• Journal of KIISE:Computer Systems and Theory
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• v.27 no.2
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• pp.117-122
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• 2000

The fractal image compression is based on the self-similarity that some area in an image is very similar to others. This compression method offers high compression ratio and fast decompression, but it has very long encoding time. To cut-off the encoding time, most researches give a restriction on domain blocks to be compared with a range block or make an effective search sequence of the domain blocks for a range block. However, most of them take much encoding time yet. In this research, we propose an algorithm that greatly reduces the encoding time by considering the coherence between range blocks. This algorithm first classifies all range blocks into some groups using the coherence between range blocks, and then searches corresponding domain blocks only for the key block of each group. If this scheme is joined in a prior work of the other fractal compression algorithm, it will give a great effectiveness to encoding time.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2254-2263
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• 2000

This paper proposes an efficient predictive image-compression technique based on vector quantization of blocks of pels. In the proposed method edge contents of blocks control the selection of predictors and block shapes as well. The maximum number of bits assigned to quantizers has been in creased to 3bits/pel from 1/5bits/pel, the setting employed by forerunners in predictive vector quantization of images. This increase prevents the saturation in SNR observed in their results in high bit rates. The variable block shape is instrumental in eh reconstruction of edges. The adaptive procedure is controlled by means of he standard deviation ofp rediction errors generated by a default predictor; the standard deviation address a decision table which can be set up beforehand. eh proposed method is characterized by overall improvements in image quality over A-VQ-PE and A-DCT VQ, both of which are known for their efficient use of vector quantizers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1499-1513
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• 2014

This paper proposes a novel blind image watermarking scheme exploiting Block Truncation Coding (BTC). Most of existing BTC-based watermarking or data hiding methods embed information in BTC compressed images by modifying the BTC encoding stage or BTC-compressed data, resulting in watermarked images with bad quality. Other than existing BTC-based watermarking schemes, our scheme does not really perform the BTC compression on images during the embedding process but uses the parity of BTC quantization data to guide the watermark embedding and extraction processes. In our scheme, we use a binary image as the original watermark. During the embedding process, the original cover image is first partitioned into non-overlapping $4{\times}4$ blocks. Then, BTC is performed on each block to obtain its BTC quantized high mean and low mean. According to the parity of high mean and the parity of low mean, two watermark bits are embedded in each block by modifying the pixel values in the block to make sure that the parity of high mean and the parity of low mean in the modified block are equal to the two watermark bits. During the extraction process, BTC is first performed on each block to obtain its high mean and low mean. By checking the parity of high mean and the parity of low mean, we can extract the two watermark bits in each block. The experimental results show that the proposed watermarking method is fragile to most image processing operations and various kinds of attacks while preserving the invisibility very well, thus the proposed scheme can be used for image authentication.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1944-1947
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• 2003

In this paper, we propose a dynamic-based compression system by creating mosaic background and transmitting the change information. A dynamic mosaic of the background is progressively integrated in a single image using the camera motion information. For the camera motion estimation, we calculate affine motion parameters for each frame sequentially with respect to its previous frame. The camera motion is robustly estimated on the background by discriminating between background and foreground regions. The modified block-based motion estimation is used to separate the back-ground region.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2264-2275
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• 1994

In this paper, image compression based on neural networks is presented with block classification and coding. Multilayer neural networks with error back-propagation learning algorithm are used to transform the normalized image date into the compressed hidden values by reducing spatial redundancies. Image compression can basically be achieved with smaller number of hidden neurons than the numbers of input and output neurons. Additionally, the image blocks can be grouped for adaptive compression rates depending on the characteristics of the complexity of the blocks in accordance with the sensitivity of the human visual system(HVS). The quantized output of the hidden neuron can also be entropy coded for an efficient transmission. In computer simulation, this approach lie in the good performances even with images outside the training set and about 25:1 compression rate was achieved using the entropy coding without much degradation of the reconstructed images.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.27-34
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• 2009

Block truncation coding (BTC) is a simple and efficient image compression technique. However, it yields images of unacceptable quality and significant blocking effects are seen when the block size is increasing. Ordered dither block truncation coding (ODBTC) was proposed to solve above problem maintaining the same compression capability. In this paper, we propose a data hiding method which can be applied to halftone images. EMD was developed by Zhang and Wang [6] for data hiding in the grayscale image. Therefore, EMD is not proper for bitmap images. In this paper, we show that EMD can be used to apply bitmap halftone image. In our experiments, we prove that our method is efficient in a halftone image.

• 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.

• 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.

• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.1-8
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• 2012

In this paper, we propose a rate-distortion optimization method to improve the coding efficiency of the conventional 3D-DCT based compression method using adaptive block mode selection for integral images. In the conventional 3D-DCT based compression method, 3D-DCT blocks of variable block sizes are adaptively selected depending on the characteristics of integral images, and then 3D-DCT is performed. The proposed method applies a rate-distortion optimization to determine the optimal block mode. In addition, we suggest the optimal Lagrangian parameter for integral images. Experimental results show that the proposed method gives bit-rate reduction of about 5%.

• Journal of KIISE:Software and Applications
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• v.32 no.10
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• pp.949-955
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• 2005

In this Paper, we propose fast block matching algorithm by dividing complex areas based on complexity order of reference block and square sub-block to reduce an amount of computation of full starch(FS) algorithm for fast motion estimation, while keeping the same prediction quality compared with the full search algorithm. By using the fact that matching error is proportional to the gradient of reference block, we reduced unnecessary computations with square sub-block adaptive matching scan based image complexity instead of conventional sequential matching scan and row/column based matching scan. Our algorithm reduces about $30\%$ of computations for block matching error compared with the conventional partial distortion elimination(PDE) algorithm without any prediction quality, and our algorithm will be useful in real-time video coding applications using MPEG-4 AVC or MPEG-2.