• ETRI Journal
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• v.31 no.1
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• pp.1-9
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• 2009

Frequency domain analysis is a fundamental procedure for understanding the characteristics of visual data. Several studies have been conducted with 2D videos, but analysis of stereoscopic 3D videos is rarely carried out. In this paper, we derive the Fourier transform of a simplified 3D video signal and analyze how a 3D video is influenced by disparity and motion in terms of temporal aliasing. It is already known that object motion affects temporal frequency characteristics of a time-varying image sequence. In our analysis, we show that a 3D video is influenced not only by motion but also by disparity. Based on this conclusion, we present a temporal anti-aliasing filter for a 3D video. Since the human process of depth perception mainly determines the quality of a reproduced 3D image, 2D image processing techniques are not directly applicable to 3D images. The analysis presented in this paper will be useful for reducing undesirable visual artifacts in 3D video as well as for assisting the development of relevant technologies.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1641-1652
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• 2021

To analyze the crime scene, the role of digital evidence such as CCTV and black box is very important. Such digital evidence is often damaged due to device defects or intentional deletion. In this case, the deleted video can be restored by well-known techniques like the frame-based recovery method. Especially, the data such as the video can be generally fragmented and saved in the case of the memory used almost fully. If the fragmented video were recovered in units of images, the sequence of the recovered images may not be continuous. In this paper, we proposed a new video restoration method to match the sequence of recovered images. First, the images are recovered through a frame-based recovery technique. Then, after analyzing the time information marked on the images, the time information was extracted and recognized via optical character recognition (OCR). Finally, the recovered images are rearranged based on the time information obtained by OCR. For performance evaluation, we evaluate the recovery rate of our proposed video restoration method. As a result, it was shown that the recovery rate for the fragmented video was recovered from a minimum of about 47% to a maximum of 98%.

• Journal of Distribution Science
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• v.21 no.9
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• pp.43-53
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• 2023

Purpose: This study aims to examine the demographic similarities and differences in objectives, methodology, and findings of previous studies in the context of gaining financial literacy using videos. This study employs a systematic review design. Research design, data and methodology: Based on the content analysis method, 15 articles were chosen from Scopus and Science Direct during 2015-2020. After formulating the research questions, the paper identification process, screening, eligibility, and quality appraisal are discussed in the methodology. The keywords for the advanced search included "Financial literacy," "Financial Education," and "Video". Results: The results of this study indicate the effectiveness of learning financial literacy using videos. Significant results were obtained when students interacted with the video content distribution. The findings of this study provide an overview and lead to a better understanding of the use of video in financial literacy. Conclusions: This study is important as a guide for educators in future research and practice planning. A systematic review on this topic is the research gap. Video learning was active learning that involved student-centered activities that help students engage with financial literacy. By conducting a systematic review, researchers and readers may also understand how extending an individual's financial literacy may change after financial education.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.1-13
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• 2020

In order to understand the recommendation algorithm applied to the online video platform, this study examines the relationship between the content characteristics of K-pop music videos and related videos recommended for playback on YouTube, and analyses which videos are recommended as related videos through network analysis. As a result, the more liked videos, the higher recommendation ranking and most of the videos belonging to the same channel or produced by the same agency were recommended as related videos. As a result of the network analysis of the related video, the network of K-pop music video is strongly formed, and the BTS music video is highly centralized in the network analysis of the related video. These results suggest that the network between K-pops is strong, so when you enter K-pop as a search query and watch videos, you can enjoy K-pop continuously. But when watching other genres of video, K-pop may not be recommended as a related video.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.17-25
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• 2022

Hash functions are widely used for fast equality analysis of video files because of their fixed small output sizes regardless of their input sizes. However, the hash function has the possibility of a hash collision in which different inputs derive the same output value, so there is a problem that different video files may be mistaken for the same file. In this paper, we propose an equality analysis scheme in which different video files always derive different output values using identifier information and double hash. The scheme first extracts the identifier information of an original video file, and attaches it into the end of the original file with a steganography method. Next the scheme calculates two hash output values of the original file and the extended file with attached identifier information. Finally the scheme utilizes the identifier information, the hash output value of the original file, and the hash output value of the extended file for the equality analysis of video files. For evaluation, we implement the proposed scheme into a practical software tool and show that the proposed scheme performs well the equality analysis of video files without hash collision problem and increases the resistance against the malicious hash collision attack.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.31-40
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• 2021

The multi-channel video streaming system is an essential device for future ground combat vehicles. For the system, the application of digital interfaces is required instead of the direct analog method to support selectable multiple channels. However, due to the characteristics of the digital interfaces that require en/decoding and signal conversion, the system should support the ability to adapt to quality and delay requirements depending on how video data is utilized. To support addressed issue, this study designs and emulates the multi-channel compressed-video streaming system of ground combat vehicle's fire control system based on commercial standards. Using the system, this study analyzes the quality of video according to the rotational speed of the acquisition device and Glass-to-Glass (G2G) delay between video acquisition and display devices according to video compression rates. Through these experiments and analysis, this paper presents the design direction of the system having scalability on the latest technology while providing high-quality video data streaming flexibly.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.213-216
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• 2022

There has been an increase in video surveillance for public safety and security, which increases the video data, leading to analysis, and storage issues. Furthermore, most surveillance videos contain an empty frame of hours of video footage; thus, extracting useful information is crucial. The prominent framework used in surveillance for efficient storage and analysis is video synopsis. However, the existing video synopsis procedure is not applicable for creating an abnormal object-based synopsis. Therefore, we proposed a lightweight synopsis methodology that initially detects and extracts abnormal foreground objects and their respective backgrounds, which is stitched to construct a synopsis.

• ETRI Journal
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• v.27 no.6
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• pp.806-809
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• 2005

The main goal of this research is automatic video analysis using a face recognition technique. In this paper, an automatic video management system is introduced with a variety of functions enabled, such as index, edit, summarize, and retrieve multimedia data. The automatic management tool utilizes MPEG-7 visual descriptors to generate a video index for creating a summary. The resulting index generates a preview of a movie, and allows non-linear access with thumbnails. In addition, the index supports the searching of shots similar to a desired one within saved video sequences. Moreover, a face recognition technique is utilized to personalbased video summarization and indexing in stored video data.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.827-831
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• 2002

MediaGIS is a concept of tightly integrated multimedia with spatial information. VideoGIS is an example of MediaGIS focused on the interaction or interaction of video and spatial information. Our suggested GeoVideo, a new concept of VideoGIS has its key feature in interactiveness. In GeoVideo, the geographic tasks such as browsing, searching, querying, spatial analysis can be performed based on video itself. GeoVideo can have the meaning of paradigm shift from artificial, static, abstracted and graphical paradigm to natural, dynamic, real, and image-based paradigm. We discuss about the integration of video and geography and also suggest the GeoVideo system design. Several considerations on expanding the functionalities of GeoVideo are explained for the future works.

• 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.