Paper Review: Blind Image Quality Assessment Based on High Order Statistics Aggregation (논문 리뷰)

BIQA — HOSA

It is known that the contrast normalization by blocks of the image is useful for constructing codebooks in the BIQA domain.

This paper represents how to construct codebooks using high order statistics. The basic feature set is obtained by the contrast normalization. Then, using the features from the CSIQ database, by adopting K-means clustering, we can retain codebooks with 100 centers. In the run-time, the method proposes high order features; mean, variance, and skewness. The input features are used to get distances to K clusters, and distances are the mean, variance, and skewness distances. Then the method performs regression over three distances.

In addition to state-of-the-art performance, one of the two biggest advantages of the proposed method is that it generalizes very well. It is quite common that the deep-learning-based method in IQA shows poor generalization performance. Unlike others, the proposed method is trained on one dataset and works very well on the other datasets.

The other one is that it is very fast compared to other methods. It is not common that BIQA performs within 1 second using CPU to process 512by512 input, but the proposed method performs in 0.35second, which is extremely fast.

In conclusion, HOSA is one of the best methods in terms of speed and cross-domain performance in the BIQA domain.