• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.829-836
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• 2018

This study attempts to detect a shot boundary in films(or dramas) based on the length of a sequence. As films or dramas use scene change effects a lot, the issues regarding the effects are more diverse than those used in surveillance cameras, sports videos, medical care and security. Visual techniques used in films are focused on the human sense of aesthetic therefore, it is difficult to solve the errors in shot boundary detection with the method employed in surveillance cameras. In order to define the errors arisen from the scene change effects between the images and resolve those issues, the mixed algorithm based upon color histogram, edge histogram, and optical flow was implemented. The shot boundary data from this study will be used when analysing the configuration of meaningful shots in sequences in the future.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.259-262
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• 2012

In this paper, we propose a new fast algorithm for effective scene change detection. The proposed algorithm exploits Otsu threshold matching technique, which was proposed earlier. In this method, the current and the reference frames are divided into square blocks of particular size. After doing so, the pixel histogram of each block is generated. According to Otsu method, every histogram distribution is assumed to be bimodal, i.e. pixel distribution can be divided into two groups, based on within-group variance value. The pixel value that minimizes the within-group variance is said to be Otsu threshold. After Otsu threshold is found, the same procedure is performed at the reference frame. If the difference between Otsu threshold of a block in the current frame and co-located block in the reference frame is larger than predefined threshold, then a scene change between those two blocks is detected.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1044-1049
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• 2000

In detection of a scene change of the moving pictures which has massive information capacity, the temporal sampling method has faster searching speed than the sequential searching method for the whole moving pictures, yet employed searching algorithm and detection interval greatly affect searching time and searching precision. In this study, the whole moving pictures were primarily retrieved by the temporal sampling method. When there exist a scene change within the sampling interval, we suggested a fast searching algorithm using binary searching and derived an equation formula to determine optimal primary retrieval which can minimize computation, and showed the result of the experiment on MPEG moving pictures. The result of the experiment shows that the searching speed of the suggested algorithm is maximum 13 times faster than the one of he sequential searching method.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.265-268
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• 2001

In content-based video data retrieval, the representative-frame is usually used. To do that, the skill of detection for scene change is needed. Generally the color histogram comparison is used, but sensitive to light variation and tends to miss the scene change of similar color histogram. This paper shows how to use both color histogram comparison and entropy to prevent the false-positive of scene change occurred by light variation. At the experiments, il is more powerful to light variation to use both color histogram comparison entropy than to use only color histogram comparison.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.2
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• pp.25-30
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• 2003

Recently, development of digital technology is occupying a large part of multimedia information like character, voice, image, video, etc. Research about video indexing and retrieval progresses especially in research relative to video. In this paper, we propose new algorithm(Frame Feature Comparison) for MPEG video segmentation. Shot, Scene Change detection is basic and important works that segment it in MPEG video sequence. Generally, the segmentation algorithm that uses much has defect that occurs an error detection according to a flash of camera, movement of camera and fast movement of an object, because of comparing former frames with present frames. Therefore, we distinguish a scene change one more time using a scene change point detected in the conventional algorithm through comparing its mean value with abutted frames. In the result, we could detect more corrective scene change than the conventional algorithm.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.440-450
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• 2001

This paper discusses an effective algorithm which is proposed for abrupt scene change detection in MPEG bitstream. The proposed algorithm restores DC images by decoding only DC coefficients and estimates the new motion vectors between adjacent DC images and detects scene change by similarity measure between frames. The proposed algorithm calculates similarity measure between adjacent frames, i.e motion compensated inter-frame correlation, and detects scene change by comparing this similarity measure with threshold value independent of sequences. Experimental results show that the proposed algorithm has more than 90% \`recall\` and \`precision\` in almost sequences and these results can be considered better than other algorithms using threshold value dependent of sequences.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.129-132
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• 2003

Fixed GOP allocation in MPEG-2 cannot cope with scene change and amount of motion, which results in degradation picture quality. By finding suitable N and M and allocating dynamic GOP, the improvement of picture quality can be achieved. In this paper, N and M are determined by scene change detection and estimation of amount of motion using color histogram per each macroblock. The simulation results show that the average PSNR is improved, especially around the shot boundaries.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2S
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• pp.629-637
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• 2000

The content-based information retrieval for a multimedia database uses feature information extracted from the compressed videos. This paper presents an effective method to detect scene changes from compressed videos. Scene changes are detected with DC values of DCT coefficients in MPEG-1 encoded video sequences. Instead of decoding full frames. partial macroblocks of each frame, horizontal and vertical macroblocks, are decoded to detect scene changes. This method detects abrupt scene changes by decoding minimal number of blocks and saves a lot of computation time. The performance of the proposed algorithm is analyzed based on the precision and the recall. The experimental results show the effectiveness in computation time and detection rate to detect scene changes of various MPEG-1 video streams.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.951-957
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• 2002

When retrieving voluminous capacity of video image data, it is necessary to provide synopsized frame lists of video image data for indexing and replaying at the exact point where the user want to retrieve. We apply Mean Squared Error method to extract certain pixel value from diagonal direction of a frame. The RGB value of a pixel extracted from each frame is saved in a matrix form, and this frame is retrievedas a scene change point if the compared value of two points met the certain condition. Also implement the algorithm and provide a way to seize entire structure of video image and the point of scene changes. finally, we analyze and prove that our method has better performance compared with the others.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2353-2358
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• 2011

Sleep is very important physiology to our human, about one third of human life was sent over to sleep. This paper measures of sleep and proposes sleep quality and future direction in order to improve the sleep environment. Sleep measure was determined by using X2 histogram that is one of the scene change detection method. X2 histogram method is one of the statistical scene change detection and is used in many studies because of the histogram method performs better than the other. And find out their relationship by entering the degree of fatigue, alcohol, and hungry in order to develop quality of sleep and extracting to tossing and turning according to each situation.