Article: Reversible image watermarking based on texture analysis of grey level co-occurrence matrix Journal: International Journal of Computational Science and Engineering (IJCSE) 2019 Vol.19 No.1 pp.83 - 92 Abstract: Embedding the watermark in the complex area of the image can effectively improve concealment. However, most methods simply use the mean squared error (MSE) and some simple methods to judge the texture complexity. In this paper, we propose a new texture analysis method based on grey level co-occurrence matrix (GLCM) and provide an in-depth discussion on how to accurately choose a complex region. This new method is applied to the reversible image watermarking. Firstly, the original host image is divided into 128 * 128 sub blocks. Then, we use the mean square error to assign the weight of the four texture feature parameters to establish the relationship between the characteristic parameters and the complexity of an image sub block. Applying this formulaic series, we can calculate the complexity of each sub block, along with the selection of the maximum sub blocks of the texture complexity. If the embedding position is insufficient, then we select the second sub block to be embedded in the watermark, until the satisfactory embedding capacity is reached. Pairwise prediction error extend (PPEE) is used to hide the data. Inderscience Publishers - linking academia, business and industry through research

Title: Reversible image watermarking based on texture analysis of grey level co-occurrence matrix

Authors: Shu-zhi Li; Qin Hu; Xiao-hong Deng; Zhao-quan Cai

Addresses: School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China ' School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China ' School of Applied Science, Jiangxi University of Science and Technology, Ganzhou, 341000, China ' Huizhou University, Huizhou, 516007, China

Abstract: Embedding the watermark in the complex area of the image can effectively improve concealment. However, most methods simply use the mean squared error (MSE) and some simple methods to judge the texture complexity. In this paper, we propose a new texture analysis method based on grey level co-occurrence matrix (GLCM) and provide an in-depth discussion on how to accurately choose a complex region. This new method is applied to the reversible image watermarking. Firstly, the original host image is divided into 128 * 128 sub blocks. Then, we use the mean square error to assign the weight of the four texture feature parameters to establish the relationship between the characteristic parameters and the complexity of an image sub block. Applying this formulaic series, we can calculate the complexity of each sub block, along with the selection of the maximum sub blocks of the texture complexity. If the embedding position is insufficient, then we select the second sub block to be embedded in the watermark, until the satisfactory embedding capacity is reached. Pairwise prediction error extend (PPEE) is used to hide the data.

Keywords: grey level co-occurrence matrix; GLCM; image sub block; texture complexity; reversible image watermarking.

DOI: 10.1504/IJCSE.2019.099642

International Journal of Computational Science and Engineering, 2019 Vol.19 No.1, pp.83 - 92

Received: 28 May 2016
Accepted: 04 Nov 2016
Published online: 20 May 2019 *

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Title: Reversible image watermarking based on texture analysis of grey level co-occurrence matrix

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