• Journal of Digital Contents Society
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• v.4 no.2
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• pp.211-221
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• 2003

The objective of this paper is performance analysis of the digital document image decimation algorithm which generates a value of decimated element by an average of a target pixel value and a value of neighbor intelligible element to adaptively reflect the merits of ZOD method and FOD method on the decimated image. First, a target pixel located at the center of sliding window is selected, then the gradient amplitudes of its right neighbor pixel and its lower neighbor pixel are calculated using first order derivative operator respectively. Secondly, each gradient amplitude is divided by the summation result of two gradient amplitudes to generate each local intelligible weight. Next, a value of neighbor intelligible element is obtained by adding a value of the right neighbor pixel times its local intelligible weight to a value of the lower neighbor pixel times its intelligible weight. The decimated image can be acquired by applying the process repetitively to all pixels in input image which generates the value of decimated element by calculating the average of the target pixel value and the value of neighbor intelligible element. In this paper, the performance comparison of proposed method and conventional methods in terms of subjective performance and hardware complexity is analyzed and the preferable approach for developing the decimation algorithm of the digital document image on the basis of this analysis result has been reviewed.

• ETRI Journal
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• v.40 no.5
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• pp.677-686
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• 2018

A novel data hiding technique based on modified histogram shifting that incorporates multi-bit secret data hiding is proposed. The proposed technique divides the image pixel values into embeddable and nonembeddable pixel values. Embeddable pixel values are those that are within a specified limit interval surrounding the peak value of an image. The limit interval is calculated from the number of secret bits to be embedded into each embeddable pixel value. The embedded secret bits can be perfectly extracted from the stego image at the receiver side without any overhead bits. From the simulation, it is found that the proposed technique produces a better quality stego image compared to other data hiding techniques, for the same embedding rate. Since the proposed technique only embeds the secret bits in a limited number of pixel values, the change in the visual quality of the stego image is negligible when compared to other data hiding techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4537-4556
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• 2020

This research was based on the study conducted by Khodaei et al. (2012), namely, the least-significant-bit (LSB) substitution combined with the pixel-value differencing (PVD) steganography, and presented an improved irreversible image steganography method. Such a method was developed through integrating the improved LSB substitution with the modulus function-based PVD steganography to increase steganographic capacity of the original technique while maintaining the quality of images. It partitions the cover image into non-overlapped blocks, each of which consists of 3 consecutive pixels. The 2nd pixel represents the base, in which secret data are embedded by using the 3-bit LSB substitution. Each of the other 2 pixels is paired with the base respectively for embedding secret data by using an improved modulus PVD method. The experiment results showed that the method can greatly increase steganographic capacity in comparison with other PVD-based techniques (by a maximum amount of 135%), on the premise that the quality of images is maintained. Last but not least, 2 security analyses, the pixel difference histogram (PDH) and the content-selective residual (CSR) steganalysis were performed. The results indicated that the method is capable of preventing the detection of the 2 common techniques.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.4
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• pp.68-75
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• 2008

This paper proposes a new data hiding method for binary images using the weighting value of pixel-value differencing. The binary cover image is partitioned into non-overlapping sub-blocks and find the most suitable position to embed a secret bit for each sub-block. The proposed method calculates the weighted value for a sub-block to pivot a pixel to be changed. This improves the image quality of the stego-image. The experimental results show that the proposed method achieves a good visual quality and high capacity.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.431-438
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• 2016

In this paper, we present an upsampling technique for depth map image using color and depth weights. First, we construct a high-resolution image using the bilinear interpolation technique. Next, we detect a common edge region using RGB color space, HSV color space, and depth image. If an interpolated pixel belongs to the common edge region, we calculate weighting values of color and depth in $3{\times}3$ neighboring pixels and compute the cost value to determine the boundary pixel value. Finally, the pixel value having minimum cost is determined as the pixel value of the high-resolution depth image. Simulation results show that the proposed algorithm achieves good performance in terns of PSNR comparison and subjective visual quality.

• Journal of Sensor Science and Technology
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• v.29 no.2
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• pp.123-127
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• 2020

In this paper, the estimation of the disparity for depth extraction in monochrome complementary metal-oxide-semiconductor (CMOS) image sensors with offset pixel apertures is presented. To obtain the depth information, the disparity information between two different channel data of the offset pixel apertures is required. The disparity is caused by the difference in the response angle between the left- and right-offset pixel aperture images. A depth map is implemented by the generated disparity. Therefore, the disparity is the most important factor for realizing 3D images from the designed CMOS image sensor with offset pixel apertures. The disparity is influenced by the pixel height and offset value of the offset pixel aperture. To confirm this correlation, the offset value is set to maximum within the pixel area, and the disparity values corresponding to the difference in the heights are calculated and compared. The disparity is derived using the camera-lens formula. Two monochrome CMOS image sensors with offset pixel apertures are used in the disparity estimation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.29-35
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• 2012

In this paper, it is presented the method of image recovering which existing is only pixel processing, but suggesting method is concluding image clustering overlap degree after classfying around unit fixel to crowd pixel. Concluding overlap degree threshold value is after identifying pattern pixel and grasping geometry structure of sample pattern and deduction of deciding function. distinguishing feature space is above four dimension is reason of not visual observation of pattern structure. consideration of distribution structure is distance of center of crowd pixel, the number of each crowd pattern pixel and standard deviation. The over threshold value elimate the overlap image and the downward is recovered and established dynamic image. memory storage deduction of 20% and elevation of 15% performance is estimated in recovery of image.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.74-76
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• 2010

Digital images or videos are used in modern digital devices. The resolution of HDTV in digital broadcasting system is higher than that of previous analog systems. Also, mobile phone with 3G can provide images as well as video streaming services in realtime. In these circumstances, the visual quality of images has become an important factor. We can make image clear by transient improvement process that reduces transient in edges. In this paper, we present an transient improvement algorithm. The proposed algorithm improves edges by making smooth edge to steep edge. Before performing transient improvement algorithm, edge detection algorithm should be operated. Laplacian operator is used in edge detection, and the absolute value of it is used to calculate gain value. Then, local maximum and minimum values are computed to discriminate current pixel value to raise up or pull down. Compensating value that gain value multiplies with the difference between maximum (or minimum) value and current pixel value adds (or subtracts) to current pixel value. That is, improved signal is generated by making the narrow transient of edge. The advantage of proposed algorithm is that it doesn't produce shooting problem like overshoot or undershoot.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1371-1378
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• 2017

In this paper, we present an upsampling technique for depth map image using selective bilateral weights and a color weight using laplacian function. These techniques prevent color texture copy problem, which problem appears in existing upsamplers uses bilateral weight. First, we construct a high-resolution image using the bicubic interpolation technique. Next, we detect a color texture region using pixel value differences of depth and color image. If an interpolated pixel belongs to the color texture edge region, we calculate weighting values of spatial and depth in $3{\times}3$ neighboring pixels and compute the cost value to determine the boundary pixel value. Otherwise we use color weight instead of depth weight. Finally, the pixel value having minimum cost is determined as the pixel value of the high-resolution depth image. Simulation results show that the proposed algorithm achieves good performance in terns of PSNR comparison and subjective visual quality.