Abstract: 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.

Key words: saliency image quality evaluation, color contrast, siamese neural network, semantic extraction