论文标题
图像质量评估的强度敏感相似性指数
Intensity-Sensitive Similarity Indexes for Image Quality Assessment
论文作者
论文摘要
由于成像技术和计算机视觉的快速进步，图像质量评估（IQA）的重要性不断增加。在众多的IQA方法中，结构相似性（SSIM）指数及其变体可以更好地与人类视觉系统的感知质量相匹配。但是，当图像包含较低信息时，SSIM方法不足以敏感，其中重要信息仅占据图像的低比例，而大多数图像类似噪声，这在科学数据中很常见。因此，对于此类低信息图像，我们提出了两种新的IQA方法，强度加权SSIM索引和低信息相似性指数。此外，提出了辅助指数来协助评估。还展示了这些新的IQA方法在自然图像和特定场图像中的应用，例如射电天文图像，医学图像和遥感图像。结果表明，我们的IQA方法的性能优于最先进的SSIM方法，用于输入图像的高强度部分的差异，并且具有与原始和基于梯度的SSIM相似的性能，用于低强度零件的差异。不同的相似性指数适用于不同的应用程序，我们在结果中证明了这一点。
The importance of Image quality assessment (IQA) is ever increasing due to the fast paced advances in imaging technology and computer vision. Among the numerous IQA methods, Structural SIMilarity (SSIM) index and its variants are better matched to the perceived quality of the human visual system. However, SSIM methods are insufficiently sensitive, when images contain low information, where the important information only occupies a low proportion of the image while most of the image is noise-like, which is common in scientific data. Therefore, we propose two new IQA methods, InTensity Weighted SSIM index and Low-Information Similarity Index, for such low information images. In addition, auxiliary indexes are proposed to assist with the assessment. The application of these new IQA methods to natural images and field-specific images, such as radio astronomical images, medical images, and remote sensing images, are also demonstrated. The results show that our IQA methods perform better than state-of-the-art SSIM methods for differences in high-intensity parts of the input images and have similar performance to that of the original and gradient-based SSIM for differences in low-intensity parts. Different similarity indexes are suitable for different applications, which we demonstrate in our results.
