视觉特征深度融合的图像质量评价

针对当前视觉感知特性研究和图像特征评价算法的不足，通过构建视觉多通道神经网络融合预测模型，提出一种视觉特征深度融合的图像质量评价方法. 首先，结合人类视觉系统特性设计直方图统计和奇异值分解2个互补视觉评价算法，进一步对图像各视觉通道的稀疏化梯度信息进行深度处理. 其次，构建BP神经网络融合模型，对各层视觉特征的多通道评价融合分别进行预测. 最后，对3层视觉特征评价从内层到外层逐层地进行深度自适应融合. 实验结果表明，所构建的融合模型有效提高了各种评价算法的指标水平，所提方法优于已有方法.

Image quality assessment with deep pooling of visual feature

The neural network pooling prediction model of visual multi-channel was constructed and an image quality assessment method based on deep pooling of visual feature was proposed, aiming at the shortcoming of current research on visual perception characteristic and image feature assessment algorithms. Firstly, two complementary visual assessment algorithms based on histogram statistics and singular value decomposition were designed with human visual system characteristics. Further, the sparse gradient information of every visual channel for one image was deeply processed. Secondly, the multi-channel assessment pooling of every visual feature layer was predicted, respectively, by constructing a pooling model of the BP neural network. Finally, the visual feature assessment of three layers was deep adaptively pooled from the inner layer to the outer layer. The experiment results show that the constructed pooling model effectively improves the index level of every assessment algorithm, and the proposed method achieves great advantage compared to the existing methods.