• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.914-922
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• 1987

An image coding technique based on a segmentation, which utilizes a simplified description of regions composing an image, is investigated in this paper. The proposed coding technique consists of 3 stages: segmentation, contour coding. In this paper, emphasis was given to texture coding in order to improve a quality of an image. Split-and-merge method was employed for a segmentation. In the texture coding, a linear predictive coding(LPC), along with approximation technique based on a two-dimensional polynomial function was used to encode texture components. Depending on a size of region and a mean square error between an original and a reconstructed image, appropriate texture coding techniques were determined. A computer simulation on natural images indicates that an acceptable image quality at a compression ratio as high as 15-25 could be obtained. In comparison with a discrete cosine transform coding technique, which is the most typical coding technique in the first-generation coding, the proposed scheme leads to a better quality at compression ratio higher than 15-20.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1730-1733
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• 1997

In this paper, a realization of a subband coding with TMS320C80 is studied. TMS320C80 is a multi-media processor specially designed for an image process. A main topic of this paper, as mentioned above, is an application of TMS320C80 to subband coding. Subband coding is the coding that devides full image to several subbands and encodes each subband with different coding methods. As using that methods, good image compression can be obtained. First above all, goal of this paper deals with TMS320C80 in coding still image and useds it in expending it's application to 3-D video coding.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.232-239
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• 2023

Aiming at the poor compression quality of traditional image compression coding (ICC) algorithm, a multi-description ICC algorithm based on depth learning is put forward in this study. In this study, first an image compression algorithm was designed based on multi-description coding theory. Image compression samples were collected, and the measurement matrix was calculated. Then, it processed the multi-description ICC sample set by using the convolutional self-coding neural system in depth learning. Compressing the wavelet coefficients after coding and synthesizing the multi-description image band sparse matrix obtained the multi-description ICC sequence. Averaging the multi-description image coding data in accordance with the effective single point's position could finally realize the compression coding of multi-description images. According to experimental results, the designed algorithm consumes less time for image compression, and exhibits better image compression quality and better image reconstruction effect.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.114-127
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• 2017

This paper proposes a color image coding algorithm based on shape-adaptive all phase biorthogonal transform (SA-APBT). This algorithm is implemented through four procedures: color space conversion, image segmentation, shape coding, and texture coding. Region-of-interest (ROI) and background area are obtained by image segmentation. Shape coding uses chain code. The texture coding of the ROI is prior to the background area. SA-APBT and uniform quantization are adopted in texture coding. Compared with the color image coding algorithm based on shape-adaptive discrete cosine transform (SA-DCT) at the same bit rates, experimental results on test color images reveal that the objective quality and subjective effects of the reconstructed images using the proposed algorithm are better, especially at low bit rates. Moreover, the complexity of the proposed algorithm is reduced because of uniform quantization.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.169-172
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• 2005

Genetic algorithm based predictor for lossless image compression is propsed. We describe a genetic algorithm to learn predictive model for lossless image compression. The error image can be further compressed using entropy coding such as Huffman coding or arithmetic coding. We show that the proposed algorithm can be feasible to lossless image compression algorithm.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.9
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• pp.1410-1419
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• 1989

In this paper, we present a new synthetic high coding method which gives high image compression ratio. Given an image, only its low-pass component is transmitted by DCT coding` the high-pass component is not transmitted but synthesized using edge sharpening on the reconstructed low-pass image at the receiver. For the DCT coding which is used to encode the low-pass image, we used an improved version of Cox's variance estimator. Also, introduced are new image quality measures called GSNR and EPR which emphasize perceptual aspects of image quality. Experimental results show that the performance of the proposed synthetic high coding is better in various quality measures than that of Cox's adaptive transform coding. Also, it yields acceptable image quality with neither apparent block effect nor visible granular noise even at high compression ratio of about 30:1.

• Journal of the Korea Computer Industry Society
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• v.3 no.9
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• pp.1287-1294
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• 2002

The algorithm method for progressive image coding based on discrete wavelet transform presented in a paper. After discrete wavelet transform and extract edge information through edge detection, and then designed efficient coding method more then established embedded coding algorithm using expanded EZW algorithm. Generally, edges have a relatively higher influence on image reconstruction. Occurred DWT on image, and can classify significant coefficients and non-significant coefficients. Using property that edge part has appeared significant coefficient in the paper. Especially, we confirmed that higher frequency sub region on DWT image present homogenous direction property. And on embedded coding, which are effective and well-directed information have higher priority to image reconstruction on transmission. Therefore, our technique algorithm system perform better than that of the conventional method such as progressive image coding application.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.148-155
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• 2000

In image coding applications such as web browsing and image database searching, it is very useful to quickly view a small portion of the image with higher quality. Region of interest (ROI) coding technique provides the capability to reconstruct the ROI in advance of decompressing the rest of the image, with a smaller number of transmitted bits compared to the case where the entire image is treated with the same priority. In this paper, a progressive ROI coding method using the enbedded coder is presented, and an efficient transmission method for the ROI information. Experimental results show that the proposed progressive ROI coding technique can be effectively used for image coding applications such as web browsing and image database searching system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.2
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• pp.96-101
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• 2003

Recently, Fractal Image Coding has been studied as a way of efficient data compressing scheme. In the beginning, Fractal Image Coding has been studied for the data compressing in black ＆ white images and linear images. A. E. Jacquin suggested LIFS which expends to Fractal Image Coding for a gray scale image. Currently, YTKT's LIFS scheme which is using Gram-Schmidt is so efficient that enough to compete with the JPEG which is the national standards. This paper investigates the way of greatly reduced calculation for the orthogonalization algorithm.

• The Journal of Natural Sciences
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• v.4
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• pp.191-220
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• 1991

A new texture segmentation-based image coding technique which performs segmentation based on roughness of textural regions and properties of the human visual system (HVS) is presented. This method solves the problems of a segmentation-based image coding technique with constant segments by proposing a methodology for segmenting an image texturally homogeneous regions with respect to the degree of roughness as perceived by the HVS. The fractal dimension is used to measure the roughness of the textural regions. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into three texture classes; perceived constant intensity, smooth texture, and rough texture. An image coding system with high compression and good image quality is achieved by developing an efficient coding technique for each segment boundary and each texture class. For the boundaries, a binary image representing all the boundaries is created. For regions belonging to perceived constant intensity, only the mean intensity values need to be transmitted. The smooth and rough texture regions are modeled first using polynomial functions, so only the coefficients characterizing the polynomial functions need to be transmitted. The bounda-ries, the means and the polynomial functions are then each encoded using an errorless coding scheme. Good quality reconstructed images are obtained with about 0.08 to 0.3 bit per pixel for three different types of imagery ; a head and shoulder image with little texture variation, a complex image with many edges, and a natural outdoor image with highly textured areas.