HVS-based structural similarity for image quality assessment
HVS-based structural similarity for image quality assessment
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DOI：
10.1109/icosp.2008.4697344
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
Bo Wang;Zhibing Wang;Yupeng Liao;Xinggang Lin
中科院分区：
文献类型：
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作者：
Bo Wang;Zhibing Wang;Yupeng Liao;Xinggang Lin
客观质量评估很重要，并且广泛用于图像处理。最近，提出了指定的结构相似性，该指标基于以下假设：人类视觉感知高度适应提取结构信息。在许多情况下，该指标的性能比PSNR更好，但如果评估不良模糊的图像，则失败。这种限制与人类视觉系统（HVS）的特征不一致，并导致我们将HVS字符应用于图像结构相似性的灵感。我们的方法基于HVS的结构相似性（HSSIM）在频域和空间域中采用HVS字符。可以在我们的实验中得出结论，HSSIM的性能比PSNR和SSIM更好，尤其是对于模糊的图像。
Objective quality assessment is important and widely used in image processing. Recently, the metric named structural similarity is proposed, which is based on the assumption that human visual perception is highly adapted for extracting structural information. This metric has a better performance than PSNR in many cases but fails in case evaluating the badly blurred images. This limitation is inconsistent with the characteristics of human visual system (HVS) and leads to our inspiration of applying HVS characters to images structural similarity. Our method, HVS-based structural similarity(HSSIM), employs the HVS characters both in frequency domain and spatial domain. It can be concluded in our experiment that HSSIM performs better than PSNR and SSIM, especially for badly blurred images.