Abstract
The majority of existing objective panoramic image quality assessment algorithms are based on peak signal to noise ratio (PSNR) or structural similarity (SSIM). However, they are not highly consistent with human perception. In this paper, a new blind panoramic image quality assessment metric is proposed based on project-weighted gradient local binary pattern histogram (PWGLBP), which explores the structure degradation in sphere by combining with the nonlinear transformation relationship between the projected plane and sphere. Finally, support vector regression (SVR) is adopted to learn a quality predictor from feature space to quality score space. The experimental results demonstrate the superiority of our proposed metric compared with state-of-the-art objective PIQA methods.
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This work was supported by Natural Science Foundation of China under Grant No. 61671283, 61301113.
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Xia, Y., Wang, Y., Ye, P. (2020). Blind Panoramic Image Quality Assessment Based on Project-Weighted Local Binary Pattern. In: Zhai, G., Zhou, J., Yang, H., An, P., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2019. Communications in Computer and Information Science, vol 1181. Springer, Singapore. https://doi.org/10.1007/978-981-15-3341-9_21
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DOI: https://doi.org/10.1007/978-981-15-3341-9_21
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