• Journal of Broadcast Engineering
• /
• v.22 no.3
• /
• pp.366-374
• /
• 2017

Since a display device such as TV or digital signage is getting larger, the types of media is getting changed into wider view one such as UHD, panoramic and jigsaw-like media. Especially, panoramic and jigsaw-like media is realized by stitching video clips, which are captured by different camera or devices. However, a stitching process takes long time, and has difficulties in applying for a real-time process. Thus, this paper suggests to find out 2D Adjacency Matrix, which tells spatial relationships among those video clips in order to decrease a stitching processing time. Using the Discrete Cosine Transform (DCT), we convert the each frame of video source from the spatial domain (2D) into frequency domain. Based on the aforementioned features, 2D Adjacency Matrix of images could be found that we can efficiently make the spatial map of the images by using DCT. This paper proposes a new method of generating 2D adjacency matrix by using DCT for producing a panoramic and jigsaw-like media through various individual video clips.

• Journal of Korea Multimedia Society
• /
• v.8 no.8
• /
• pp.1051-1056
• /
• 2005

Least mean square(LMS) algorithm is one of the most popular algorithm in adaptive signal processing because of the simplicity and the small computation. But the convergence speed of time domain adaptive algorithm is slow when the spread width of eigen values is wide. Moreover we have to choose the step size well for convergency in this paper, we use adaptive algorithm of wavelet transform. And we propose a new wavelet based adaptive algorithm of wavelet transform. And we propose a new wavelet based adaptive algorithm with variable step size, which Is linear to absolute value of error signal. We applied this algorithm to adaptive noise canceler. Simulation results are presented to compare the performance of the proposed algorithm with the usual algorithms.

• Journal of the Optical Society of Korea
• /
• v.20 no.3
• /
• pp.341-357
• /
• 2016

A novel asymmetric multiple-image encryption method using an octonion Fresnel transform (OFST) and a two-dimensional Sine Logistic modulation map (2D-SLMM) is presented. First, a new multiple-image information processing tool termed the octonion Fresneltransform is proposed, and then an efficient method to calculate the OFST of an octonion matrix is developed. Subsequently this tool is applied to process multiple plaintext images, which are represented by octonion algebra, holistically in a vector manner. The complex amplitude, formed from the components of the OFST-transformed original images and modulated by a random phase mask (RPM), is used to derive the ciphertext image by employing an amplitude- and phase-truncation approach in the Fresnel domain. To avoid sending whole RPMs to the receiver side for decryption, a random phase mask generation method based on SLMM, in which only the initial parameters of the chaotic function are needed to generate the RPMs, is designed. To enhance security, the ciphertext and two decryption keys produced in the encryption procedure are permuted by the proposed SLMM-based scrambling method. Numerical simulations have been carried out to demonstrate the proposed scheme's validity, high security, and high resistance to various attacks.

• International Journal of Computer Science & Network Security
• /
• v.21 no.12spc
• /
• pp.556-564
• /
• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• Journal of Broadcast Engineering
• /
• v.20 no.5
• /
• pp.718-727
• /
• 2015

In this paper, we present a novel segmentation method that can extract new foreground objects from a current frame in real-time. It is performed by detecting differences between the current frame and reference frame taken from a fixed camera. We minimize computing complexity for real-time video processing. First DCT (Discrete Cosine Transform) is utilized to generate rough binary segmentation maps where foreground and background regions are separated. DCT shows better result of texture analysis than previous methods where texture analysis is performed in spatial domain. It is because texture analysis in frequency domain is easier than that in special domain and intensity and texture in DCT are taken into account at the same time. We maximize run-time efficiency of DCT by considering color information to analyze object region prior to DCT process. Last we use Guided filter for natural matting of the generated binary segmentation map. In general, Guided filter can enhance quality of intermediate result by incorporating guidance information. However, it shows some limitations in homogeneous area. Therefore, we present an additional method which can overcome them.

• Journal of Broadcast Engineering
• /
• v.2 no.1
• /
• pp.45-55
• /
• 1997

In this paper, we propose a fast algorithm with adaptive thresholding for the wavelet transform (WT) based blocking artifact reduction. In the fast algorithm, all processings that are equivalent to the processing in WT domain of the first and second scale are performed in spatial domain. In the adaptive thresholding, the threshold values used to classify the block boundary are selected adaptively according to each input image by using the statistical properties of the WT of the coded signal at block boundary and at block center, which can be obtained in spatial domain. Experimental results showed that the proposed fast algorithm is about 10 times faster than the WT-based algorithm. It also was found that the postprocessing with proposed adaptive thresholding yields some PSNR improvement and better subjective quality over that with nonadaptive thresholding which has best performance at high compression ratios of a certain .image, even at low compression ratios.

• The KIPS Transactions:PartA
• /
• v.14A no.1 s.105
• /
• pp.73-76
• /
• 2007

The determination of the conformal maps from the unit disk onto a Jordan region has been completed by solving the Theodorsen equation which is an nonlinear equation for the boundary correspondence function. Wegmann's method has been well known for the efficient mothed among the many suggestions for the Theodorsen equation. We proposed an improved method for convergence by applying a low-frequency pass filter to the Wegmann's method and theoretically proved convergence of improved iteration[1, 2]. And we proposed an effective method which makes it possible to estimate an error even if the real value is nut acquired[3]. In this paper, we propose an automatic algorithm for numerical conformal mapping bared on this error analysis in our early study. By this algorithm numerical conformal mapping is determined automatically according to the given domain of problem and the required accuracy. The discrete numbers and parameters of the low-frequency filter were acquired only by experience. This algorithm, however, is able to determine the discrete numbers and parameters of the low-frequency filter automatically in accordance with the given region This results from analyzing the function, which may decide the shape of the given domain under the assumption that the degree of the problem depends of the transformation of a given domain, as seen in the Fourier Transform. This proposed algorithm is also ploved by numerical experience.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.2
• /
• pp.1019-1022
• /
• 2005

As recent information communication technology is rapidly developed, a lot of researches related to speech signal processing have been processed. So pitch period is applied as important factor to many application fields such as speech recognition, speaker identification, speech analysis and synthesis. Therefore, many algorithms related to pitch detection have been proposed in time domain and frequency domain and AMDF(average magnitude difference function) which is one of pitch detection algorithms in time domain chooses time interval from valley to valley as pitch period. But, in selection of valley point to detect pitch period, complexity of the algorithm is increased. So in this paper we proposed pitch detection algorithm using rotation transform of AMDF, that taking the global minimum valley point as pitch period and established a threshold about the phoneme in beginning portion, to exclude pitch period selection. and compared existing methods with proposed method through simulation.

• The Journal of the Acoustical Society of Korea
• /
• v.27 no.7
• /
• pp.372-378
• /
• 2008

Voice activity detectors (VAD) are important in wireless communication and speech signal processing, In the conventional VAD methods, an expression for the likelihood ratio test (LRT) based on statistical models is derived in discrete Fourier transform (DFT) domain, Then, speech or noise is decided by comparing the value of the expression with a threshold, This paper presents a new statistical VAD method based on a signal subspace approach, The probabilistic principal component analysis (PPCA) is employed to obtain a signal subspace model that incorporates probabilistic model of noisy signal to the signal subspace method, The proposed approach provides a novel decision rule based on LRT in the signal subspace domain, Experimental results show that the proposed signal subspace model based VAD method outperforms those based on the widely used Gaussian distribution in DFT domain.

• Journal of Advanced Navigation Technology
• /
• v.20 no.4
• /
• pp.298-304
• /
• 2016

In this paper, a wavelet packet transform method is proposed for improving the altitude control performance of quadrotor UAV using an ultrasonic rangefinder. A ground tests are conducted using an ultrasonic rangefinder that is much used for vertical takeoff and landing. An ultrasonic rangefinder suffers from signal's spike due to specular reflectance and acoustic noise. The occurred spikes in short time span need to be analyzed at both sides time and frequency domain. The analyzed spikes of the ultrasonic rangefinder using a wavelet packet transform. Compared with the discrete wavelet transform, the wavelet packet decomposition can obtain more abundant time-frequency localization information, so it is more suitable for analyzing and processing ultrasonic signals spike. Experimental results show that it can effectively remove the spikes of the ultrasonic rangefinder.