No reference image quality metric via distortion identification and multi-channel label transfer
In this paper, we propose a no reference image quality assessment (NR-IQA) algorithm based on distortion identification (DI) and multi-channel label transfer (LT). First, the distortion type classification is used to obtain the query image's probabilities of belonging to each distortion type. Then, the distortion specific label transfer is implemented in multiple distortion category channels. Based on the hypothesis that the similar images share the similar subjective qualities, the label transfer predicts the subjective quality of the query image by pooling the labels of its k-nearest neighbors (KNN) retrieved from the annotated samples. A weighting average of the multi-channel label transfer's outputs is computed to obtain the final perceptual quality score. The weight is the query image's probability that belongs to the corresponding distortion type. The experimental results show that the proposed method outperforms representative NR-IQA approaches and some full-reference metrics.