论文标题
深层多尺度功能学习用于扭曲的图像质量评估
Deep Multi-Scale Features Learning for Distorted Image Quality Assessment
论文作者
论文摘要
图像质量评估（IQA）旨在估计基于人类感知的图像视觉质量。尽管现有的深度神经网络（DNN）在解决IQA问题方面表现出了重要的有效性，但它仍然需要通过利用有效的多尺度功能来改善基于DNN的质量评估模型。在本文中，由人类视觉系统（HVS）组合多尺度特征以供感知的动力，我们建议使用金字塔功能学习学习具有层次多尺度功能的DNN，以进行扭曲的图像质量预测。我们的模型基于亮度域中的残差图和扭曲的图像，其中提出的网络包含空间金字塔池并具有网络结构中的金字塔。我们提出的网络以深刻的端到端监督方式进行了优化。为了验证所提出的方法的有效性，在四个广泛使用的图像质量评估数据库上进行了广泛的实验，证明了我们的算法的优势。
Image quality assessment (IQA) aims to estimate human perception based image visual quality. Although existing deep neural networks (DNNs) have shown significant effectiveness for tackling the IQA problem, it still needs to improve the DNN-based quality assessment models by exploiting efficient multi-scale features. In this paper, motivated by the human visual system (HVS) combining multi-scale features for perception, we propose to use pyramid features learning to build a DNN with hierarchical multi-scale features for distorted image quality prediction. Our model is based on both residual maps and distorted images in luminance domain, where the proposed network contains spatial pyramid pooling and feature pyramid from the network structure. Our proposed network is optimized in a deep end-to-end supervision manner. To validate the effectiveness of the proposed method, extensive experiments are conducted on four widely-used image quality assessment databases, demonstrating the superiority of our algorithm.
