Abstract
Recently, structural similarity image metric (SSIM) becomes the most popular model for image quality assessment (IQA). The idea behind SSIM is that natural images are highly structured, and estimate a general similarity of the image pairs from luminance, contrast and structure comparison. A novel similarity measure based on K-L transform is presented in this paper. It combines edge and texture components to provide a hierarchical description of image structure. We validate the performance of our algorithm with an extensive subjective study involving two sets of compressed images, the JPEG and the JPEG2000 images at the LIVE website. The experimental results show that the obtained quality metric had a high correlation with the subjective measure and outperforms SSIM.
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Jiang, C., Xiao, F., He, X. (2014). A New Image Structural Similarity Metric Based on K-L Transform. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_17
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DOI: https://doi.org/10.1007/978-3-662-45643-9_17
Publisher Name: Springer, Berlin, Heidelberg
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