A tone-mapped image (TMI) obtained from the corresponding high dynamic range (HDR) image induces artifacts and distortion, which might result in the loss of structure information and impaired color. By analyzing the visual characteristics of TMIs, this work proposes a robust blind visual quality evaluation method for TMIs by using gradient and chromatic statistics (VQGC). First, motivated by the perceptual mechanism that the human visual system (HVS) is sensitive to image structure variation, we employ the gradient features to measure structure degradation in TMIs. To predict structure distortion accurately, we compute the gradient magnitude and orientation to measure image structure variation, and the relative gradient magnitude and orientation are also computed to capture microstructure change. Second, the color invariance descriptors are utilized to capture the visual degradation of colorfulness by local binary pattern (LBP) on four chromatic feature maps. Finally, the gradient and chromatic features are combined together as the final quality-aware feature vector, which is applied to assess the perceptual quality of TMIs by support vector regression (SVR). Comparison experiments show that the performance of the proposed method is better than other existing blind quality assessment methods on public databases.

中文翻译：

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通过梯度和色度统计分析对色调映射图像进行盲质量评估

从相应的高动态范围（HDR）图像获得的色调映射图像（TMI）会导致伪影和失真，这可能会导致结构信息丢失和颜色受损。通过分析TMI的视觉特性，这项工作提出了一种使用梯度和色度统计（VQGC）的鲁棒的TMI盲视觉质量评估方法。首先，受人类视觉系统（HVS）对图像结构变化敏感的感知机制的启发，我们采用梯度特征来测量TMI中的结构退化。为了准确地预测结构变形，我们计算梯度幅度和方向以测量图像结构变化，并且还计算相对梯度幅度和方向以捕获微结构变化。第二，颜色不变性描述符用于通过四个彩色特征图上的局部二进制模式（LBP）捕获色彩的视觉退化。最后，将梯度和色度特征组合在一起，作为最终的质量感知特征向量，该向量将用于通过支持向量回归（SVR）评估TMI的感知质量。对比实验表明，该方法的性能优于公共数据库上其他现有的盲目质量评估方法。