An Improved BRISQUE Algorithm Based on Non-zero Mean Generalized Gaussian Model and Global Structural Correlation Coefficients

To solve the problem of the limited description ability of the quality-aware features used in blind/referenceless image spatial quality evaluator(BRISQUE)algorithm and enhance the accuracy and robustness of the BRISQUE algorithm,an improved BRISUQE(IBRISQUE)algorithm was proposed in this paper.First,we adopted non-zero mean symmetric generalized Gaussian distribution(GGD)model to obtain the quality-aware features from mean subtracted and contrast normalized(MSCN)coefficients.Then,we used non-zero mean asymmetric generalized Gaussian distribution(AGGD)to extract the quality-aware features that could represent the local structural distortions from the neighboring MSCN coefficients along four orientations.Finally,we utilized Pearson linear correlation coefficients(PLCC)of neighboring MSCN coefficients from horizontal,vertical,main-diagonal,and secondary-diagonal directions as the quality-aware features reflecting global structural distortions.The IBRISQUE algorithm was tested on the LIVE and TID2013 benchmark databases.Comparing with state-of-the-art image quality assessment algorithms,IBRISQUE algorithm achieves higher prediction accuracy while the efficiency maintains a roughly equal level compared with the original BRISQUE algorithm.The proposed algorithm that strikes a favorable balance between accuracy and complexity outperforms other competing algorithms significantly.