Abstract
This paper presents an improved structural similarity index (SSIM) for video quality assessment based on human visual system (HVS). To integrate visual characteristics to our SSIM, different weighted values are determined by those visual characteristics including contrast sensitivity, multi-channel structure, visual masking and so on. This method has the properties of simple and efficiency as the same of the SSIM method. And it is more suitable for human visual system due to fusing HVS. The experimental results show that the method can reflect people’s subjective feelings in a better way and is better than other traditional methods in fitting M2 (Correlation coefficient of Non-linear regression), M3 (Spearman rank), M4 (Outlier Ratio) of VQEG Phase I MOS.
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Tu, W., Xie, Z., Gan, L. (2012). Video Quality Assessment Combining Structural Distortion and Human Visual System. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_59
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DOI: https://doi.org/10.1007/978-3-642-33478-8_59
Publisher Name: Springer, Berlin, Heidelberg
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