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Difference of Visual Information Metric Based on Entropy of Primitive

Yanghong Zhang1, Feng Sun2, Liwei Tian1, Jinfeng Li3, Longqing Zhang3, Shengfu Lan3

1 Guangdong University of Science and Technology, Dong Guan, China.
2 Lenovo (Shanghai) Information Technology Co., Ltd., Shanghai, China.

* Corresponding Author: Longqing Zhang. Email: email.

Computers, Materials & Continua 2020, 62(2), 817-831.


Image sparse representation is a method of efficient compression and coding of image signal in the process of digital image processing. Image after sparse representation, to enhance the transmission efficiency of the image signal. Entropy of Primitive (EoP) is a statistical representation of the sparse representation of the image, which indicates the probability of each base element. Based on the EoP, this paper presents an image quality evaluation method-Difference of Visual Information Metric (DVIM). The principle of this method is to evaluate the image quality with the difference between the original image and the distorted image. The comparative experiments between DVIM & PSNR & SSIM are carried out. It was found that there was a great improvement in the image quality evaluation of geometric changes. This method is an effective image quality evaluation method, which overcomes the weakness of other quality evaluation methods for geometrically changing images to a certain extent, and is more consistent with the subjective observation of the human eye.


Cite This Article

APA Style
Zhang, Y., Sun, F., Tian, L., Li, J., Zhang, L. et al. (2020). Difference of visual information metric based on entropy of primitive. Computers, Materials & Continua, 62(2), 817-831.
Vancouver Style
Zhang Y, Sun F, Tian L, Li J, Zhang L, Lan S. Difference of visual information metric based on entropy of primitive. Comput Mater Contin. 2020;62(2):817-831
IEEE Style
Y. Zhang, F. Sun, L. Tian, J. Li, L. Zhang, and S. Lan "Difference of Visual Information Metric Based on Entropy of Primitive," Comput. Mater. Contin., vol. 62, no. 2, pp. 817-831. 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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