基于孪生网络的全参考彩色图像质量评价算法

Full-Reference Color Image Quality Assessment Algorithm Based on Siamese Network

针对彩色图像质量评价问题,提出了一种基于孪生网络的全参考图像质量评价算法。在特征提取阶段,将双卷积结构作为特征提取单元,分别提取参考图像块和失真图像块的不同维度特征。通过卷积和池化操作,充分融合提取的图像特征,然后通过两级全连接层和平均池化,得到最终的质量评价分数。分别在LIVE和TID2013数据集上展开训练和测试,并交叉进行跨库测试,均取得了较好的实验结果。实验表明,本文算法对彩色图像质量的评价表现出良好的主客观评价一致性,体现了优越的质量评价性能和良好的泛化能力。另外,本文算法结构简单,参数计算量少,能够实现端到端训练,有望作为反馈网络用于图像去噪等场合。

Aiming at the solution of color image quality assessment,this paper proposes a full-reference color image quality assessment algorithm based on siamese network.We adopt the double convolution block as the feature extraction unit.The double convolution block extracts features on different dimensions of the reference image patch and the distorted image patch.Then,we fuse the extracted image features via the convolution and the pooling operations.After that,the final quality score is obtained through two levels of the fully connected layers and the average pooling layer.We performed training-testing experiments of the algorithm on the LIVE and the TID2013 datasets.Furthermore,the cross-database evaluation between the two databases is also conducted.Both achieved prominent results.The experiments show that the proposed algorithm has a good subjective and objective evaluation consistency for the evaluation of color image quality,indicating that the algorithm has exceptional performance and generalization ability.In addition,the algorithm has a simple architecture and fewer parameters.Since the network can be trained end-to-end,it is promising to exploit further usage other than image quality assessment.For an instance,the network can be leveraged as a feedback network for extended applications such as image denoising.