A Multi-Scale Learning Local Phase and Amplitude Blind Image Quality Assessment for Multiply Distorted Images

In the real application, digital images may undergo the process of acquisition, compression and transmission, which causes the excess blurring, quantization and noise. However, the metrics of image quality assessment (IQA) for multiply distorted images are very limited. In this paper, we propose a new multi-scale learning quality-aware features (MS-LQAF) blind image quality assessment algorithm for multiply distorted images, by using both local phase and local amplitude. In the new model, a distorted image is decomposed into 3 scales by Gabor transform, and its phase congruency image (PCI), phase congruency covariance maximum image (PCCmax) and phase congruency covariance minimum image (PCCmin) are produced. Then we calculate contrast sensitivity function and gray level-gradient co-occurrence matrix features from distorted image and its PCI, PCCmax and PCCmin, and mean value of intensity of PCI, PCCmax and PCCmin, and overlapping blocked local amplitude of distorted image. At last SVR is used to build the approximating function between these features and subjective mean opinion scores. Both local phase and local amplitude features are extracted at multi-scale images, which supply more flexibility than previous single-scale methods in incorporating the variations of viewing scene. Comparative experiments between our proposed metric and state-of-the-art full-reference and no-reference metrics are conducted on two newly released multiply distorted image databases (LIVEMD, MDID2013), which demonstrate the effectiveness of our proposed method.

Li C. (2018) A Multi-Scale Learning Local Phase and Amplitude Blind Image Quality Assessment for Multiply Distorted Images. IEEE Access.