We propose an efficient blind/no-reference image quality assessment algorithm using a log-derivative statistical model of natural scenes. Our method, called DErivative Statistics-based QUality Evaluator (DESIQUE), extracts image quality-related statistical features at two image scales in both the spatial and frequency domains. In the spatial domain, normalized pixel values of an image are modeled in two ways: pointwise-based statistics for single pixel values and pairwise-based log-derivative statistics for the relationship of pixel pairs. In the frequency domain, log-Gabor filters are used to extract the fine scales of the image, which are also modeled by the log-derivative statistics. All of these statistics can be fitted by a generalized Gaussian distribution model, and the estimated parameters are fed into combined frameworks to estimate image quality. We train our models on the LIVE database by using optimized support vector machine learning. Experiment results tested on other databases show that the proposed algorithm not only yields a substantial improvement in predictive performance as compared to other state-of-the-art no-reference image quality assessment methods, but also maintains a high computational efficiency.