• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1028-1040
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• 2000

Since image data has large data amount, proper image compression is necessary to transmit and store the data efficiently. Image compression brings about bit rate reduction but results in some artifacts. This artifacts are blocking artifacts, mosquito noise, which are observed in DCT based compression image, and ringing artifacts, which is perceived around the edges in wavelet based compression image. In this paper, we propose directional postprocessing technique which improved the decoded image quality using the fact that human vision is sensible to ringing artifacts around the edges of image. First we detect the edge direction in each block. Next we perform directional postprocessing according to detected edge direction. Proposed method is that the edge direction is block. Next performed directional postprocessing according to detected edge direction. If the correlation coefficients are equivalent to each directions, postprocessing is not performed. So, time of the postproces ing brings about shorten.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2205-2210
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• 2009

In this paper, the proposed method is a denoising technology by tangent curve interpolation of zero points. The problem of the hard threshold method is improved by the proposed method. The quantity of time fluctuation of the electromagnetic signal as the quantity of electric fluctuation of the natural world or the curve of motion waveform of the fast movement of human extracted using virtual reality is, in fact, complex. Therefore it is important to decide exactly the signal properties as the inflection point for observation signal. In particular, it is necessary to extract the properties after denoising, since the measurement signal of the natural world include some noises. It shows that the noise of the inflection point signal with noise II, noise factor 5, is eliminated by the proposed method, and the result of SNR for the signal is improved 3.4dB than that by the conventional hard threshold.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2118-2125
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• 2014

Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1008-1014
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• 2003

In this paper, we propose the image retrieval method based on object regions using bidirectional round filter in the wavelet transform domain. A conventional method that includes unnecessary background information reduce retrieval efficiency, because of the extraction of feature vectors from the whole region of subband. On proposed method, it extracts accurate feature vectors and keep certainly retrieval efficiency in case of reduced feature vectors, because of the extraction of feature vectors from the only extracted object region. Furthermore, it improve retrieval efficiency by removing unnecessary background information. Consequently, the retrieval efficiency is improved with 2.5%∼5.5% values, which have a little chances to vary according to characteristics of image.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.240-246
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• 2003

In this paper, we propose the fast image retrieval method based on object regions using bidirectional round filter in the wavelet transform region. A conventional method that extracts feature vectors on the whole of subband is reduced retrieval efficiency, because of unnecessary background information. The proposed method that extracts feature vectors on the only object region of subband by using bidirectional round filter improve retrieval efficiency, because of removing of background information. And it certainly maintains retrieval efficiency in case of reduction of feature vectors according to color information. Consequently, the retrieval efficiency is improved with 2.5%∼5.3% values, which have a little changes according to characteristics of image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5C
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• pp.520-529
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• 2007

In this paper, we study the characteristics of parameters which are related to performance of MCTF which is a key technique for wavelet-based scalable video coding, and propose an improved MCTF method. The proposed MCTF method adopts the motion estimation of which motion vector field is distributed more uniformly using variable block sizes. By using the proposed method, the decomposition performance of temporal filter is improved, and the energy in high-frequency frames is reduced. It can help the entropy coder to generate lower bitrate. From simulation results, we verify the decomposed energy on high-frequency frame using the proposed method is reduced by 25.86% at the most in terms of variance of the high-frequency frame.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.431-438
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• 2004

Medical ultrasonic imaging is a useful imaging facility known to be most safe and easy. It enables physicians to observe the inside structures of the bodies, blood flow, and motions of internal organs. Some physical properties of biologic tissues can also be estimated from backscattered sounds. However, the ultrasonic pulses interrogating the living organisms leave their footprints in the returning signals during imaging. Some significant details are buried in the footprints and their overlaps from adjacent particles. These distortions also decrease the quality of the images. Many research efforts have been made to enhance the image quality and to recover the acoustic information in various ways. In this study, a new interrogation method based on the wavelet and subband filter bank is proposed. It adopts the subband wavelet filters satisfying the perfect-reconstruction (PR) conditions as the interrogating pulses to restore the details useful in tissue characterization and to enhance the image quality. The proposed method was applied to two types of simulations of ultrasonic imaging. The results showed its ability to restore the detailsin the simulated interrogation of biologic tissues, and verified the improved image quality in the simulated imaging of general ultrasonic phantom compared with the conventional method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.185-188
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• 2002

With improved network system and development of computer technology, a lot of hospitals are equipping PACS that deals with process and transmission of the medical images. Owing to equipment of PACS the problems on transmission and storage of the medical images were treated. The way to solve the problems is to use various image processing techniques and compression methods This paper describes RLC in lossless image compression method, JPEG using DCT in loss image compression applied to medical images as way implementing DICOM standard. Now the medical images were compressed with Wavelet transform method have been taken advantage of image process. And compression rate of each compression methods was analyzed.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.270-277
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• 2010

The BAMS filter is a kind of wavelet shrinkage filter based on the Bayes estimators with no simulation, therefore it can be used for a real time filter. The denoising efficiency of BAMS filter is seriously affected by the estimated noise variance in each wavelet band. To remove noise in signals in existing BAMS filter, the noise variance is estimated by using the quartile of the finest level of details in the wavelet decomposition, and with this variance, the noise of the level is removed. In this paper, to remove the image noise includingodified quartile of the level of detail is proposed. And by these techniques, the image noises of mid and high frequency bands are removed, and the results showed that the increased PSNR of ab the midband noise, the noise variance estimation method using the monotonic transform and the mout 2[dB] and the effectiveness in denosing of low noise deviation images.