• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.12
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• pp.746-755
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• 2014

Compressive sensing (CS) has drawn much interest as a new sampling technique that enables signals to be sampled at a much lower than the Nyquist rate. By noting that the block-based compressive sensing can still keep spatial correlation in measurement domain, in this paper, we propose a novel encoding technique for measurement data obtained in the block-based CS of natural image. We apply discrete wavelet transform (DWT) to decorrelate CS measurements and then assign a proper quantization scheme to those DWT coefficients. Thus, redundancy of CS measurements and bitrate of system are reduced remarkably. Experimental results show improvements in rate-distortion performance by the proposed method against two existing methods of scalar quantization (SQ) and differential pulse-code modulation (DPCM). In the best case, the proposed method gains up to 4 dB, 0.9 dB, and 2.5 dB compared with the Block-based CS-Smoothed Projected Landweber plus SQ, Block-based CS-Smoothed Projected Landweber plus DPCM, and Multihypothesis Block-based CS-Smoothed Projected Landweber plus DPCM, respectively.

• Journal of Broadcast Engineering
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• v.22 no.5
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• pp.643-651
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• 2017

Steganography is one of information hiding technologies and discriminated from cryptography in that it focuses on avoiding the existence the hidden information from being detected by third parties, rather than protecting it from being decoded. In this paper, as an image steganography method which uses images as media, we propose a new block matching method that embeds information into the discrete wavelet transform (DWT) domain. The proposed method, based on a statistical analysis, reduces loss of embedding capacity due to inequable use of candidate blocks. It works in such a way that computes the variance of each candidate block, preserves candidate blocks with high frequency components while reducing candidate blocks with low frequency components by compressing them exploiting the k-means clustering algorithm. Compared with the previous block matching method, the proposed method can reconstruct secret images with similar PSNRs while embedding higher-capacity information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6A
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• pp.618-627
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• 2002

In processing images using 2 dimensional Discrete Wavelet Transform(2D-DWT), the method to process the pixels around the image boundary may affect the quality of image and the cost to implement in hardware and software. This paper proposed an effective method to treat the boundary pixels, which is apt to implement in hardware and software without losing the quality of the image costly. This method processes the 2-D image as 1-D array so that 2-D DWT is performed by considering the image with the serial-sequential data structure (Serial-Sequential Processing). To show the performance and easiness in implementation of the proposed method, an image compression codec which compresses image and reconstructs it has been implemented and experimented. It included log-scale fried quantizer, but the entropy coder was not implemented. From the experimental results, the proposed method showed the SNR of almost the same SNR(Signal to Noise Ratio) to the Periodic Expansion(PE) method when the compression ratio(excluding entropy coding) of 2:1, 15.3% higher than Symmetric Expansion(SE) method, and 9.3% higher than 0-pixel Padding Expansion(ZPE) method. Also PE method needed 12.99% more memory space than the proposed method. By considering only the compression process, SE and ZPE methods needed additional operations than the proposed one. In hardware implementation, the proposed method in this paper had 5.92% of overall circuit as the control circuit, while SE, PE, and ZPE method has 22%, 21,2%, and 11.9% as the control circuit, respectively. Consequently, the proposed method can be thought more effective in implementing software and hardware without losing any image quality in the usual image processing applications.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.1-12
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• 2013

This paper explores design space of many-core processors for a fire feature extraction algorithm. This paper evaluates the impact of varying the number of cores and memory sizes for the many-core processor and identifies an optimal many-core processor in terms of performance, energy efficiency, and area efficiency. In this study, we utilized 90 samples with dimensions of $256{\times}256$ (60 samples containing fire and 30 samples containing non-fire) for experiments. Experimental results using six different many-core architectures (PEs=16, 64, 256, 1,024, 4,096, and 16,384) and the feature extraction algorithm of fire indicate that the highest area efficiency and energy efficiency are achieved at PEs=1,024 and 4,096, respectively, for all fire/non-fire containing movies. In addition, all the six many-core processors satisfy the real-time requirement of 30 frames-per-second (30 fps) for the algorithm.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.295-312
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• 2017

Digital video content is the most information-intensive and high-value content. Therefore, it is necessary to protect the intellectual property rights of these contents, and this paper also proposes a watermarking method of digital image for this purpose. The proposed method uses the frequency characteristics of 2-Dimensional Discrete Wavelet Transform (2D-DWT) for digital images and digital watermark on global data without using local or specific data of the image for watermark embedding. The method to insert digital watermark data uses a simple Quantization Index Modulation (QIM) and a multiple watermarking method that inserts the same watermark data in multiple. When extracting a watermark, multiple watermarks are extracted and the final watermark data is determined by a simple statistical method. This method is an empirical method for experimentally determining the parameters in the watermark embedding process. The proposed method performs experiments on various images against various attacks and shows the superiority of the proposed method by comparing the performance with the representative existing methods.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.55-64
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• 2013

Due to the popularization of digital high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy for anyone to make a high-quality counterfeit money. However, the probability of detecting a counterfeit money to the general public is extremely low. In this paper, we propose a counterfeit money detection algorithm using a general purpose scanner. This algorithm determines counterfeit money based on the different features in the printing process. After the non-local mean value is used to analyze the noises from each money, we extract statistical features from these noises by calculating a gray level co-occurrence matrix. Then, these features are applied to train and test the support vector machine classifier for identifying either original or counterfeit money. In the experiment, we use total 324 images of original money and counterfeit money. Also, we compare with noise features from previous researches using wiener filter and discrete wavelet transform. The accuracy of the algorithm for identifying counterfeit money was over 94%. Also, the accuracy for identifying the printing source was over 93%. The presented algorithm performs better than previous researches.