• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.12-20
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• 2018

Efforts have been made to reconstruct low-resolution underwater images to high-resolution ones by using the image SR (Super-Resolution) method, all to improve efficiency when acquiring side-scan sonar images. As side-scan sonar images are similar with the optical images with respect to exploiting 2-dimensional signals, conventional image restoration methods for optical images can be considered as a solution. One of the most typical super-resolution methods for optical image is a sparse coding and there are studies for verifying applicability of sparse coding method for underwater images by analyzing sparsity of underwater images. Sparse coding is a method that obtains recovered signal from input signal by linear combination of dictionary and sparse coefficients. However, it requires huge computational load to accurately estimate sparse coefficients. In this study, a sparse coding based underwater image super-resolution method is applied while a selective reconstruction method for object region is suggested to reduce the processing time. For this method, this paper proposes an edge detection and object and non object region classification method for underwater images and combine it with sparse coding based image super-resolution method. Effectiveness of the proposed method is verified by reducing the processing time for image reconstruction over 32 % while preserving same level of PSNR (Peak Signal-to-Noise Ratio) compared with conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.211-220
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• 2002

In this paper, we propose an efficient algorithm to compress simple computer cell animation at very low bit rate. The structure of proposed algorithm consists of intra frame coding and inter frame coding. In inter frame coding, animation is encoded by color quantization using a palette, rearrangement of index, ADPCM used in JPEG-LS, mapping, classification, and entropy coding. In interframe coding, classifying the characteristics of motion, animation is encoded by block based motion replenishment. Experimental results show that the proposed methods turns out to outperform conventional methods including Flash, FLC, Motion-JPEG, MPEG-1, and MPEG-4.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of KIISE
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• v.42 no.4
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• pp.487-495
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• 2015

Since most of information is computerized nowadays, it is extremely important to promote the security of the computerized information. However, the software itself can threaten the safety of information through many abusive methods enabled by coding mistakes. Even though the Secure Coding Guide has been proposed to promote the safety of information by fundamentally blocking the hacking methods, it is still hard to apply the techniques on other programming languages because the proposed coding guide is mainly written for C and Java programmers. In this paper, we reclassified the coding rules of the Secure Coding Guide to extend its applicability to programming languages in general. The specific coding guide adopted in this paper is the C Secure Coding Guide, announced by the Ministry of Government Administration and Home Affairs of Korea. According to the classification, we applied the rules of programming in Sprout, which is a newly proposed Korean programming language. The number of vulnerability rules that should be checked was decreased in Sprout by 52% compared to C.

• Journal of the Korea Computer Industry Society
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• v.6 no.5
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• pp.767-774
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• 2005

In this paper, we propose an efficient coding method of digital hologram using standard compression tools for video images. At first, we convert fringe patterns into video data using a principle of CGH(Computer Generated Hologram), and then encode it. In this research, we propose a compression algorithm is made up of various method such as pre-processing for transform, local segmentation with global information of object image, frequency transform for coding, scanning to make fringe to video stream, classification of coefficients, and hybrid video coding. The proposed algorithm illustrated that it have better properties for reconstruction and compression rate than the previous methods.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.88-96
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• 2007

H.264 is a new international video coding standard that can achieve considerably higher coding efficiency than conventional standards. Its coding gain has been achieved by employing advanced video coding methods. Specially, the increased number of macroblock modes and the complex mode decision procedure using the Lagrangian optimization are the main factors for increasing coding efficiency. Although H.264 obtains improved coding efficiency, it is difficult to do an real-time encoding because it considers all coding parameters in the mode decision procedure. In this paper, we propose a fast mode decision algorithm which classifies the macroblock modes in order to determine the optimal mode having low complexity quickly. Simulation results show that the proposed algorithm can reduce the encoding time by 34.95% on average without significant PSNR degradation or bit-rate increment. In addition, in order to show the validity of simulation results, we set up a low boundary condition for coding efficiency and complexity and show that the proposed algorithm satisfies the low boundary condition.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.113-116
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• 2000

In this paper, we present new scheme for image coding which efficiently use the relationship between the properties of spatial image and its wavelet transform. Firstly an original image is decomposed into several layers by the wavelet transform, and simultaneously decomposed into 2$\^$n/ ${\times}$ 2$\^$n/ blocks. Each block is classified into 3 regions according to their property, i.e., low activity region(LAR), midrange activity region(MAR), high activity region(HAR). Secondly we are applied texture modeling technique to LAR, MAR and HAR are encoded by Stack-Run coding technique. Finally our scheme Is superior to the Zerotree method in both reconstructed image Quality and transmitted bit rates.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1851-1863
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• 2015

– In the paper, to handle the problem of traditional bag-of-features model ignoring the spatial relationship of local features in human action recognition, we proposed a Multiscale Spatial Position Coding under Locality Constraint method. Specifically, to describe this spatial relationship, we proposed a mixed feature combining motion feature and multi-spatial-scale configuration. To utilize temporal information between features, sub spatial-temporal-volumes are built. Next, the pooled features of sub-STVs are obtained via max-pooling method. In classification stage, the Locality-Constrained Group Sparse Representation is adopted to utilize the intrinsic group information of the sub-STV features. The experimental results on the KTH, Weizmann, and UCF sports datasets show that our action recognition system outperforms the classical local ST feature-based recognition systems published recently.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1349-1357
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• 2012

In recent years, security accidents which are becoming the socially hot issue not only cause financial damages but also raise outflow of private information. Most of the accidents have been immediately caused by the software weakness. Moreover, it is difficult for software today to assure reliability because they exchange data across the internet. In order to solve the software weakness, developing the secure software is the most effective way than to strengthen the security system for external environments. Therefore, suggests that the coding guide has emerged as a major security issue to eliminate vulnerabilities in the coding stage for the prevention of security accidents. Developers or administrators effectively in order to use secure coding coding secure full set of security weaknesses organized structurally and must be managed. And the constant need to update new information, but the existing Secure Coding and Security weakness is organized structurally do not. In this paper, we will define and introduce the structured weakness for mobile applications by the surveys of existing secure coding and coding rules for code analysis tools in Java.