• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.2
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• pp.45-55
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• 1994

In this paper, a block based image approximation technique using the Self Affine System(SAS) from the fractal theory is suggested. Each block of an image is divided into 4 tiles and 4 affine mapping coefficients are found for each tile. To find the affine mapping cefficients that minimize the error between the affine transformed image block and the reconstructed image block, the matrix euation is solved by setting each partial differential coefficients to aero. And to ensure the convergence of coding block. 4 uniformly partitioned affine transformation is applied. Variable block size technique is employed in order to applynatural image reconstruction property of fractal image coding. Large blocks are used for encoding smooth backgrounds to yield high compression efficiency and texture and edge blocks are divided into smaller blocks to preserve the block detail. Affine mapping coefficinets are found for each block having 16$\times$16, 8$\times$8 or 4$\times$4 size. Each block is classified as shade, texture or edge. Average gray level is transmitted for shade bolcks, and coefficients are found for texture and edge blocks. Coefficients are quantized and only 16 bytes per block are transmitted. Using the proposed algorithm, the computational load increases linearly in proportion to image size. PSNR of 31.58dB is obtained as the result using 512$\times$512, 8 bits per pixel Lena image.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.4
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• pp.108-114
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• 2008

In this paper, we proposed robust blind watermarking algorithm using texture block coefficient based on discrete cosine transform(DCT). The proposed method embedded all of watermark signals into DC component of $8\times8$ block DCT in order to robust various external attack The texture block coefficient was composed absolute value of DCT coefficients. Experiment showed that the proposed method better than conventional methods in the invisibility and various attack such as dithering, cropping, and scaling.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.102-112
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• 2012

In this paper, we propose a texture classification using wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features with WPCA (whitened principal component analysis) classifier. In the proposed method, the wavelet transform is first applied to a query image. The BDIP and BVLC operators are next applied to the wavelet subbands. Global moments for each subband of BDIP and BVLC are then computed and fused into a feature vector. In classification, the WPCA classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the query feature vector. Experimental results show that the proposed method yields excellent texture classification with low feature dimension for test texture image DBs.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.183-186
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• 2001

In this paper, we first propose new texture features, BVLC (block variation of local correlation coefficients) moments, for content-based image retrieval (CBIR) and then present an image retrieval method based on the fusion of BDIP and BVLC moments. BDIP uses the local probabilities in image blocks to extract valley and edges well. BVLC uses the variations of local correlation coefficients in images blocks to measure texture smoothness well. In order not to be affected with the movement, rotation, and size of an object, the first and second moments of BDIP and BVLC are used for CBIR. Corel DB and Vistex DB are used to evaluate the performance of the proposed retrieval method. Experimental results show that the presented retrieval method yields average 12% better performance than the method using only BDIP or BVLC moments and average 13% better performance than the method using wavelet moments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.103-114
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• 2005

This paper presents a content-based image retrieval (CBIR) method using the combination of color and texture features. As a color feature, a color autocorrelogram is chosen which is extracted from the hue and saturation components of a color image. As a texture feature, BDIP(block difference of inverse probabilities) and BVLC(block variation of local correlation coefficients) are chosen which are extracted from the value component. When the features are extracted, the color autocorrelogram and the BVLC are simplified in consideration of their calculation complexity. After the feature extraction, vector components of these features are efficiently quantized in consideration of their storage space. Experiments for Corel and VisTex DBs show that the proposed retrieval method yields 9.5% maximum precision gain over the method using only the color autucorrelogram and 4.0% over the BDIP-BVLC. Also, the proposed method yields 12.6%, 14.6%, and 27.9% maximum precision gains over the methods using wavelet moments, CSD, and color histogram, respectively.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.577-580
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• 1999

Padding is a technique that enables applying conventional discrete cosine transform to encode boundary blocks of arbitrarily shaped objects by assigning imaginary values to the pixels that are not included in the object. Padding prevents the increase of high frequency DCT coefficients. However, in some boundary blocks, too many padded pixels are coded due to a small portion of object pixels. To reduce the number of padded pixels and to improve coding efficiency, we propose a block merging method for texture coding. The proposed mothed searches the shape information of boundary blocks and excludes the 4$\times$4 pixels of 8$\times$8 blocks if all the 4$\times$4 pixels are in the background region, and merges the remained 4$\times$4 pixels into new 8$\times$8 blocks. Experimental results show that our proposed method yields a rate-distortion gain about 0.5~1.6㏈ compared to conventional padding method, LPE

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.412-434
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• 2023

This study discusses the high-order diffusion method in the wavelet domain. It aims to improve the edge protection capability of the high-order diffusion method using wavelet coefficients that can reflect image information. During the first step of the proposed diffusion method, the wavelet packet decomposition is a more refined decomposition method that can extract the texture and structure information of the image at different resolution levels. The high-frequency wavelet coefficients are then used to construct the edge detection function. Subsequently, because accurate wavelet coefficients can more accurately reflect the edges and details of the image information, by introducing the idea of state weight, a scheme for recovering wavelet coefficients is proposed. Finally, the edge detection function is constructed by the module of the wavelet coefficients to guide high-order diffusion, the denoised image is obtained. The experimental results showed that the method presented in this study improves the denoising ability of the high-order diffusion model, and the edge protection index (SSIM) outperforms the main methods, including the block matching and 3D collaborative filtering (BM3D) and the deep learning-based image processing methods. For images with rich textural details, the present method improves the clarity of the obtained images and the completeness of the edges, demonstrating its advantages in denoising and edge protection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.465-474
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• 2005

In this paper, a directional reconstruction of lost block in image over noisy channel is presented. DCT coefficients or pixel values in the lost blocks are recovered by using the linear interpolation with available neighboring blocks that are adaptively selected by the directional measure that are composed of the DDC (Difference of DC opposite blocks)and SAC(Similarity of AC opposite blocks) between opposite blocks around lost blocks. The proposed directional recovery method is effective for the strong edge and texture regions because we do not make use of the fixed 4-neighboring blocks but exploit the varying neighboring blocks adaptively by the directional information in the local image. In this paper, we describe the novel directional measure(CDS: Combination of DDC and SAC) composed of the DDC and the SAC and select the usable block to recover the lost block with the directional measure. The proposed method shows about 0.6dB PSNR improvement in average compared to the conventional methods.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.125-132
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• 2004

In this paper, a watermark inserting method according to the sensitivity of human visual system and minimizing distortion of original DCT coefficients in DCT transform domain is proposed. The proposed method inserts a more robust watermark in the insensitive block of human vision by reordering the blocks according to the human visual system which is appropriate to the JPEG image compression. It also enhances the invisibility and robustness in high compression rate in terms of the watermark inserting method within the block which minimizes distortions of original DCT coefficients. The computer simulation results show that the proposed method maintains high image quality and good robustness in high compression rate compared with conventional watermarking method.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1173-1176
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• 2005

Depending on the characteristics of each 3D-DCT block, images can be classified into three types: images with motion and textures, images with high textures and little motion, images with little textures and little motion. In this paper, we propose an adaptive watermarking method using these characteristics of each 3D-DCT block. and the human visual system. The proposed method classifies patterns of 3D-DCT blocks based on the motion and texture information, and classifies the image type according to the ratio of these patterns. The watermark is inserted proportional to the 3D-DCT coefficients by using pattern adaptive JND, which. makes the proposed watermarking robust by inserting watermarks in as many blocks as possible. Experimental results show that the proposed method achieves better performance in terms of invisibility and robustness than the previous method.