• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1997.11a
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• pp.146-151
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• 1997

Recntly many devics print electronic images in a variety of ways. But the reproduced color, gamut mappung method and expanded nonlinear quantization are proposed. The color gamut mapping uses saturation mapping of HSI color space. Dithering operation for printing uses expanded nonlinear quantization which considers overlapping phenomena of neighboring printing dots. So the printed image is similar to the image of monitor and can produce high quality image in the low bit color devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5056-5078
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• 2018

In this work we propose a compression technique that makes use of linear and perceptually uniform CIE $La^*b^*$ color space in the JPEG image compression framework to improve its performance at lower bitrates. To generate quantization matrices suitable for the linear and perceptually uniform CIE $La^*b^*$ color space, a novel linear Contrast Sensitivity Function (CSF) is used. The compression performance in terms of Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR), is further improved by utilizing image dependent, variable and non-uniform image sub-blocks generated using a proposed histogram-based merging technique. Experimental results indicate that the proposed linear CSF based quantization technique yields, on an average, 8% increase in CR for the same reconstructed image quality in terms of PSNR as compared to the conventional YCbCr color space. The proposed scheme also outperforms JPEG in terms of CR by an average of 45.01% for the same reconstructed image quality.

• Journal of Broadcast Engineering
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• v.19 no.3
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• pp.329-341
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• 2014

Kernel-based mean-shift object tracking has gained more interests nowadays, with the aid of its feasibility of reliable real-time implementation of object tracking. This algorithm calculates the best mean-shift vector based on the color histogram similarity between target model and target candidate models, where the color histograms are usually produced after uniform color-space quantization for the implementation of real-time tracker. However, when the image of target model has a reduced contrast, such uniform quantization produces the histogram model having large values only for a few histogram bins, resulting in a reduced accuracy of similarity comparison. To solve this problem, a non-uniform quantization algorithm has been proposed, but it is hard to apply to real-time tracking applications due to its high complexity. Therefore, this paper proposes a fast non-uniform color-space quantization method using the histogram equalization, providing an adjusted histogram distribution such that the bins of target model histogram have as many meaningful values as possible. Using the proposed method, the number of bins involved in similarity comparison has been increased, resulting in an enhanced accuracy of the proposed mean-shift tracker. Simulations with various test videos demonstrate the proposed algorithm provides similar or better tracking results to the previous non-uniform quantization scheme with significantly reduced computation complexity.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.848-854
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• 1996

Many image display devices allow only a limited number of colors to be simultaneously displayed. in disphaying of natural color image using color palette, it is necessary to construct an optimal color palette and the optimal mapping of each pixed of the original image to a color from the palette. In this paper, we proposed the clustering algorithm using local region block centered one color cluster in the prequantized 3-D histogram. Cluster pairs which have the least distortion error are merged by considering distortion measure. The clustering process is continued until to obtain the desired number of colors. The same as the clustering process, original color value. The proposed algorithm incroporated with a spatial activity weighting value which is reflected sensitivity of HVS quantization errors in smoothing region. This method produces high quality display images and considerably reduces computation time.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.111-120
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• 2004

This paper proposes a vector error diffusion method for smear artifact reduction in the boundary region. This artifact mainly results from a large accumulation of quantization errors. In particular, color bands with a smear artifact, the width of a few pixels appear along the edges. Accordingly, to reduce this artifact, the proposed halftoning process excludes the large accumulated Quantization error by comparing the vector norms and vector angles between the error-corrected vector and eight primary color patches. When the vector norm of the error corrected vector is larger than those of eight primary color patches, the quantization error vector is excluded from the quantization error distribution process. In addition, the quantization error is also excluded when the angle between eight primary color patches and error corrected vector is large. As a result, the proposed method enables a visually pleasing halftone pattern to be generated by all three color separations into account in a device- independent color space and reduces smear artifact in the boundary regions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.3
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• pp.59-65
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• 2004

This paper explores an image segmentation and representation method using Vector Quantization(VQ) on color and texture for content-based image retrieval system. The basic idea is a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. These schemes are used for object-based image retrieval. Features for image retrieval are three color features from HSV color model and five texture features from Gray-level co-occurrence matrices. Once the feature extraction scheme is performed in the image, 8-dimensional feature vectors represent each pixel in the image. VQ algorithm is used to cluster each pixel data into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to object within the image. The proposed method can retrieve similar images even in the case that the objects are translated, scaled, and rotated.

• Journal of IKEEE
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• v.14 no.2
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• pp.76-81
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• 2010

The need for dedicated hardware continue to decrease as the mobile CPU's performance increases. But, there is a limit to a mobile CPU's performance. GP-GPU(General-Purpose computing on Graphics Processing Units) can improve performance without adding other dedicated hardware. This paper presents the implementation of Inverse Quantization, Inverse DCT and Color Space Conversion module in H.264/AVC decoder using GP-GPU for a mobile environments. The proposed architecture improves approximately 40% of performance when it use all the features.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.473-481
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• 2005

In this paper, we propose an image segmentation method preserving object's boundaries by using the number of quantized colors and merging regions using adaptive threshold values. First of all, the proposed method quantizes an original image by a vector quantization and the number of quantized colors is determined differently using PSNR each image. We obtain initial regions from the quantized image, merge initial regions in CIE Lab color space and RGB color space step by step and segment the image into semantic regions. In each merging step, we use color distance between adjacent regions as similarity-measure. Threshold values for region-merging are determined adaptively according to the global mean of the color difference between the original image and its split-regions and the mean of those variations. Also, if the segmented image of RGB color space doesn't split into semantic objects, we merge the image again in the CIE Lab color space as post-processing. Whether the post-processing is done is determined by using the color distance between initial regions of the image and the segmented image of RGB color space. Experiment results show that the proposed method splits an original image into main objects and boundaries of the segmented image are preserved. Also, the proposed method provides better results for objective measure than the conventional method.

• Journal of Korea Multimedia Society
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• v.7 no.3
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• pp.319-326
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• 2004

In this paper, we propose color image segmentation by region merging method preserving the boundary of an object. The proposed method selects initial region by using quantized image's index map after vector quantizing an original image. After then, we merge regions by applying boundary restricted factor in order to consider the boundary of an object in HSI color space. Also we merge the regions in RGB color space for non-processed regions in HSI color space. And we reduce processing time by decreasing iterative process in region merging algorithm. Experimental results have demonstrated the superiority in region's segmentation results and processing time for various images.