Blind image quality assessment (BIQA) methods based on visual codebooks have received much attention due to its prominent generalization capacity across different image domains. Existing codebook-based BIQA methods depend on large-size codebooks and high-dimensional features, which are memory-consuming and have the risk of over-fitting. Thus, it is necessary to design quality metrics with much smaller codebooks. This paper presents a novel multistage feature encoding (MSFE)-based BIQA method which requires much lower dimensional features while preserving comparable or even better performance. To specify, MSFE is performed over multiple cascaded and much smaller sub-codebooks to generate more compact and discriminative features for quality prediction. The latter stage takes the encoding residuals in the former stage as input. We use KSVD and sparse coding for codebook training and feature encoding in the framework, respectively. Finally, the generated sparse feature codes in all stages are combined and aggregated over the entire image for quality prediction via support vector regression (SVR). We evaluate the proposed method on several natural and screen content image databases. The experimental results confirm its superiority in terms of both validity and universality.