Abstract
Image quality assessment is a very important component in image processing. A good image quality assessment method provides designers a criterion for comparing performance. Every designer directs toward the target of increasing the score produced by this method. In addition, this quality assessment criteria can facilitate designers to propose a suitable strategy for improving their methods. Image quality assessment methods are designed specially according to task goals. However, lots of methods are simple and human adjusted, they are hard to solve complicated quality assessing problems which are closer to the human visual system. Recently, many subjective quality assessment methods based on deep neural network have been proposed, which need large amounts of labeling data, and these methods has gotten significant improvement. Nevertheless, there have some cases difficult to label the subjective quality score directly (like: Image Retargeting), which cannot be learned by existing method due to the obstacle of collecting training data. In this thesis, we propose a pairwise training model for image subjective quality assessment. Our architecture is based on Siamese network, which can make our model training on paired-comparison data. This architecture lets us solve the problem on image retargeting and easily extends to similar tasks. On general subjective quality assessment database, our method can also alleviate the difficulty of data collections. Experiment shows that our proposed method produces better results compared to previous works on the retargeted image case and comparative results in the general quality assessment database, even under the limitation of labeling information.