• Title/Summary/Keyword: Adjacent pixel value

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An Efficient Pixel Value Prediction Algorithm using the Similarity and Edge Characteristics Existing in Neighboring Pixels Scanned in Inverse s-order (역 s-순으로 스캔된 주변 픽셀들에 존재하는 유사성과 에지 특성을 이용한 효율적인 픽셀 값 예측 기법)

  • Jung, Soo-Mok
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.11 no.1
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    • pp.95-99
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    • 2018
  • In this paper, we propose an efficient pixel value prediction algorithm that can accurately predict pixel value using neighboring pixel values scanned in reverse s-order in the image. Generally, image has similarity with similar values between adjacent pixel values, and may have directional edge characteristics. In this paper, we proposed a method to improve pixel value prediction accuracy by improving GAP(Gradient Adjacent Pixel) algorithm for predicting pixel value by using similarity between adjacent pixels and edge characteristics. The proposed method increases the accuracy of the predicted pixel value by precisely predicting the pixel value using the positional weights of the neighboring pixels. Experiments on real images confirmed the superiority of the proposed algorithm. The proposed algorithm is useful for applications such as reversible data hiding, reversible watermarking, and data compression applications.

Advanced Pixel Value Prediction Algorithm using Edge Characteristics in Image

  • Jung, Soo-Mok
    • International Journal of Internet, Broadcasting and Communication
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    • v.12 no.1
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    • pp.111-115
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    • 2020
  • In this paper, I proposed an effective technique for accurately predicting pixel values using edge components. Adjacent pixel values are similar to each other. That is, generally, similarity exists between adjacent pixels in an image. In the proposed algorithm, edge components are detected using the surrounding pixels in the first step, and pixel values are estimated using the edge components in the second step. Therefore, the prediction accuracy of the pixel value is improved and the prediction error is reduced. Pixel value prediction is a necessary technique for various applications such as image magnification and confidential data concealment. Experimental results show that the proposed method has higher prediction accuracy and fewer prediction error. Therefore, the proposed technique can be effectively used for applications such as image magnification and confidential data concealment.

Pixel value prediction algorithm using three directional edge characteristics and similarity between neighboring pixels

  • Jung, Soo-Mok
    • International Journal of Internet, Broadcasting and Communication
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    • v.10 no.1
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    • pp.61-64
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    • 2018
  • In this paper, a pixel value prediction algorithm using edge components in three directions is proposed. There are various directional edges and similarity between adjacent pixels in natural images. After detecting the edge components in the x-axis direction, the y-axis direction, and the diagonal axis direction, the pixel value is predicted by applying the detected edge components and similarity between neighboring pixels. In particular, the predicted pixel value is calculated according to the intensity of the edge component in the diagonal axis direction. Experimental results show that the proposed algorithm can effectively predict pixel values. The proposed algorithm can be used for applications such as reversible data hiding, reversible watermarking to increase the number of embedded data.

Detection of LSB Matching Revisited Using Pixel Difference Feature

  • Li, Wenxiang;Zhang, Tao;Zhu, Zhenhao;Zhang, Yan;Ping, Xin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.10
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    • pp.2514-2526
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    • 2013
  • This paper presents a detection method for least significant bit matching revisited (LSBMR) steganography. Previous research shows that the adjacent pixels of natural images are highly correlated and the value 0 appears most frequently in pixel difference. Considering that the message embedding process of LSBMR steganography has a weighted-smoothing effect on the distribution of pixel difference, the frequency of the occurrence of value 0 in pixel difference changes most significantly whereas other values approximately remain unchanged during message embedding. By analyzing the effect of LSBMR steganography on pixel difference distribution, an equation is deduced to estimate the frequency of difference value 0 using the frequencies of difference values 1 and 2. The sum of the ratio of the estimated value to the actual value as well as the ratio of the frequency of difference value 1 to difference value 0 is used as the steganalytic detector. Experimental results show that the proposed method can effectively detect LSBMR steganography and can outperform previous proposed methods.

Data Hiding Technique using the Characteristics of Neighboring Pixels and Encryption Techniques

  • Jung, Soo-Mok
    • International journal of advanced smart convergence
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    • v.11 no.4
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    • pp.163-169
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    • 2022
  • In this paper, we propose a data hiding technique that effectively hides confidential data in the LSB of an image pixel by using the characteristics of the neighboring pixels of the image and the encryption techniques. In the proposed technique, the boundary surface of the image and the flat surface with little change in pixel values are investigated. At the boundary surface of the image, 1 bit of confidential data is encrypted and hidden in the LSB of the boundary pixel to preserve the characteristics of the boundary surface. In the pixels of the plane where the change in pixel value is small, 2 bits secret data is encrypted and hidden in the lower 2 bits of the corresponding pixel. In this way, when confidential data is hidden in an image, the amount of confidential data hidden in the image is greatly increased while maintaining excellent image quality. In addition, the security of hidden confidential data is strongly maintained. When confidential data is hidden by applying the proposed technique, the amount of confidential data concealed increases by up to 92.2% compared to the existing LSB method. The proposed technique can be effectively used to hide copyright information in commercial images.

Design of Image Recognition Module for Face and Iris Area based on Pixel with Eye Blinking (눈 깜박임 화소 값 기반의 안면과 홍채영역 영상인식용 모듈설계)

  • Kang, Mingoo
    • Journal of Internet Computing and Services
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    • v.18 no.1
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    • pp.21-26
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    • 2017
  • In this paper, an USB-OTG (Uiversal Serial Bus On-the-go) interface module was designed with the iris information for personal identification. The image recognition algorithm which was searching face and iris areas, was proposed with pixel differences from eye blinking after several facial images were captured and then detected without any activities like as pressing the button of smart phone. The region of pupil and iris could be fast involved with the proper iris area segmentation from the pixel value calculation of frame difference among the images which were detected with two adjacent open-eye and close-eye pictures. This proposed iris recognition could be fast processed with the proper grid size of the eye region, and designed with the frame difference between the adjacent images from the USB-OTG interface with this camera module with the restrict of searching area in face and iris location. As a result, the detection time of iris location can be reduced, and this module can be expected with eliminating the standby time of eye-open.

Quality improvement scheme of magnified image by using gradient information between adjacent pixel values (인접 픽셀 값과의 기울기 정보를 이용한 확대 영상의 화질 개선 기법)

  • Jung, Soo-Mok
    • Journal of the Korea Society of Computer and Information
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    • v.17 no.2
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    • pp.59-67
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    • 2012
  • In this paper, an efficient interpolation scheme using gradient information between adjacent pixel values was proposed to estimate the value of interpolated pixel to have the locality which exists in real image and the characteristic of simple convex surface and simple concave surface which exist partially in the real image. PSNR(Peak Signal to Noise Ratio) was used to evaluate the performance of the proposed scheme. The PSNR values of the magnified images using the proposed scheme are greater than those of the magnified images using the previous interpolation schemes.

A Study on Atmospheric Correction in Satellite Imagery Using an Atmospheric Radiation Model (대기복사모형을 이용한 위성영상의 대기보정에 관한 연구)

  • Oh, Sung-Nam
    • Atmosphere
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    • v.14 no.2
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    • pp.11-22
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    • 2004
  • A technique on atmospheric correction algorithm to the multi-band reflectance of Landsat TM imagery has been developed using an atmospheric radiation transfer model for eliminating the atmospheric and surface diffusion effects. Despite the fact that the technique of satellite image processing has been continually developed, there is still a difference between the radiance value registered by satellite borne detector and the true value registered at the ground surface. Such difference is caused by atmospheric attenuations of radiance energy transfer process which is mostly associated with the presence of aerosol particles in atmospheric suspension and surface irradiance characteristics. The atmospheric reflectance depend on atmospheric optical depth and aerosol concentration, and closely related to geographical and environmental surface characteristics. Therefore, when the effects of surface diffuse and aerosol reflectance are eliminated from the satellite image, it is actually corrected from atmospheric optical conditions. The objective of this study is to develop an algorithm for making atmospheric correction in satellite image. The study is processed with the correction function which is developed for eliminating the effects of atmospheric path scattering and surface adjacent pixel spectral reflectance within an atmospheric radiation model. The diffused radiance of adjacent pixel in the image obtained from accounting the average reflectance in the $7{\times}7$ neighbourhood pixels and using the land cover classification. The atmospheric correction functions are provided by a radiation transfer model of LOWTRAN 7 based on the actual atmospheric soundings over the Korean atmospheric complexity. The model produce the upward radiances of satellite spectral image for a given surface reflectance and aerosol optical thickness.

Reversible Data Hiding Algorithm Based on Pixel Value Ordering and Edge Detection Mechanism

  • Nguyen, Thai-Son;Tram, Hoang-Nam;Vo, Phuoc-Hung
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.10
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    • pp.3406-3418
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    • 2022
  • Reversible data hiding is an algorithm that has ability to extract the secret data and to restore the marked image to its original version after data extracting. However, some previous schemes offered the low image quality of marked images. To solve this shortcoming, a new reversible data hiding scheme based on pixel value ordering and edge detection mechanism is proposed. In our proposed scheme, the edge image is constructed to divide all pixels into the smooth regions and rough regions. Then, the pixels in the smooth regions are separated into non overlapping blocks. Then, by taking advantages of the high correlation of current pixels and their adjacent pixels in the smooth regions, PVO algorithm is applied for embedding secret data to maintain the minimum distortion. The experimental results showed that our proposed scheme obtained the larger embedding capacity. Moreover, the greater image quality of marked images are achieved by the proposed scheme than that other previous schemes while the high EC is embedded.

Digital Image Enhancement Algorithm

  • Yoo, Suk Won
    • International Journal of Advanced Culture Technology
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    • v.4 no.3
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    • pp.48-55
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    • 2016
  • Conventional techniques for solving the noise problem have problems to generate different results, depending on the image size and weight values of the used masks, and they require many operations by using a complex formula. In this paper, we propose an image enhancement algorithm to solve the noise problem in a simple, yet easy-to-use way. For this purpose, we determined the difference between the noise of the two adjacent pixels for the horizontal and vertical, and for the two diagonal directions that each of the noise problem occurred, and then we got the average value of these pixel values. Then, we solve the noise problem by using the optimal average value in accordance with occurrence of the noise in the horizontal and vertical, and two adjacent pixels in a diagonal direction. As a result, we got the result that the noise solution in a simple, yet easy-to-use method to obtain a resultant image.