• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.520-523
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• 2006

Lately, development of multimedia industry has multimedia data serviced through various wire wireless communicating environment. These multimedia data is displayed on the device of various sizes such as mobile telephone, PDA, notebook, PC. Therefore, there has needed to resize multimedia data appropriate to the screen size of user's display device. In this paper, we propose the method to resizing H.264 videos sequence fitting to the screen size of user's display device. The existing most methods reduce the size of video sequence, but this paper proposes the upsampling method. The results show the proposed method upsamples video sequences without blocking effects.

• ETRI Journal
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• v.36 no.2
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• pp.264-270
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• 2014

This paper introduces a novel repetitive delivery scheme for the left and right views in service-compatible (SC) 3D video that provides full backward compatibility to a legacy DTV system while retaining HD 3D visual quality without additional bandwidth or a codec over the legacy broadcasting channel. The proposed SC delivery scheme transmits individual view sequences of a 3D video in interlaced form, that is, a left-view sequence of a 3DTV program to be used repeatedly is transmitted first and stored locally, and the right-view sequence of the 3D program is then transmitted. This paper specifically describes the signaling, synchronization, and storage format methods used to validate the proposed SC delivery scheme. The experiment results show that the proposed SC delivery scheme can be effectively applied for an SC 3DTV service without degrading the DTV quality using only legacy DTV platforms.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.3
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• pp.273-281
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• 2004

There is the temporal correlation of the video sequence between the motion vector of current block and the motion vector of previous block. In this paper, we propose the prediction search algorithm for block matching using the temporal correlation of the video sequence and the center-biased property of motion vectors. The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(sum of absolute difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate point on each search region. Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved up to the 1.06㏈ as depend on the video sequences and improved about 0.19∼0.46㏈ on an average except the full search(FS) algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1086-1092
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• 2009

Stereoscopic conversion has been an important and challenging issue for many 3-D video applications. Usually, there are two different stereoscopic conversion approaches, i.e., image motion-based conversion that uses motion information and object-based conversion that partitions an image into moving or static foreground object(s) and background and then converts the foreground in a stereoscopic object. As well, since the input sequence is MPEG-1/2 compressed video, motion data stored in compressed bitstream are often unreliable and thus the image motion-based conversion might fail. To solve this problem, we present the utilization of key-motion that has the better accuracy of estimated or extracted motion information. To deal with diverse motion types, a transform space produced from motion vectors and color differences is introduced. A key-motion is determined from the transform space and its associated stereoscopic image is generated. Experimental results validate effectiveness and robustness of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12B
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• pp.2401-2406
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• 1999

Various techniques extracting feature vectors have been studied for the cut detection in compressed video data. In case of using the histogram of occurrence of pixel's values as a feature vector, the precise detection of cuts would not be expected because of not considering the spatial correlation of pixels. And more sophisticated algorithms such as CCV(Color Coherent Vector) and Correlrogram tend to be used. Though these methods can be able to detect cuts rather precisely, they require much more processing time because of a enormous amount of computations. In this paper we propose a method of the cut detection using spatial correlation of DC values of luminance components in MPEG video sequence. This requires less processing time and also It can increase the rates of detecting the correct cuts by using advanced comparative method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.585-588
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• 2010

Recently, there is increasing concern about smart phone due to the release of Iphone and Android phone. Like the normal desktop application, using multimedia contents will have important role for smart phone application. The multimedia contents mentioned here mean Image and Video. The famous codecs for Image and video are Jpeg Mpeg and h.264, which is popular recently as a vedeo codec because of its high compression ratio compared with the other video codecs. In this paper, we will use Jpeg and H.264 encoded raw sequences. This paper will suggest how to decode and display effectively Jpeg and H.264 raw sequences arrived from socket stream. And we will show you the simulation result.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.13-19
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• 2022

Video Object Segmentation (VOS) task requires an amount of labeled sequence data, which limits the performance of the current VOS methods trained with public datasets. In this paper, we propose two effective data augmentation schemes for VOS. The first augmentation method is to swap the background segment to the background from another image, and the other method is to play the sequence in reverse. The two augmentation schemes for VOS enable the current VOS methods to robustly predict the segmentation labels and improve the performance of VOS.

• Annual Conference of KIPS
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• 2020.11a
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• pp.967-970
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• 2020

In a previous work we have done, we presented a monitoring system to automatically detect some dogs' behaviors from videos. However, the input video data used by that system was pre-trimmed to ensure it contained a dog only. In a real-life situation, the monitoring system would continuously receive video data, including frames that are empty and ones that contain people. In this paper, we propose a YOLOv4-based system for automatic object detection and trimming of dog videos. Sequences of frames trimmed from the video data received from the camera are analyzed to detect dogs and people frame by frame using a YOLOv4 model, and then records of the occurrences of dogs and people are generated. The records of each sequence are then analyzed through a rule-based decision tree to classify the sequence, forward it if it contains a dog only or ignore it otherwise. The results of the experiments on long untrimmed videos show that our proposed method manages an excellent detection performance reaching 0.97 in average of precision, recall and f-1 score at a detection rate of approximately 30 fps, guaranteeing with that real-time processing.

• ETRI Journal
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• v.25 no.2
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• pp.140-143
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• 2003

In this paper, we present a probabilistic approach to determining whether extracted facial features from a video sequence are appropriate for creating a 3D face model. In our approach, the distance between two feature points selected from the MPEG-4 facial object is defined as a random variable for each node of a probability network. To avoid generating an unnatural or non-realistic 3D face model, automatically extracted 2D facial features from a video sequence are fed into the proposed probabilistic network before a corresponding 3D face model is built. Simulation results show that the proposed probabilistic network can be used as a quality control agent to verify the correctness of extracted facial features.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.787-788
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• 2008

In this paper, we propose a Motion Estimation (ME) based on Frame Difference (FD) for frame interpolation in video sequence with luminance variation. Proposed algorithm uses limited blocks whose motion is predicted by FD for ME. The Block average of current and previous frame for the blocks which has no motion variation is used as interpolated block. In experiments, the proposed algorithm shows better performance than conventional algorithms.