Weighted Fuzzy-Based PSNR for Watermarking

One of the problems of conventional visual quality evaluation criteria such as PSNR and MSE is the lack of appropriate standards based on the human visual system (HVS). They are calculated based on the difference of the corresponding pixels in the original and manipulated image. Hence, they practically do not provide a correct understanding of the image quality. Watermarking is an image processing application in which the image's visual quality is an essential criterion for its evaluation. Watermarking requires a criterion based on the HVS that provides more accurate values than conventional measures such as PSNR. This paper proposes a weighted fuzzybased criterion that tries to find essential parts of an image based on the HVS. Then these parts will have larger weights in computing the final value of PSNR. We compare our results against standard PSNR, and our experiments show considerable consequences. Keywords—HVS, watermark, imperceptibility, weighted

[1]  Nader Karimi,et al.  Framework for robust blind image watermarking based on classification of attacks , 2016, Multimedia Tools and Applications.

[2]  Liming Zhang,et al.  Saliency-Based Image Quality Assessment Criterion , 2008, ICIC.

[3]  Nader Karimi,et al.  ReDMark: Framework for residual diffusion watermarking based on deep networks , 2018, Expert Syst. Appl..

[4]  Nader Karimi,et al.  Adaptive blind image watermarking using edge pixel concentration , 2015, Multimedia Tools and Applications.

[5]  Itsuo Kumazawa,et al.  A comparative study of image quality assessment , 2018, 2018 International Workshop on Advanced Image Technology (IWAIT).

[6]  Oscar Castillo,et al.  Review of Recent Type-2 Fuzzy Image Processing Applications , 2017, Inf..

[7]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[8]  Huchuan Lu,et al.  Saliency Detection via Graph-Based Manifold Ranking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Kris Kitani,et al.  No-Reference Image Quality Assessment via Feature Fusion and Multi-Task Learning , 2020, ArXiv.

[10]  Ehsan Pourjavad,et al.  A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system , 2017, Journal of Intelligent Manufacturing.

[11]  Nader Karimi,et al.  Robust watermarking in non-ROI of medical images based on DCT-DWT , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[12]  Nader Karimi,et al.  Robust image watermarking scheme using bit-plane of hadamard coefficients , 2016, Multimedia Tools and Applications.

[13]  Yu-Wing Tai,et al.  Salient Region Detection via High-Dimensional Color Transform and Local Spatial Support , 2014, IEEE Transactions on Image Processing.

[14]  Mohammad Shorif Uddin,et al.  Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study , 2019, Journal of Computer and Communications.

[15]  Yu Fu,et al.  Visual saliency detection by spatially weighted dissimilarity , 2011, CVPR 2011.

[16]  Shahram Shirani,et al.  Subjective and Objective Quality Assessment of Image: A Survey , 2014, ArXiv.

[17]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[18]  Yuhui Zheng,et al.  Image segmentation by generalized hierarchical fuzzy C-means algorithm , 2015, J. Intell. Fuzzy Syst..