forty-six caregivers were recruited, with 23 pairs being randomly assigned to the control and experimental groups each. While the experimental group took part in the video-based education, the control group was involved in typical lectures. For two weeks, both groups were educated on the principles of infection control, medical and external handwashing, standard precautions, and quarantine. Their knowledge, performance, and self-efficacy were evaluated before and after the program. There was a significant increase in knowledge (p<.001 and p=.005) and infection control performance (p<.001) in the experimental and control groups. Similarly, self-efficacy, self-regulatory efficacy, task-difficulty preference and confidence significantly increased in the experimental group (p<.001). In the control group, only task-difficulty preference significantly increased (p=.005). Consequently, the online video-based infection control education program applying the social cognitive theory proved effective in improving the caregivers' knowledge and performance in infection control, and their self-efficacy. We suggest the use of this program in effective infection control education for caregivers in the future.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.229-234
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• 2021

The explosive growth of video-based services is considered as the dominant contributor to Internet traffic. Hence it is very important for video service providers to meet the quality expectations of end-users. In the past, the Quality of Service (QoS) was the key performance of networks but it considers only the network performances (e.g., bandwidth, delay, packet loss rate) which fail to give an indication of the satisfaction of users. Therefore, Quality of Experience (QoE) may allow content servers to be smarter and more efficient. This work is motivated by the inherent relationship between the QoE and the QoS. We present a no-reference (NR) prediction model based on Deep Neural Network (DNN) to predict video QoE. The DNN-based model shows a high correlation between the objective QoE measurement and QoE prediction. The performance of the proposed model was also evaluated and compared with other types of neural network architectures, and three known machine learning methodologies, the performance comparison shows that the proposed model appears as a promising way to solve the problems.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.31-42
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• 2005

Generally, conventional video searching or classification methods are based on its meta-data. However, it is almost Impossible to represent the precise information of a video data by its meta-data. Therefore, a processing method of video data that is based on its meta-data has a limitation to be efficiently applied in application fields. In this paper, for efficient classification of video data, a classification method of video data that is based on its low-level data is proposed. The proposed method extracts the characteristics of video data from the given video data by clustering process, and makes the profile of the video data. Subsequently. the similarity between the profile and video data to be classified is computed by a comparing process of the profile and the video data. Based on the similarity. the video data is classified properly. Furthermore, in order to improve the performance of the comparing process, generating and comparing techniques of integrated profile are presented. A comparing technique based on a differentiated weight to improve a result of a comparing Process Is also Presented. Finally, the performance of the proposed method is verified through a series of experiments using various video data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.374-395
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• 2017

Users quite often experience volatile channel conditions which negatively influence multimedia transmission. HTTP adaptive streaming has emerged as a new promising technology where the video quality can be adjusted to variable network conditions. Nevertheless, the new technology does not remain without drawbacks. As it has been observed, multiple video players sharing the same network link have often problems with achieving good efficiency and stability of play-out due to a mutual interference and competition among video players. Our investigation indicates that there may be another cause for under-performance of the streamed video. In an emulated environment, we implemented three algorithms of adaptive video play-out based on bandwidth or buffer assessment. As we show, traffic generated by players employing the same or similar play-out strategies is positively correlated and synchronised (clustered), whereas traffic originated from different play-out strategies shows negative or no correlations. However, when some of the parameters of the play-out strategies are randomised, the correlation and synchronisation diminish what has a positive impact on the smoothness of the traffic and on the video quality perceived by end users. Our research shows that non-correlated traffic flows generated by play-out strategies improve efficiency and stability of streamed adaptive video.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.2
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• pp.132-135
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• 2009

The video similarity measure is very important factor to index and to retrieve for video data. In this paper, we propose the luminance projection model to measure the video similarity efficiently. Most algorithms for video indexing have been commonly used histograms, edges, or motion features, whereas in this paper, the proposed algorithm is employed an efficient measure using the luminance projection. To index effectively the video sequences and to decrease the computational complexity, we calculate video similarity using the key frames extracted by the cumulative measure, and compare the set of key frames using the modified Hausdorff distance. Experimental results show that the proposed luminance projection model yields the remarkable accuracy and performance than the conventional algorithm.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.384-389
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• 2008

Recently, the necessity of systems which effectively store and retrieve video data has increased. Conventional video retrieval systems retrieve data using menus or text based keywords. Due to the lack of information, many video clips are simultaneously searched, and the user must have a certain level of knowledge to utilize the system. In this paper, we suggest a natural language based conversational video retrieval system that reflects users' intentions and includes more information than keyword based queries. This system can also retrieve from events or people to their movements. First, an event database is constructed based on meta-data which are generated by domain analysis for collected video in an office environment. Then, a script database is also constructed based on the query pre-processing and analysis. From that, a method to retrieve a video through a matching technique between natural language queries and answers is suggested and validated through performance and process evaluation for 10 users The natural language based retrieval system has shown its better efficiency in performance and user satisfaction than the menu based retrieval system.