• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.158-165
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• 2014

High Efficiency Video Coding (HEVC), the latest video coding standard, has twice of the compression efficiency compared to AVC/H.264 under the same image quality condition. To obtain the improved efficiency, however, it was adopted for many methods which need complicated calculation, and the time complexity of HEVC was increased more than that of AVC/H.264. To solve this problem, the various fast algorithms have been researched. In this paper, we propose a fast intra prediction mode decision method which uses result of Rough Mode Decision (RMD) and Most Probable Mode (MPM). The proposed method selects a best predicted mode by comparing each predicted directions which are calculated through RMD and MPM. We applied the proposed method to HM 10.0 and conducted an comparing experiment in All-Intra environment. The experiment result showed that total encoding time is reduced by about 26% on average with about a 0.8% loss of BD-rate.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.993-994
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• 2008

There are nine modes of the intra prediction for $4{\times}4$ luma blocks in H.264/AVC, each of which is identified by the prediction direction and reference pixels. Especially, mode 8 is modified to enhance coding efficiency, considering that the mode does not use left-bottom pixels although they are available. That is, we propose a modified intra prediction method of mode 8 which uses left-bottom pixels if available.

• ETRI Journal
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• v.30 no.4
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• pp.506-515
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• 2008

The H.264/AVC standard has adopted new coding tools such as intra-prediction, variable block size, motion estimation with quarter-pixel-accuracy, loop filter, and so on. The adoption of these tools enables an H.264/AVC-coded bitstream to have more information than was possible with previous standards. In this paper, we propose an effective spatial error concealment method with low complexity in H.264/AVC intra-frame. From information included in an H.264/AVC-coded bitstream, we use prediction modes of intra-blocks to recover a damaged block. This is because the prediction direction in each prediction mode is highly correlated to the edge direction. We first estimate the edge direction of a damaged block using the prediction modes of the intra-blocks adjacent to a damaged block and classify the area inside the damaged block into edge and flat areas. Our method then recovers pixel values in the edge area using edge-directed interpolation, and recovers pixel values in the flat area using weighted interpolation. Simulation results show that the proposed method yields better video quality than conventional approaches.

• Journal of Broadcast Engineering
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• v.9 no.3
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• pp.214-224
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• 2004

In this paper, an adaptive spatio-temporal predictive coding based on the H.264 is proposed for 3D immersive media encoding, such as 3D image processing, 3DTV, and 3D videoconferencing. First, we propose a spatio-temporal predictive coding using the same view and inter-view images for the two TPPP, IBBP GOP (group of picture) structures 4hat are different from the conventional simulcast method. Second, an 2D inter-view direct mode for the efficient prediction is proposed when the proposed spatio-temporal prediction uses the IBBP structure. The 2D inter-view direct mode is applied when the temporal direct mode in B(hi-Predictive) picture of the H.264 refers to an inter-view image, since the current temporal direct mode in the H.264 standard could no: be applied to the inter-view image. The proposed method is compared to the conventional simulcast method in terms of PSNR (peak signal to noise ratio) for the various 3D test video sequences. The proposed method shows better PSNR results than the conventional simulcast mode.

• Journal of Korea Multimedia Society
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• v.12 no.7
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• pp.922-928
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• 2009

In H.264 as the newest video standard, 9 modes are used in order to predict the signal values of a block composed with several pixels by intra prediction. From these process, H.264 can bring high compression ratio in the encoded signal but the use of total 9 modes can give the inefficiency of the increase of the complexity induced by the amount of operation processing or the number of searching which is applied to compare adjacent pixels. This paper proposes a simplification method of prediction mode for the intra-picture coding by considering subjective interest region. There are certain region being interested within a picture of the video sequence. This region requires better subjective picture quality than the other regions. The proposed method increases the simplification of prediction mode by providing just essential modes of total 9 modes for less interest regions compared with the interest region. It is possible to get the additional 11%$\sim$15% simplification of the prediction mode by the proposed method, compared with the conventional method which simplifies the prediction mode for all of the picture by using the prediction characteristics only.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.44-53
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• 2010

The H.264/AVC has been designed to use 9 directional intra prediction modes for removing spatial redundancy. It also employs high correlation between neighbouring block modes in sending mode information. For indication of the mode, smaller bits are assigned for higher probable modes and are compressed by predicting the mode with minimum value between two prediction modes of neighboring two blocks. In this paper, we calculated the statistical probability of prediction modes of the current block to exploit the correlation among the modes of neighboring two blocks with several test video sequences. Then, we made the probable prediction table that lists 5 most probable candidate modes for all possible combinatorial modes of upper and left blocks. By using this probability table, one of 5 higher probable candidate modes is selected based on RD-optimization to reduce computational complexity and determines the most probable mode for each cases for improving compression performance. The compression performance of the proposed algorithm is around 1.1%~1.50%, compared with JM14.2 and we achieved 18.46%~36.03% improvement in decoding speed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.8
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• pp.1199-1203
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• 2012

H.264 adopted many compression tools to increase image data compression efficiency such as B frame bi-directional predictions, the direct mode coding and so on. Despite its high compression efficiency, H.264 can suffer from its long coding time due to the complicated tools of H.264. To realize a high performance H.264, several fast algorithms were proposed. One of them is adaptive fast direct mode decision algorithm using mode and Lagrangian cost prediction for B frame in H.264/AVC (MLP) algorithm which can determine the direct coding mode for macroblocks without a complex mode decision process. However, in this algorithm, macroblocks not satisfying the conditions of the MLP algorithm are required to process the complex mode decision calculation, yet suffering a long coding time. To overcome the problem, this paper proposes a fast direct mode prediction algorithm. Simulation results show that the proposed algorithm can determine the direct mode coding without a complex mode decision process for 42% more macroblocks and, this algorithm can reduce coding time by up to 23%, compared with Jin's algorithm. This enables to encode B frames fast with a less quality degradation.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.147-158
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• 2021

Versatile Video Coding (VVC) is the latest video coding standard developed by Joint Video Exploration Team (JVET). In VVC, the quadtree plus multi-type tree (QT+MTT) structure of coding unit (CU) partition is adopted, and its computational complexity is considerably high due to the brute-force search for recursive rate-distortion (RD) optimization. In this paper, we aim to reduce the time complexity of inter-picture prediction mode since the inter prediction accounts for a large portion of the total encoding time. The problem can be defined as classifying the split mode of each CU. To classify the split mode effectively, a novel convolutional neural network (CNN) called multi-level tree (MLT-CNN) architecture is introduced. For boosting classification performance, we utilize additional information including inter-picture information while training the CNN. The overall algorithm including the MLT-CNN inference process is implemented on VVC Test Model (VTM) 11.0. The CUs of size 128×128 can be the inputs of the CNN. The sequences are encoded at the random access (RA) configuration with five QP values {22, 27, 32, 37, 42}. The experimental results show that the proposed algorithm can reduce the computational complexity by 11.53% on average, and 26.14% for the maximum with an average 1.01% of the increase in Bjøntegaard delta bit rate (BDBR). Especially, the proposed method shows higher performance on the sequences of the A and B classes, reducing 9.81%~26.14% of encoding time with 0.95%~3.28% of the BDBR increase.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.995-1006
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• 2011

A time series can be decomposed into simple components with a multiscale method. Empirical mode decomposition(EMD) is a recently invented multiscale method in Huang et al. (1998). It is natural to apply a classical prediction method such a vector autoregressive(AR) model to the obtained simple components instead of the original time series; in addition, a prediction procedure combining a classical prediction model to EMD and Hilbert spectrum is proposed in Kim et al. (2008). In this paper, we suggest to adopt principal component analysis(PCA) to the prediction procedure that enables the efficient selection of input variables among obtained components by EMD. We discuss the utility of adopting PCA in the prediction procedure based on EMD and Hilbert spectrum and analyze the daily worm account data by the proposed PCA adopted prediction method.