• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.13-20
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• 2013

A combined DWT and DCT based watermarking technique of low frequency watermarking with weighted correction is proposed. The DWT has excellent spatial localization, frequency spread and multi-resolution characteristics, which are similar to the theoretical models of the human visual system (HVS). The DCT based watermarking techniques offer compression while DWT based watermarking techniques offer scalability. These desirable properties are used in this combined watermarking technique. In the proposed method watermark bits are embedded in the low frequency band of each DCT block of selected DWT sub-band. The weighted correction is also used to improve the imperceptibility. The extracting procedure reverses the embedding operations without the reference of the original image. Compared with the similar approach by DCT based approach and DWT based approach, the experimental results show that the proposed algorithm apparently preserves superiori mage quality and robustness under various attacks such as JPEG compression, cropping, sharping, contrast adjustments and so on.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2727-2729
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• 2002

In an emergency telemedicine system such as High-quality Multimedia based Real-time Emergency Telemedicine(HMRET) service, it is very important to examine the status of the patient continuously using the multimedia data including the biological signals(ECG, BP, Respiration, $SpO_2$) of the patient. In order to transmit these data real time through the communication means which have the limited transmission capacity. It is also necessary to compress the biological data besides other multimedia data. For the HMRET service, we developed the lossless biological signal compression program in MSVC++ 6.0 using DPCM method and JPEG Huffman table, and tested in an internet environment.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.2
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• pp.23-32
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• 2020

The compression sensing technology, CAFB, was developed to obtain the raw signal of the target structure by compressing it into a signal of the intended frequency range. At this point, for compression sensing, the CAFB can be optimized for various reference signals depending on the desired frequency range of the target structure. In addition, optimized CAFB should be able to efficiently compress the effective structural answers of the target structure even in sudden/dangerous conditions such as earthquakes. In this paper, the targeted frequency range for efficient structural integrity monitoring of relatively flexible structures was set below 10Hz, and the optimization method of CAFB for this purpose and the seismic response performance of CAFB in seismic conditions were evaluated experimentally. To this end, in this paper, CAFB was first optimized using Kobe seismic waveform, and embedded it in its own wireless IDAQ system. In addition, seismic response tests were conducted on two span bridges using Kobe seismic waveform. Finally, using an IDAQ system with built-in CAFB, the seismic response of the two-span bridge was wirelessly obtained, and the compression signal obtained was cross-referenced with the raw signal. From the results of the experiment, the compression signal showed excellent response performance and data compression effects in relation to the raw signal, and CAFB was able to effectively compress and sensitize the effective structural response of the structure even in seismic situations. Finally, in this paper, the optimization method of CAFB was presented to suit the intended frequency range (less than 10Hz), and CAFB proved to be an economical and efficient data compression sensing technology for instrumentation-monitoring of seismic conditions.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1007-1010
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• 2002

In this paper, we present a new blocking artifact reduction algorithm using interpolation and signal adaptive filter (SAF) based on the edge map. Generally, block-based coding, such as JPEG and MPEG, is the most popular image compression method. However, for high compression it produces noticeable blocking and ringing artifacts in the decoded image. In proposed method, all the block is classified into low and high frequency blocks in block classification procedure. And edge map is obtained by using Sobel operator on decoded image. And according to the block property we applied blocking artifacts reduction algorithm. Namely, four neighbor low frequency block is participated in interpolation based on edge map. And ringing artifacts is removed by applying a signal adaptive filter around the edge using edge map in high frequency block. The computer simulation results confirmed a better performance by the proposed method in both the subjective and objective image qualities.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.104-111
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• 2010

This paper presents an adaptive image enhancement method using the spatial frequency property in the DCT(discrete cosine transform) compressed domain. The dc coefficients, the illumination components of image, are adjusted to compress the dynamic range of image, and the ac coefficients are modified to enhance the contrast by using the human visual system(HVS) and the spatial frequency property. The ac coefficients are separated into vertical direction, horizontal direction, and mixed spatial frequency components, and adaptively modified to minimize the block artifacts that possibly occur in the image enhancement. The proposed method using dynamic range compression and adaptive contrast enhancement shows the advanced performance without the block artifact compared with existing method.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.457-458
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• 2007

This paper propose a method for representing distributed data of sensor networks. The proposed method is based on a general distributed regression framework that models sensor data by fitting a global function to each of the local measurements and explores the possible extensions of distribution regression by using complex signal representations. In order to reduce the amount of processed data and the required communication, the signal model has to be as compact as possible, with the ability to restore the arbitrary measurements. To achieve this requirement, data compression step is included, where the basis function set is changed to an overcomplete set. This have better advantages in case of nonstationary signal modeling than complete base representation.

• Journal of Biomedical Engineering Research
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• v.20 no.3
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• pp.307-314
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• 1999

This paper describes an informatlon compression of electrically evoked myoelectric signal, M-wave. This wave shows a direct response m lato-response of nerve conductlQn study and has a characteristic with finite time support. M-wave is a useful factor for investing neurodi~ease and is often desirable to have a compact description of its shape and time evolution. The aim of this paper is to show that the AR modeling IS a effective method for compressing an information of M-wave. First, AR model parameters of real M-wave are estimated. And then. they are verified by approximatmg a M-wave using estimated AR parameters and by comparing to other melhod, Hermite tlansform[4]. To concretely evaluate the proposed method, the NMSE(normalized mean square error) of approximation curves are compared. As a result, AR modeling is effective for M-wave assessment because of its capability for the information compression.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.51-56
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• 2011

This paper proposes an efficient moving picture compression for video sequences with luminance variations. In the proposed algorithm, the luminance variation parameters are estimated and local motions are compensated. To detect the frame required luminance compensation, we employ the frame classification based on the cross entropy between histograms of two successive frames, which can reduce the computational redundancy. Simulation results show that the proposed method yields a higher peak signal to noise ratio (PSNR) than that of the conventional methods, with a low computational load, when the video scene contains large luminance variations.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.3
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• pp.107-111
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• 2005

With the increasing use of multimedia technologies, image compression requires higher performance as well as new functionality in the informationized society. Specially, in the specific area of still image encoding in embedded system, a new standard, JPEG2000 that improve various problem of JPEG was developed. This paper proposed a method that reduce quantity of data delivered in EBCOT(Embedded Block Coding with Optimized Truncation) process using SPIHT(Set Partitioning in Hierarchical Trees) Algorithm to optimize selection of threshold from feature of wavelet transform coefficients and to remove sign bit in LL area for the increment of compression efficiency on JPEG2000. The experimental results showed the proposed algorithm achieves more improved bit rate in embedded system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.221-229
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• 2004

The Continuous Wavelets Transform project signal f(t) to "Time-scale"plan utilizing the time varied function which called "wavelets". This Transformation permit to analyze scale time dependence of signal f(t) thus the local or global scale properties can be extracted. Moreover, the signal f(t) can be reconstructed stably by utilizing the Inverse Continuous Wavelets Transform. In this paper, the EEG signal is analyzed by wavelets coherence method and the De-noising procedure is represented.