The algorithm is computationally attractive given the availability of platforms optimized for DCT computing. The approach is based on a simple Bayesian inference model to predict image quality scores given certain features extracted. The features are based on an NSS model of the DCT image coefficients. The model's estimated parameters are used to form features that are indicative of perceptual quality. These characteristics are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we call BLIINDS-II, requires minimal training and adopts a simple probabilistic model for punctuation prediction. Given the characteristics extracted from a test image, the quality score is chosen that maximizes the probability of the empirically determined inference model as the predicted quality index of that image. When tested in the LIVE IQA database, it is shown that BLIINDS-II correlates highly with human quality judgments, at a level that is competitive with the popular SSIM index.