Abstract
Blind image quality assessment (BIQA) assesses the perceptual quality of the distorted image without any information about its original reference image. Features, in consistent with human visual system (HVS), have been proved effective for BIQA. Motivated by this, we propose a novel general purpose BIQA approach. Firstly, considering that HVS is sensitive to image texture and edge, the image gradient and wavelet decomposition is computed. Secondly, taking the direction sensitivity of HVS into account, the gray level co-occurrence matrixes (GLCMs) are calculated in two directions at four scales on the computed feature maps, i.e., gradient and wavelet decomposition maps, as well as the image itself. Then, four features are extracted for each of GLCM matrix. Finally, a regression model is established to map image features to subjective opinion scores. Extensive experiments are conducted on LIVE II, TID2013 and CSIQ databases, and show that the proposed method is superior to the state-of-the-art BIQA methods and comparable to SSIM and PSNR.
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This work was supported by the National Natural Science Foundation of China under Grant 61471262, the International (Regional) Cooperation and Exchange under Grant 61520106002.
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Yue, G., Hou, C., Ma, T., Yang, Y. (2018). Blind Image Quality Assessment via Analysis of GLCM. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_11
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DOI: https://doi.org/10.1007/978-3-319-74521-3_11
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