• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.286-296
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• 2005

This paper presents a stereo-based image compression and transmission system for 3D realistic television. In the proposed system, a disparity map is extracted from an input stereo image pair and the extracted disparity map and one of two input images are transmitted or stored at a local or remote site. However, correspondences can not be determined in occlusion areas. Thus, it is not easy to recover 3D information in such regions. In this paper, a reconstruction image compensation algorithm based on error block concealment and in-loop filtering is proposed to minimize the reconstruction error in generating stereo image pair. The effectiveness of the proposed algorithm is shown in term of objective accuracy of reconstruction image with several real stereo image pairs.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.982-987
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• 2007

The necessity of data compression scheme for volume data has been increased because of the increase of data capacity and the amount of network uses. Now we have various kinds of compression schemes, and we can choose one of them depending on the data types, application fields, the preferences, etc. However, the capacity of data which is produced by application scientists has been excessively increased, and the format of most scientific data is 3D volume. For 2D image or 3D moving pictures, many kinds of standards are established and widely used, but for 3D volume data, specially time-varying volume data, it is very difficult to find any applicable compression schemes. In this paper, we present a compression scheme for encoding time-varying volume data. This scheme is aimed to encoding time-varying volume data for visualization. This scheme uses MPEG's I- and P-frame concept for raising compression ratio. Also, it transforms volume data using Daubechies D4 filter before encoding, so that the image quality is better than other wavelet-based compression schemes. This encoding scheme encodes time-varying volume data composed of single precision floating-point data. In addition, this scheme provides the random reconstruction accessibility for an unit, and can be used for compressing large time-varying volume data using correlation between frames while preserving image qualities.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.847-850
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• 2000

This paper proposes an image compressing and template matching algorithm for face image using GHA (Generalized Hebbian Algorithm). GHA is a part of PCA (Principal Component Analysis), that has single-layer perceptrons and operates and self-organizing performance. We used this algorithm for feature extraction of face shape, and our simulations verify the high performance for the proposed method. The shape for face in the fact that the eigenvector of face image can be efficiently represented as a coefficient that can be acquired by a set of basis is to compress data of image. From the simulation results, the mean PSNR performance is 24.08[dB] at 0.047bpp, and reconstruction experiment shows that good reconstruction capacity for an image that not joins at leaning.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.568-573
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• 2000

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.57-61
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• 2013

A hybrid genetic algorithm based learning method for the morphological neural networks (MNN) is proposed. The morphological neural networks are based on max-plus algebra, therefore, it is difficult to optimize the coefficients of MNN by the learning method with derivative operations. In order to solve the difficulty, a hybrid genetic algorithm based learning method to optimize the coefficients of MNN is used. Through the image compression/reconstruction experiment using test images extracted from standard image database(SIDBA), it is confirmed that the quality of the reconstructed images obtained by the proposed method is better than that obtained by the conventional neural networks.

• Imaging Science in Dentistry
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• v.50 no.4
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• pp.331-337
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• 2020

Purpose: As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images. Materials and Methods: Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep super-resolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images. Results: The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1. Conclusion: VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.73-76
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• 2001

We propose a new method for image coding that it is based on highly related property between a spatial image and wavelet transform image. The characteristics have an important role in the design of proposed algorithm. This algorithm for image coding is to obtain high compression rate at low bit rate. The other side, the high activity regions are related to significant coefficients which give much influence to image reconstruction, because they mean the important factor to represent the appearance of images such as edge or boundary. For some images with low activity, we can obtain the reconstructed image with near to 30 dB at 150: 1 compression ratio.

• ETRI Journal
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• v.41 no.4
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• pp.415-425
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• 2019

Generative adversarial networks (GAN) have been successfully used in many image restoration tasks, including image denoising, super-resolution, and compression artifact reduction. By fully exploiting its characteristics, state-of-the-art image restoration techniques can be used to generate images with photorealistic details. However, there are many applications that require faithful rather than visually appealing image reconstruction, such as medical imaging, surveillance, and video coding. We found that previous GAN-training methods that used a loss function in the form of a weighted sum of fidelity and adversarial loss fails to reduce fidelity loss. This results in non-negligible degradation of the objective image quality, including peak signal-to-noise ratio. Our approach is to alternate between fidelity and adversarial loss in a way that the minimization of adversarial loss does not deteriorate the fidelity. Experimental results on compression-artifact reduction and super-resolution tasks show that the proposed method can perform faithful and photorealistic image restoration.

• ETRI Journal
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• v.44 no.1
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• pp.94-104
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• 2022

Because of its convenience and compatibility with various image processing techniques, digital image representation of holograms is generally used in digital holography, and thus, quality assessment of digital holograms is an essential issue. This study proposes a new objective quality metric for digital phase hologram image processing. The proposed metric is based on a newly defined phase distortion created by taking the 2π periodicity of phase information into account. The experimental results show that the proposed metric correlates with reconstruction image quality better than the existing metric under random distortions and also works well with JPEG 2000 compression. It is expected to be broadly used in phase image processing and compression applications including phase holograms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.1
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• pp.11-18
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• 2001

In this paper, we proposed the image compression algorithm using triangular feature based GHA. In feature extraction, the input images are divided into eight areas of triangular shape, that has positional information for face image compression. The proposed algorithm reduces blocking effects in image reconstruction and contains informations of face feature and shapes of face as input images are divided into eight. We used triangular feature extraction for positional information and GHA for shape information of face images. Simulation results show that the proposed algorithm has a better performance than the block based K-means and non-parsed image based GHA in PSNR at the same bpp.