• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.231-236
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• 2015

In this paper, the technical analysis and characteristics of screen content coding (SCC) based on High efficiency video coding (HEVC) are presented. For SCC, which is increasingly used these days, HEVC SCC standardization has been proceeded. Technologies such as intra block copy (IBC), palette coding, and adaptive color transform are developed and adopted to the HEVC SCC standard. This paper examines IBC and palette coding that significantly impacts RD performance of SCC for screen content. The HEVC SCC reference model (SCM) 4.0 was used to comparatively analyze the coding performance of HEVC SCC based on the HEVC range extension (RExt) model for screen content.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.115-125
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• 2018

This paper presents a fast partition decision framework for High Efficiency Video Coding (HEVC) Screen Content Coding (SCC) based on machine learning. Currently, the HEVC performs quad-tree block partitioning process to achieve optimal coding efficiency. Since this process requires a high computational complexity of the encoding device, the fast encoding process has been studied as determining the block structure early. However, in the case of the screen content video coding, it is difficult to apply the conventional early partition decision method because it shows different partition characteristics from natural content. The proposed method solves the problem by classifying the screen content blocks after partition decision, and it shows an increase of 3.11% BD-BR and 42% time reduction compared to the SCC common test condition.

• Journal of Broadcast Engineering
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• v.20 no.1
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• pp.57-67
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• 2015

This paper describes and analyzes IBC (intra block copy) in HEVC (high efficiency video coding) SCC (screen content coding) to improve the coding efficiency of IBC. HEVC SCC reference software SCM 2 is employed to analyze the selection ratio of IBC which is newly adopted in HEVC SCC, and the tools for IBC such as the block vector prediction and block vector coding method are evaluated. Experimental results show the average IBC selection ratio is 31.08% and 0.33% in I-Slice and B-Slice, respectively. Based on this results, the coding efficiency of IBC could be improved by utilizing IBC selectively. In addition, analysis tests of block vector prediction and the block vector coding method show the current methods are not efficient to screen content videos, and the analysis results are presented to improve these methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.183-188
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• 1998

This paper describes the concept of object-based coding for future broadcasting environments. Digital broadcasting uses the MPEG2 coding scheme which is regarded as a picture-based coding. An object-based coding scheme is a potential candidate for future broadcasting both for studio and distribution uses, and it offers a higher compression more flexible content handling. This paper also describes key technologies that we have been developing for the object-based coding, e.g., image analysis, object extraction and coding of objects.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.1-7
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• 2017

Joint Collaborative Team on Video Coding (JCT-VC) of ISO/IEC MPEG and ITU-T developed the HEVC Screen Content Coding (HEVC SCC) standard as the HEVC extension for the screen content video coding. The Intra Block Copy (IBC) is the most effective tool adopted in HEVC SCC and predicts current block from already reconstructed neighboring blocks in the same picture. To reduce the amount of data in BV (Block Vector) to be transmitted, a BV predictor (BVP) is used to generate the BV differences in the IBC BV coding. In this paper, we analyze the current BV prediction process using HEVC reference software SCM-2.0 and SCM-4.0. Based on the analysis results, we propose an improved BVP candidate selection algorithm by adding a search process for adjacent BVs in addition to the existing spatial BVP candidates. Experimental results show that the BD-rate reduction of our proposed improvements ranges from 0.2% to 1%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3149-3165
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• 2013

Content based image retrieval is increasingly gaining popularity among image repository systems as images are a big source of digital communication and information sharing. Identification of image content is done through feature extraction which is the key operation for a successful content based image retrieval system. In this paper content based image retrieval system has been developed by adopting a strategy of combining multiple features of shape, color and relevance feedback. Shape is served as a primary operation to identify images whereas color and relevance feedback have been used as supporting features to make the system more efficient and accurate. Shape features are estimated through second derivative, least square polynomial and shapes coding methods. Color is estimated through max-min mean of neighborhood intensities. A new technique has been introduced for relevance feedback without bothering the user.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.5_6
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• pp.305-320
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• 2004

Moving object segmentation is a fundamental function for content-based application. Moving object edges are produced by matching the detected moving edges with the current frame edges. But we can often experience the object edge disconnectedness due to coincidence of similarity between the object and background colors or the decrease of movement of moving object. The edge disconnectedness is a serious problem because it degrades the object visual quality so conspicuously That it sometimes makes it inadequate to perform content-based coding. We have solved this problem by developing a robust and comprehensive edge linking algorithm. And we also developed an automatic moving object segmentation algorithm. These algorithms can produce the completely linked moving object edge boundary and the accurate moving object segmentation. These algorithms can process CIF 30 frames/sec in a PC. These algorithms can be used for the MPEG-4 content-based coding.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.687-696
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• 2015

JCT-VC (Joint Collaborative Team on Video Coding) continues developing HEVC/Screen Content Coding (HEVC/SCC) as an extension for efficiently coding screen content videos, including computer animations, graphics, and electrical documents, based on HEVC/Range extension (HEVC/RExt.). Color space conversion from RGB space being captured from CCD sensors is popular in natural video coding. However, the conversion is often undesired for screen contents because of a significant loss in perceptual quality. Therefore, several coding tools including cross-component prediction (CCP) and in-loop adaptive color space transform (ACST) have been developed for an efficient screen content video coding in order to reduce the redundancies between color spaces while maintaining the original color space. In this paper, we review the two coding tools, i.e., CCP and ACST exploiting the correlation in the RGB color space and conduct the performance analysis of the coding tools. In our simulation results, CCP and ACST provide 11.7% BD-rate saving and 16.4% BD-rate saving, respectively, while the two coding tools provide 18.2% BD-rate saving in total. Following this idea, if the two coding tools are exclusively selected, we provide 93% encoding measurement time with a 0.3% coding loss.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.544-554
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• 2012

Screen content refers to images or videos generated by various electronic devices such as computers or mobile phones, whereas natural content refers to images captured by cameras. Screen contents show different statistical characteristics from natural images, so the conventional video codecs which were developed mainly for the coding of natural videos cannot guarantee good coding performances for screen contents. Recently, researches on efficient SCC(Screen Content Coding) are being actively studied, and especially at ongoing JCT-VC(Joint Collaborative Team on Video Coding) meeting for HEVC(High Efficiency Video Coding) standard, SCC issues are being discussed steadily. In this paper, we analyze the performances of high efficient coding tools in HM(HEVC Test Model) on SCC, and present an optimized SCC model based on the analysis results. We also present the characteristics of screen contents and the future research issues as well.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.198-205
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• 2022

Along with a variety of coding education, physical computing education for controlling various sensors is being actively conducted for elementary, middle, and high school students in line with the era of the fourth industrial revolution. A problem with physical computing education using Arduino is pin connection errors between Arduino and various sensors. Most of the students who come into contact with the Arduino for the first time often do not know the purpose of the Arduino pin and the connection position of the pin. Also, hardware built with incorrect pin connections to the Arduino board often does not work properly. If this case continues, students will lose interest in coding education. Therefore, in this paper, we implemented an augmented reality application that informs the connection process of the Arduino board and the sensor during physical computing coding education using Arduino, and designed and implemented educational content for the Arduino pin position and connection process. First, we explain the role of the Arduino board and the sensor and the location of the pins. After that, the students run the educational augmented reality educational content using their smartphones and check the correct pin connection process between the Arduino and the sensor. In the physical computing education, augmented reality content is used to increase the understanding and immersion of the class. It is expected that the educational effect will also increase by inducing fun and interest in physical computing coding education.