When deep convolutional neural networks perform feature extraction, the features computed at each layer express different abstractions of visual information. The earlier layers extract highly compact low-level features such as bandpass and directional primitives, whereas deeper layers extract structural features of increasing abstraction, similar to contours, shapes, and edges, becoming less effable as the depth increases. We propose a different kind of end-to-end no-reference (NR) image quality assessment (IQA) model, which is defined as a multi-depth output convolutional neural network (MoNET). It accomplishes this by mapping both shallow and deep features to perceived quality. MoNET delivers three outputs that express shallow (lower-level) and deep (high-level) features, and maps them to subjective quality scores. The multiple outputs are combined into a single, final quality score. MoNET does this by combining the responses of three learning machines, so it may be viewed as a form of ensemble learning. The experimental results on three public image quality databases show that our proposed model achieves better performance than other state-of-the-art NR IQA algorithms.

中文翻译：

---

MoNET：基于多深度输出网络的无参考图像质量评估

当深度卷积神经网络执行特征提取时，每一层计算的特征表达视觉信息的不同抽象。较早的层提取高度紧凑的低级特征，如带通和方向基元，而更深层的提取结构特征越来越抽象，类似于轮廓、形状和边缘，随着深度的增加变得不那么容易理解。我们提出了一种不同类型的端到端无参考 (NR) 图像质量评估 (IQA) 模型，该模型被定义为多深度输出卷积神经网络 (MoNET)。它通过将浅层和深层特征映射到感知质量来实现这一点。MoNET 提供表达浅（低级）和深（高级）特征的三个输出，并将它们映射到主观质量分数。多个输出组合成一个单一的最终质量分数。MoNET 通过结合三个学习机的响应来实现这一点，因此它可以被视为一种集成学习的形式。在三个公共图像质量数据库上的实验结果表明，我们提出的模型比其他最先进的 NR IQA 算法实现了更好的性能。