  Compressed image quality assessment plays an important role in image
services, especially in image compression applications, which can be utilized
as a guidance to optimize image processing algorithms. In this paper, we
propose an objective image quality assessment algorithm to measure the quality
of compressed images. The proposed method utilizes a data-driven transform,
Saak (Subspace approximation with augmented kernels), to decompose images into
hierarchical structural feature space. We measure the distortions of Saak
features and accumulate these distortions according to the feature importance
to human visual system. Compared with the state-of-the-art image quality
assessment methods on widely utilized datasets, the proposed method correlates
better with the subjective results. In addition, the proposed methods achieves
more robust results on different datasets.
