• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.886-895
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• 2018

In this paper, we introduce a technique to detect the video forgery by using the regularity that occurs in the video compression process. The proposed method uses the hierarchical regularity lost by the video double compression and the frame deletion. In order to extract such irregularities, the depth information of CU and TU, which are basic units of HEVC, is used. For improving performance, we make a depth map of CU and TU using local information, and then create input data by grouping them in GoP units. We made a decision whether or not the video is double-compressed and forged by using a general three-dimensional convolutional neural network. Experimental results show that it is more effective to detect whether or not the video is forged compared with the results using the existing machine learning algorithm.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.812-815
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• 2022

Images displayed on a digital devices under the sunlight are generally perceived to be darker than the original images, which leads to a decrease in visibility. For better visibility, global luminance compensation or tone mapping adaptive to ambient lighting is required. However, the existing methods have limitations in chrominance compensation and are difficult to use in real world due to their heavy computational cost. To solve these problems, this paper propose a piece-wise linear curves (PLECs)-based image enhancement method to improve both luminance and chrominance. At this time, PLECs are regressed through deep learning and implemented in the form of a lookup table to real-time operation. Experimental results show that the proposed method has better visibility compared to the original image with low computational cost.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.738-750
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• 2022

In this study, we reviewed and discussed whether auditory stimuli with short length can evoke emotion-related neurological responses. The findings implicate that if personalized sound tracks are provided to XR users based on machine learning or probability network models, user experiences in XR environment can be enhanced. We also investigated that the arousal-relaxed factor evoked by short auditory sound can make distinct patterns in functional connectivity characterized from background EEG signals. We found that coherence in the right hemisphere increases in sound-evoked arousal state, and vice versa in relaxed state. Our findings can be practically utilized in developing XR sound bio-feedback system which can provide preference sound to users for highly immersive XR experiences.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.561-568
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• 2022

Most deep learning-based video object segmentation methods perform the segmentation with past prediction information stored in external memory. In general, the more past information is stored in the memory, the better results can be obtained by accumulating evidence for various changes in the objects of interest. However, all information cannot be stored in the memory due to hardware limitations, resulting in performance degradation. In this paper, we propose a method of storing new information in the external memory without additional memory allocation. Specifically, after calculating the attention score between the existing memory and the information to be newly stored, new information is added to the corresponding memory according to each score. In this way, the method works robustly because the attention mechanism reflects the object changes well without using additional memory. In addition, the update rate is adaptively determined according to the accumulated number of matches in the memory so that the frequently updated samples store more information to maintain reliable information.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.132-140
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• 2023

In this paper, we propose a method for performance enhancement of speech declipping using clipping detector. Clipping occurs when the input speech level exceeds the dynamic range of microphone, and it significantly degrades the speech quality. Recently, many methods for high-performance speech declipping based on machine learning have been developed. However, they often deteriorate the speech signal because of degradation in signal reconstruction process when the degree of clipping is not high. To solve this problem, we propose a new approach that combines the declipping network and clipping detector, which enables a selective declipping operation depending on the clipping level and provides high-quality speech in all clipping levels. We measured the declipping performance using various metrics and confirmed that the proposed method improves the average performance over all clipping levels, compared with the conventional methods, and greatly improves the performance when the clipping distortion is small.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.42-54
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• 2023

It is an important issue to compress huge holographic data in a digital format. In particular, research on the compression of phase-only holograms for commercialization is noteworthy. Conventional video coding standards optimized for natural images are not suitable for compressing phase signals, and neural network-based compression model that can be optimized for phase signals can achieve high performance, but has a memory issue in learning high-resolution holographic data. In this paper, we show that by applying a block-based learned image compression model that can solve memory problems to phase-only holograms, the proposed method can demonstrate significant performance improvement over standard codecs even under the same conditions as block-based. Block-based learned compression model can provide compatibility with conventional standard codecs, solve memory problems, and can perform significantly better against phase-only hologram compression.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.1
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• pp.233-258
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• 2022

The purpose of this study is to identify elementary school teachers' needs for access points when searching for picture books to be used as teaching media, and suggest ways to improve DLS(Digital Library System) in school libraries. To achieve this purpose, the study examined the access points provided by OPAC(Online Public Access Catalog) systems in seven domestic and foreign libraries. In addition, it conducted an online survey with elementary school teachers, and a total of 220 responses were finally analyzed. It was found that the most needed access points were topic, grade/age, content, subject/chapter, and cross-curricula learning topics, etc. Based on the results, this study suggests providing the most needed access points in DLS, developing controlled vocabulary tools, and improving system functions or the interface to enhance accessibility to picture books.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.415-422
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• 2022

This study requires a new paradigm for universities in line with the global pandemic and the 4th industrial revolution. Accordingly, we propose an educational plan for the H university online graduate school in Korea. As a research method, the implications of scholars and experts on future education were synthesized, and the cases of overseas universities using it were analyzed to propose an online graduate school education plan. As a result of the study, online graduate school needs diversity as a venue for providing new opportunities as lifelong education, and to realize this, they use a microcredit. Blockchain technology is introduced so that microcredit can be transparently verified. In addition, to correspond to various convergence major programs and further develop them, problem-solving-oriented teaching methods enhance students' convergent skills as well as active learning and interaction. More detailed curriculum research at online graduate schools is needed in the future, and we hope that you will contribute to the development of online graduate school education based on this study.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.377-379
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• 2020

The hologram data is having a dependence on the pixel pitch of the SLM (spatial light modulator) and the wavelength of light, and the quality of the digital hologram is proportional to the unit pixel pitch and the total resolution. In addition, since each pixel has a complex value, the amount of data in the digital hologram also increases exponentially, and the size is bound to be very large. Therefore, in order to efficiently handle digital hologram files, it is essential to reduce the file size through a codec and store it. Recently, research on enhancing image quality damaged by the codec is actively underway. In this paper, the hologram image of JPEG Pleno, which is the standard hologram data, was used, and the image quality damage that occurs whenthe holographic image is encoded and decoded through the JPEG2000, AVC, and HEVC codec is enhanced with a deep learning network to find out whether the image quality can be improved. we also compare and quantitatively find out the degree of improvement in image quality.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.15-20
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• 2024

Local governments are conducting smartphone usage training for the elderly to bridge the information gap caused by a super-aged society. However, the one-to-many educational approach has limitations, and the elderly face difficulties due to insufficient learning effectiveness. This study proposes an educational service that can be used in offline training settings, considering an environment where the elderly can repeatedly learn to address these issues. This service utilizes generative AI to identify the parts that users find challenging and provides personalized problems for individualized practice. Integrating this app with existing local government training programs is expected to significantly enhance the efficiency of smartphone education in terms of personalized 1:1 training, time management, and the appropriateness of educational content.