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A novel SSIM index for image quality assessment using a new luminance adaptation effect model in pixel intensity domain

机译：使用像素强度域中新的亮度适应效应模型的图像质量评估新SSIM指数

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摘要

The Structural SIMilarity (SSIM) is one of the most prominent image quality assessment (IQA) methods due to its high prediction performance and wide applicability for image quality optimization problems. To reflect the luminance adaptation (LA) characteristic of human visual system (HVS), SSIM is modelled to have high consistency with Weber's law. However, it inevitably has some intrinsic faults that wrongly incorporate the LA effect into SSIM. In this paper, we firstly analyze that Weber's law in the conventional SSIM index cannot precisely reflect the LA effect due to two reasons: (i) it is reported that Weber's law model is not able to precisely be fitted in the measured experimental results for the LA effect; (ii) SSIM is calculated with pixel intensity values, but Weber's law is applied for luminance values which have non-linear relations with the pixel intensity values. To solve this problem, we first theoretically derive a new LA effect model in pixel intensity domain using a Gamma correction function and a power-law model. We then devise a weight function for the LA effect, called LA-based local weight function (LALF) which allows the proposed LA effect model to be precisely incorporated into SSIM index. To verify the effectiveness of the proposed LALF-based SSIM, we perform comprehensive experiments on four large IQA databases. Experimental results show that the proposed LALF helps performance improvement of the SSIM index.
机译：结构相似性（SSIM）由于其较高的预测性能和对图像质量优化问题的广泛适用性，因此是最突出的图像质量评估（IQA）方法之一。为了反映人类视觉系统（HVS）的亮度适应（LA）特性，对SSIM进行建模以使其与韦伯定律具有高度一致性。但是，它不可避免地存在一些固有错误，这些错误将LA效应错误地纳入了SSIM中。在本文中，我们首先分析了传统SSIM指数中的韦伯定律不能精确反映LA的影响，这有两个原因：（i）据报道，韦伯定律模型不能精确地拟合到实测的实验结果中。 LA效应; （ii）利用像素强度值来计算SSIM，但是韦伯定律适用于与像素强度值具有非线性关系的亮度值。为了解决这个问题，我们首先在理论上使用Gamma校正函数和幂律模型在像素强度域中导出一个新的LA效应模型。然后，我们为LA效应设计权重函数，称为基于LA的局部权重函数（LALF），该函数可将提出的LA效应模型精确地合并到SSIM索引中。为了验证所提出的基于LALF的SSIM的有效性，我们在四个大型IQA数据库上进行了综合实验。实验结果表明，提出的LALF有助于改善SSIM索引的性能。

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