• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.18-28
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• 1997

This paper presents image data compression using a classified vector quantization (CVQ) which categories edge blocks according to the energy distribution of subimages in the discrete cosine transform domain. Classifying the edge blocks enhances visual quality of the compressed images while maintaining a high compression ratio. The proposed classification method categories subimages into eight lypes of edge features according to an energy distribution. A neural network, trained with the data generated from the proposed classification method, can successfully classify subimages to eight edge categories. Experimental results are given to show how the (1VQ method incorporatd with a neural network can produce faithful compressed image quality for high compression ratios.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.44-53
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• 1999

This paper proposes an algorithm that postprocesses wavelet-compressed images by using regularized iterative image restoration. First, an appropriate modeling the image degradation system for wavelet-compression system is needed. Then, the method which uses one of nonlinear functions as constraint in regularized iterative restoration is proposed in order to remove coding artifacts efficiently, such as ringing artifact and blocking artifact, resulted from loss of high frequency coefficients. Lastly, experimental results show superiority of proposed algorithm as compared with existing algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.2
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• pp.325-333
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• 1997

In fractral decoding procedure, the reconstructed image is obtained by iteratively applying the contractive transform to an arbitrary initial image. But this method is not suitable for the fast decoding because convergence speed depends on the selection of initial image. Therefore, the initial image to achieve fast decoding should be selected. In this paper, we propose an initial image estimation that can be applied to various decoding methods. The initial image similar to the original image is estimated by using only the compressed data so that the proposed method does not affect the compression ratio. From the simulation, the PSNR of the proposed initial image is 6dB higher han that of ones iterated output image of conventional decoding with Babaraimage. Computations in addition and multiplication are reduced about 96%. On the other hands, if we apply the proposed initial image to other decoding algorithms, the faster convergence speed is expected.

• Journal of the Semiconductor & Display Technology
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• v.15 no.1
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• pp.33-35
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• 2016

This paper proposes a method utilizing image compression and transmission method for image segmentation in order to reduce the time required in the process of analyzing the image information that has in the image compression process. Many studies of existing with respect to the image segmentation are being studied as a way to split a lot of a particular part in the image. We divide full image into the N equal parts. And it is compressed using the field coding. This will reduce the time-consuming than using the conventional method.

• Progress in Medical Physics
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• v.27 no.1
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• pp.1-7
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• 2016

In this work, we considered a compressed-sensing (CS)-based image deblurring scheme with a total-variation (TV) regularization penalty for improving image characteristics in digital tomosynthesis (DTS). We implemented the proposed image deblurring algorithm and performed a systematic simulation to demonstrate its viability. We also performed an experiment by using a table-top setup which consists of an x-ray tube operated at $90kV_p$, 6 mAs and a CMOS-type flat-panel detector having a $198-{\mu}m$ pixel resolution. In the both simulation and experiment, 51 projection images were taken with a tomographic angle range of ${\theta}=60^{\circ}$ and an angle step of ${\Delta}{\theta}=1.2^{\circ}$ and then deblurred by using the proposed deblurring algorithm before performing the common filtered-backprojection (FBP)-based DTS reconstruction. According to our results, the image sharpness of the recovered x-ray images and the reconstructed DTS images were significantly improved and the cross-plane spatial resolution in DTS was also improved by a factor of about 1.4. Thus the proposed deblurring scheme appears to be effective for the blurring problems in both conventional radiography and DTS and is applicable to improve the present image characteristics.

• Journal of the Korean Graphic Arts Communication Society
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• v.21 no.1
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• pp.35-44
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• 2003

Because of computer developing, the digital image making the use of many a field of application with - web-above, electronic publishing. printing, dynamic image management and photo CD production etc., however many problems of save and management. The management image use of compression moth which don't have a affect on image, reduce file size. A study used sequential DCT0based mode and progressive DCT-based mode of JPEG(Joing Photographic Experts Group) compression method and Wavelet compression method. Therefore, the analog image and digital image was changed and applied by several stages according to compression rate. It made inquiries of the optimum compression rate that be compared quality transformation between original image and compressed image. As compression image was printing simply, the quality was studied by subjective valuation method, that was studied propriety and usefulness.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.781-783
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• 1999

A storage or transmission of image is difficult without image compression processing because the numbers of generated or reborned image data are very much. In case of the random signal, image compression efficiency is low doing without loss of image information, but compressibility by using JPEG is better. We used Huffman code of JPEG, it assigne the low bit value for data of a lot of generated frequency, assigne the high bit value for data of a small quantity. This paper improved image compression efficiency with transformming Q-factor and certified the results with compressed image. A proposed method is very efficience for continuos a color tone image.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1368-1370
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• 1996

The Information of Edges which takes small area comparing with the total image is very important in DNA images as well as general images. DNA image is the object should be managed by computing and it's demanding information is less than general images, but the accuracy is more important In a huge DNA image processing system such as DNA Information Bank, the performance depends on the size of image. In this paper, we extract the edge information and make it as a binary image. To reduce the size of the original image, it was applied by JPEG image compression method. The compressed image is combined with edge information when they are restored. As a result, Both the image compression ratio and restoration quality are optimized without the loss of critical information.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.29-38
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• 2022

Although image compression is one of the essential technologies to transmit image data on a variety of surveillance and mobile healthcare applications, it causes unnecessary compression artifacts such as blocking and ringing artifacts by the lossy compression in the limited network bandwidth. Recently, image restoration methods using convolutional neural network (CNN) show the significant improvement of image quality from the compressed images. In this paper, we propose Image Denoising Convolutional Neural Networks (IDCNN) to reduce the compression artifacts for the purpose of improving the performance of object classification. In order to evaluate the classification accuracy, we used the ImageNet test dataset consisting of 50,000 natural images and measured the classification performance in terms of Top-1 and Top-5 accuracy. Experimental results show that the proposed IDCNN can improve Top-1 and Top-5 accuracy as high as 2.46% and 2.42%, respectively.