基于并行小规模卷积神经网络的图像质量评价

图像质量客观评价广泛应用在图像处理任务中，参考深度学习技术的研究成果，提出了一种基于并行小规模卷积神经网络的无参考图像质量评估算法。卷积操作和并行的多尺度输入能学习到丰富和细微的图像失真特征，首先利用高斯图像金字塔获取不同尺度的失真图像做为4路小规模单层卷积神经网络的输入，经过卷积和池化处理后，输出4路特征矢量，把学习到的特征矢量融合后，通过全连接回归映射为图像质量预测分数。参数优化分2个阶段完成，提高了模型精度。实验测试结果表明，设计的网络模型简单有效，提出的算法性能高于当前主流算法，具有很好的稳定性和较强的泛化能力。 

Image quality evaluation based on parallel small CNN

Objective evaluation of image quality is widely used in many image processing tasks. A non-reference image quality evaluation algorithm is proposed based on small parallel-mode convolutional neural networks under deep learning technology. Convolution operation and parallel multi-scale input could learn not only rich feature, but also subtle feature. Firstly, the Gaussian image pyramid is used to obtain different scale distorted images as the input of 4 small-scale single-layer convolutional neural networks. After convolution and pooling, 4 feature vectors are output, and the learned feature vectors are merged and then mapped into image quality prediction scores through fully connected regression. Para- meters are optimized through two serial stages to improve the accuracy of the model. Experimental test- ing results show that the designed small network model is effective, and the proposed algorithm has higher performance than the current comparative algorithms and has good stability and strong generalization ability.