• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2003.11a
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• pp.7-10
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• 2003

인터프레임 웨이블렛 부호화(Interframe Wavelet Coding)는 3D 서브밴드(Subband) 부호화라고도 하며, 기존의 DCT기반 Hybrid 동영상 부호화 방식에 비해 압축 효율이 우수하고. 특히 스케일러빌리티 기능이 뛰어난 부호화 방법이다. 인터프레임 웨이블렛 부호화 방법에서 복호화 과정 중 가장 연산 량이 많이 요구되는 역(inverse) 웨이블렛 변환이다 역 웨이블렛 변환의 연산 량은 복호화 과정에서 적용된 웨이블렛 변환과 동일한 연산량을 요구한다. 이는 순방향과 역방향에서 동일 길이의 필터와 분해 레벨을 사용해야 하기 때문이다. 이 웨이블렛 변환의 연산 량을 줄이기 위해 본 논문에서는 기존의 시간 밴드 영상에 대해 동일 한 웨이블렛 필터를 사용하여 공간 웨이블렛 필터를 적용하던 것을. 로우밴드에는 9/7 필터를 적용하고 하이 밴드에는 Haar필터를 사용하는 방법을 제안한다. PSNR 실험에서 기존의 9/7 필터만을 사용하는 경우와 비교한 결과 거의 차이가 없었다.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.183-186
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• 2003

인터프레임 웨이블렛 부호화(Interframe Wavelet Coding)는 3D 서브밴드 부호화라고도 하며, 기존의 DCT 기반 동영상 부호화 방식에 비해 압축 효율이 우수하고, 특히 스케일러빌리티 기능이 뛰어난 부호화 방법이다. 본 논문에서는 기존의 인터프레임 웨이블렛 부호화 방법에서 시간 밴드 영상에 대해 동일한 웨이블렛 필터를 사용하여 공간 웨이블렛 필터를 적용하던 것을, 시간 밴드 영상의 특성을 고려하여 로우 밴드와 하이 밴드에 서로 다른 웨이블렛 필터를 적용하는 방법을 제안하였다. 본 논문에서는 로우밴드에는 9/7 필터를 적용하고 하이 밴드에는 Haar필터를 적용하여 보았다. 이렇게 적용함으로서 부호과정에서 가장 많은 연산량을 필요로하는 역 웨이블렛 변환이 간단하게 되어 복호기의 복잡도가 감소하는 효과가 있다. PSNR 실험에서 기존의 9/7 필터만을 사용하는 경우와 비교한 결과 거의 차이가 없었다.

• The Journal of the Acoustical Society of Korea
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• v.19 no.4
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• pp.3-15
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• 2000

In this paper we present a new image denoising filter that can suppress additive noise components while preserving signal components in the wavelet domain. The proposed filter, which we call an adaptive wavelet shrinkage(AWS) filter, is composed of two operators: the wavelet killing operator and the adaptive shrinkage operator. Each operator is selected based on the threshold value which is estimated adaptively by using the local statistics of the wavelet coefficients. In the wavelet killing operation, the small wavelet coefficients below the threshold value are replaced by zero to suppress noise components in the wavelet domain. The adaptive shrinkage operator attenuates noise components from the wavelet components above the threshold value adaptively. The experimental results show that the proposed filter is more effective than the other methods in preserving signal components while suppressing noise.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2777-2779
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• 2002

본 논문에서는 영상안에서의 노이즈를 제거하기 위한 방법과 영상을 압축하기 위한 방법을 제안하였다. 영상을 필터링하기 위한 방법으로 해상도의 손상 없이 영상의 신호대잡음비(SNR)를 개선시킬 수 있는 국부 형태 적응 필터링을 제안하였다. 제안한 알고리즘에서는 템플릿 형태가 고정되어 있는 기존의 필터링 방법 대신에 다중 템플릿들을 정의하였다. 적응 템플릿 필터링을 자기공명영상에 적용할 때 기존의 필터링 방법들에 비하여 향상된 결과를 얻을 수 있으나. $T_1$ 영상과 같이 비교적 작은 동적 범위를 가진 영상에서는 에지에서 계단모양의 artifact가 발견되곤 한다. 본 논문에서는 다중 성분을 갖는 복셀들을 선별하여 이들에 대해서는 가장 큰 크기의 템플릿을 할당함으로써 artifact를 제거하는 방법을 제안하였다. 영상 압축에 있어서는 두 가지 모델이 제안되었다. 첫 번째로, 향상된 정수 기반 웨이블렛 변환을 사용한 무손실에 가까운 압축을 제안하였으며, 두 번째로, 완전 복원이 가능한 정수 기반 웨이블렛 변환을 사용한 통합된 유/무손실 압축을 제안하였다. 모의 실험에서, 제안된 알고리즘에 의해 재구성된 영상들은 부동 소수점 기반 웨이블렛 변환과 JPEG에 의해 재구성된 영상들에 비해 높은 신호대잡음비를 보였다.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.595-607
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• 2010

This paper addresses the problem of reducing the speckle noise in SAR images by wavelet transformation, using a non-local means(NLM) filter originated for Gaussian noise removal. Log-transformed SAR image makes multiplicative speckle noise additive. Thus, non-local means filtering and wavelet thresholding are used to reduce the additive noise, followed by an exponential transformation. NLM filter is an image denoising method that replaces each pixel by a weighted average of all the similarly pixels in the image. But the NLM filter takes an acceptable amount of time to perform the process for all possible pairs of pixels. This paper, also proposes an alternative strategy that uses the t-test more efficiently to eliminate pixel pairs that are dissimilar. Extensive simulations showed that the proposed filter outperforms many existing filters terms of quantitative measures such as PSNR and DSSIM as well as qualitative judgments of image quality and the computational time required to restore images.

• Journal of Broadcast Engineering
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• v.1 no.2
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• pp.118-132
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• 1996

This paper proposes a wavelet transform- based image compression method using the energy distribution. The proposed method Involves two steps. First, we use a wavelet transform for the subband decomposition. The original image Is decomposed into one low resolution subimage and three high frequency subimages. Each high frequency subimages have horizontal, vertical, and diagonal directional edges. The wavelet transform is luther applied to these high frequency subimages. Resultant transformed subimages have different energy distributions corresponding to different orientation of the high pass filter. Second, for higer compression ratio and computational effciency, we discard some subimages with small energy. The remaining subimages are encoded using either DPCM or quantization followed by entropy coding. Experimental results show that the proposed coding scheme has better performance in the peak signal to noise ratio(PSNR) and higher compression ratio than conventional image coding method using the wavelet transform followed by the straightforward vector quantization.

• The Journal of the Acoustical Society of Korea
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• v.16 no.4
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• pp.64-70
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• 1997

This paper is aimed to propose a new wavelet audio signal coding scheme which reduces the complexity of well-known MPEG(Moving Picture Expert Group)-Audio. The filters of MPEG0audio apply subband technique on the 16-bits PCM audio to aquire bitstream of subband sample using dynamic bit allocation. If we use the wavelet coefficients instead of subband samples and 6 bands which is less than 32 bands of MPEG-audio, the complexity can be reduced. A new audio signal compression algorithm in this paper is based on wavelet transform and the proposed algorithm is compared with MPEG-audio. At the bitrate of 256kbps, the proposed algorithm maintains the CD(Compact-disc) quality. We were able to reduce the about 40% of complexity at encoder and about 70% at decoder.

• The Journal of the Acoustical Society of Korea
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• v.20 no.5
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• pp.5-13
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• 2001

It is well known that there are two experimental errors on acoustic decay measurements. ,One is due to the influence of the band pass filter the other one is that of an averaging device. In this paper the influence of the filter is investigated in detail. To minimize the influence of the filter, the product of the filter bandwidth B (3dB bandwidth) and the reverberation time T/sub 60/ of the room under test should be at least 16. Moreover, if the initial part of an acoustic decay curve is important, the strong requirement, i. e. BT/sub 60/〉64, must be satisfied. In this paper, the wavelet filter bank instead of the band pass filter bank is applied to obtain an acoustic decay curve. As a result, the influence of filter is reduced and then the value of BT/sub 60/ required for obtaining an acceptable decay curve becomes at least 4. The strong requirement for the initial part of a decay curve is also replaced by the BT/sub 60/〉16 instead of BT/sub 60/〉64.

• The KIPS Transactions:PartC
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• v.9C no.4
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• pp.459-466
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• 2002

This paper proposes a robust speaker identification algorithm based on the wavelet filter banks and multiple decision-making scheme. Since the proposed speaker identification algorithm has a structure performing the identification algorithm independently for each subband, the noise effect of an subband can be localized. Through this process, we can obtain more robust results for the environmental noises which generally have band limited frequency. In the experiments, the proposed method showed more 15∼60% improvement than the vector quantization method for the various noisy environments.

• The Journal of Korean Association of Computer Education
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• v.5 no.4
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• pp.43-52
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• 2002

A theory of binary wavelets which are performed over binary field has been recently proposed. Binary wavelet transform (BWT) of binary images can be used as an alternative to the real-valued wavelet transform of binary images in image processing applications such as compression, edge detection, and recognition. The BWT, however, requires large amount of computations for binary wavelet reconstruction since its operation is accomplished by matrix multiplication. In this paper, an efficient binary wavelet reconstruction method which utilizes filtering operation instead of matrix multiplication is presented. Experimental results show that the proposed algorithm can significantly reduce the computational complexity of the BWT. For the reconstruction of an $N{\times}N$ image, the proposed technique requires only $2MN^2$ multiplications and $2N(M-1)^2$ additions when the filter length M, while the BWT needs $2N^3$ multiplications and $2N(N-1)^2$ additions.