• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.392-400
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• 2005

Highly integrated MPEG-4@SP video compression engine, VideoCore, is proposed for mobile application. The primary components of video compression require the high memory bandwidth since they access the external memory frequently. They include motion estimation, motion compensation, quantization, discrete cosine transform, variable length coding, and so on. The motion estimation processor adopted in VideoCore utilizes the small-size local memories such that the video compression system accesses external memory as less frequently as possible. The entire video compression system is divided into two distinct sub-systems: the integer-unit motion estimation part and the others, and both operate concurrently in a pipelined architecture. Thus the VideoCore enables the real-time high-quality video compression with a relatively low operation frequency.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.2
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• pp.408-427
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• 2011

Intensive computational complexity is an obstacle of enabling multiview video coding for real-time applications. In this paper, we present a fast macroblock (MB) mode selection algorithm for B frames which are based on the computational complexity analyses between the MB mode selection and reference frame selection. Three strategies are proposed to reduce the coding complexity jointly. First, the temporal correlation of MB modes between current MB and its temporal corresponding MBs is utilized to reduce computational complexity in determining the optimal MB mode. Secondly, Lagrangian cost of SKIP mode is compared with that of Inter $16{\times}16$ modes to early terminate the mode selection process. Thirdly, reference frame correlation among different Inter modes is exploited to reduce the number of reference frames. Experimental results show that the proposed algorithm can promote the encoding speed by 3.71~7.22 times with 0.08dB PSNR degradation and 2.03% bitrate increase on average compared with the joint multiview video model.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.179-188
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• 2017

The Internet Video Coding (IVC) standard is due to be published by Moving Picture Experts Group (MPEG) for various Internet applications such as internet broadcast streaming. IVC aims at three things fundamentally: 1) forming IVC patents under a free of charge license, 2) reaching comparable compression performance to AVC/H.264 constrained Baseline Profile (cBP), and 3) maintaining computational complexity for feasible implementation of real-time encoding and decoding. MPEG experts have worked diligently on the intellectual property rights issues for IVC, and they reported that IVC already achieved the second goal (compression performance) and even showed comparable performance to even AVC/H.264 High Profile (HP). For the complexity issue, however, there has not been thorough analysis on IVC decoder. In this paper, we analyze the IVC decoder in view of the time complexity by evaluating running time. Through the experimental results, IVC is 3.6 times and 3.1 times more complex than AVC/H.264 cBP under constrained set (CS) 1 and CS2, respectively. Compared to AVC/H.264 HP, IVC is 2.8 times and 2.9 times slower in decoding time under CS1 and CS2, respectively. The most critical tool to be improved for lightweight IVC decoder is motion compensation process containing a resolution-adaptive interpolation filtering process.

• Electronics and Telecommunications Trends
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• v.32 no.5
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• pp.10-19
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• 2017

A Telepresence service is an advanced video conferencing service at aimed providing remote users with the feeling of being present together at a particular location for a face-to-face group meeting. The effectiveness in this type of meeting can be further increased by automatically recognizing the audiovisual behaviors of the video conferencing users, accurately inferring their level of engagement from the recognized reactions, and providing proper feedback on their engagement state. In this paper, we review the recent developments of such engagement analysis techniques being utilized in various applications, such as human-robot interaction, content evaluation, telematics, and online collaboration services. In addition, we introduce a real-time engagement analysis framework employed in our telepresence service platform for an increased participation in online group collaboration settings.

• Journal of the Korea Computer Industry Society
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• v.2 no.11
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• pp.1465-1476
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• 2001

For the applications like video indexing, text understanding, and automatic captions localization system, real-time localization of captions is an essential task. This paper presents a algorithm for localization of captions in MPEG compression images based on I frame. In this algorithm, caption text regions are segmented from background images using their distinguishing texture characteristics and chrominance information. Unlike previously published algorithms which fully decompress the video sequence before extracting the text regions, this algorithm locates candidate caption text region directly in the DCT compressed domain.

• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.405-412
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• 2014

Due to the popularity of mobile phones, Peer-to-Peer (P2P) content sharing in mobile phones is highly desirable, especially in a mobile data connection. In addition, there has been an increasing demand for content sharing applications for P2P communication in Android-based smartphones as ubiquitous mobile devices. However, due to wireless network limits, unstable characteristic and restricted storage space, mobile users confront challenges in establishing connections and sharing video frames with other users. To solve this issue, we propose an architectural design and implementation of key frame sharing application that uses Chord, a distributed lookup protocol to establish a content sharing platform for mobile devices, combined with the Chord SDK based on Android. Our proposed system enables users easily to share key information on video data in real-time over a wireless network without manual processing of devices which join or leave the group and cost associated with the 3G/4G network.

• ETRI Journal
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• v.33 no.4
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• pp.641-644
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• 2011

In this letter, we propose a novel bitrate control, using both Kalman and FIR filters, based on a Hamiltonian analysis with respect to the amount of bits from each macroblock, in an encoding of a general video codec such as H.264/AVC. Since the proposed bitrate control is based on the simple computation of an optimal control method based on the Hamiltonian analysis, it is not necessary to use additional computation, such as a DCT or quantization, to estimate the bits for bitrate control. As a result, the proposed algorithm can be applied to single-pass encoding and can provide sufficient encoding speed with respect to various applications, even those requiring real-time control.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.68-75
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• 1999

Machine recognition of faces from still and video images is emerging as an active research area spanning several disciplines such as image processing, pattern recognition, computer vision and neural networks. In addition, human face identification has numerous applications such as human interface based systems and real-time video systems of surveillance and security. In this paper, we propose an algorithm that can identify a particular individual face. We consider human face identification system in color space, which hasn't often considered in conventional in conventional methods. In order to make the algorithm insensitive to luminance, we convert the conventional RGB coordinates into normalized CIE coordinates. The normalized-CIE-based facial images are KL-transformed. The transformed data are used as the input of multi-layered neural network and the network are trained using error-backpropagation methods. Finally, we verify the system performance of the proposed algorithm by experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.161-168
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• 2019

Recently, video equipments such as CCTV, which is spreading rapidly, is being used as a means to monitor and cope with abnormal situations in almost governments, companies, and households. However, in most cases, since recognizing the abnormal situation is carried out by the monitoring person, the immediate response is difficult and is used only for post-analysis. In this paper, we present the results of the development of video surveillance system that automatically recognizing the abnormal situations and sending such events to the smartphone immediately using the latest deep learning technology. The proposed system extracts skeletons from the human objects in real time using Openpose library and then recognizes the human behaviors automatically using deep learning technology. To this end, we reconstruct Openpose library, which developed in the Caffe framework, on Darknet framework to improve real-time processing. We also verified the performance improvement through experiments. The system to be introduced in this paper has accurate and fast behavioral recognition performance and scalability, so it is expected that it can be used for video surveillance systems for various applications.