Image quality assessment is of fundamental importance for various image processing applications. A novel method is presented in which the joint occurrences of statistical local representation by log‐Gabor filters and texture analysis by local tetra patterns and histograms of colour are considered as quality‐aware features. Then the dissimilarities of these features between the distorted and reference images are quantified and mapped into quality score prediction by utilising a support vector regression. Extensive experiments on LIVE, CSIQ and TID databases show that the proposed method is remarkably consistent with human perception and outperforms many state‐of‐the‐art methods, and also it is robust across different distortion types and different databases.