论文标题
无参考图像质量评估的多功能发表功能
Multi-pooled Inception features for no-reference image quality assessment
论文作者
论文摘要
图像质量评估（IQA）是从自动视频流到显示技术的广泛应用程序的重要组成部分。此外，对图像质量的测量需要对图像内容和特征进行平衡研究。我们提出的方法通过将全球平均池（GAP）层附加到ImageNet数据库预验证的卷积神经网络（CNN）上的多个成立模块来提取视觉特征。与以前的方法相反，我们不从输入图像中获取补丁。取而代之的是，输入图像被视为一个整体，并通过验证的CNN主体运行，以提取独立于分辨率的，多层的深度特征。结果，我们的方法可以很容易地推广到任何输入图像大小和验证的CNN。因此，我们提出了有关CNN基础体系结构以及不同深度特征的有效性的详细参数研究。我们证明，我们的最佳建议（称为Multigap-Nriqa）能够在三个基准IQA数据库上提供最新结果。此外，使用野生图像质量挑战数据库中的LIVE在交叉数据库测试中也证实了这些结果。
Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run through a pretrained CNN body to extract resolution-independent, multi-level deep features. As a consequence, our method can be easily generalized to any input image size and pretrained CNNs. Thus, we present a detailed parameter study with respect to the CNN base architectures and the effectiveness of different deep features. We demonstrate that our best proposal - called MultiGAP-NRIQA - is able to provide state-of-the-art results on three benchmark IQA databases. Furthermore, these results were also confirmed in a cross database test using the LIVE In the Wild Image Quality Challenge database.
