Abstract
Image quality assessment (IQA) aims to provide computational models to measure the image quality consistently with subjective assessments. The SSIM index brings IQA from pixel-based to structure-based stage. In this paper, a new similarity index based on SIFT features (SIFT-SSIM) for full reference IQA is presented. In the algorithm, proportion of matched features in extracted features of reference image and structural similarity are combined into a comprehensive quality index. Experiments on LIVE database demonstrate that SIFT-SSIM is competitive with most of state-of-the-art FR-IQA metrics, and it can achieve higher consistency with the subjective assessments in some distortion types.
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Lu, W., Li, C., Shi, Y., Sun, X. (2014). Image Quality Assessment Based on SIFT and SSIM. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Huang, K. (eds) Advances in Image and Graphics Technologies. IGTA 2014. Communications in Computer and Information Science, vol 437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45498-5_1
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DOI: https://doi.org/10.1007/978-3-662-45498-5_1
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