基于人眼色彩差异化感知的图像质量评价研究

Research on image quality evaluation based on color differential perception of human eye

针对显著性图像质量评价问题,参考人眼视觉对图像色彩的差异化感知,提出一种基于孪生神经网络对图像色彩对比显著区域进行质量评价的算法。首先,根据图像中的色彩对比和语义信息分别提取原始图像和失真图像中的色彩对比显著区域;然后,将原始图像和失真图像中对应的色彩区域作为子图像,以样本对的形式输入孪生神经网络;最后,计算主客观图像质量评估值的相关性。实验采用残差结构的Inception-ResNet-V2网络作为基础模型,同时增加EMD损失函数优化对图像质量的距离损失,经过Softmax层后输出图像质量评估值,并在TID2013数据集上进行了测试。结果表明,提出的算法在该数据集上性能良好。

Aiming at the problem of saliency image quality evaluation,referring to the differential perception of image color by human vision,an algorithm based on siamese neural network for quality evaluation of image color contrast salient regions is proposed.Firstly,according to the color contrast and its semantic information of the image,the significant regions of color contrast are extracted respectively.Secondly,the extracted color regions are inputted into the siamese neural network as sub-images in the form of sample pairs.Finally,the correlation between the subjective and objective image quality evaluation values is calculated.In the experiment,the Inception-ResNet-V2 network with residual structure is used as the basic model.The EMD loss function is introduced to optimize the distance loss.After being filtered through the Softmax layer,the image quality evaluation value is obtained.Tests are carried out on the TID2013 dataset and the test results show that the proposed algorithm has good performance on this dataset.