论文标题
发现图像质量评估的局限
Discovering Limitations of Image Quality Assessments with Noised Deep Learning Image Sets
论文作者
论文摘要
图像质量很重要，并且可能会影响图像处理和计算机视觉的整体性能以及出于许多其他原因。因此，图像质量评估（IQA）是从航空摄影解释到对象检测到医学图像分析的不同应用中的重要任务。在先前的研究中，用高分辨率（至少512x384像素）评估了棕褐色算法和PSNR算法，但图像集相对较小（不超过4,744张图像）。但是，科学家尚未评估低分辨率（不超过32x32像素），多扰动，大图像集（例如，有60,000个不同的图像不计算其扰动）的IQA算法。这项研究通过实验研究探讨了这两种IQA算法。我们首先选择了两个深度学习图像集，CIFAR-10和MNIST。然后，我们添加了68个扰动，以特定序列和噪声强度为图像添加噪声。此外，我们跟踪了具有单一和乘数图像的两种IQA算法的性能输出。在定量分析实验结果之后，我们报告了两种NOISED CIFAR-10和MNIST图像集的两个IQA的局限性。我们还解释了性能降解的三个潜在根本原因。这些发现指出了两种IQA算法的弱点。该研究结果为科学家和工程师提供了指导，开发了准确，强大的IQA算法。所有源代码，相关图像集和数字均在网站上共享（https://github.com/caperock/imagequality），以支持未来的科学和工业项目。
Image quality is important, and can affect overall performance in image processing and computer vision as well as for numerous other reasons. Image quality assessment (IQA) is consequently a vital task in different applications from aerial photography interpretation to object detection to medical image analysis. In previous research, the BRISQUE algorithm and the PSNR algorithm were evaluated with high resolution (atleast 512x384 pixels), but relatively small image sets (no more than 4,744 images). However, scientists have not evaluated IQA algorithms on low resolution (no more than 32x32 pixels), multi-perturbation, big image sets (for example, tleast 60,000 different images not counting their perturbations). This study explores these two IQA algorithms through experimental investigation. We first chose two deep learning image sets, CIFAR-10 and MNIST. Then, we added 68 perturbations that add noise to the images in specific sequences and noise intensities. In addition, we tracked the performance outputs of the two IQA algorithms with singly and multiply noised images. After quantitatively analyzing experimental results, we report the limitations of the two IQAs with these noised CIFAR-10 and MNIST image sets. We also explain three potential root causes for performance degradation. These findings point out weaknesses of the two IQA algorithms. The research results provide guidance to scientists and engineers developing accurate, robust IQA algorithms. All source codes, related image sets, and figures are shared on the website (https://github.com/caperock/imagequality) to support future scientific and industrial projects.
