Local Feature Aggregation for Blind Image Quality Assessment

Previous feature learning based blind image quality assessment (BIQA) methods invariably require large codebook or codebook updating procedure to obtain satisfying performance. In this paper, we propose a novel general purpose BIQA method, local feature aggregation (LFA) model, which requires only a much smaller codebook without the need for codebook updating. The proposed model consists of three steps. Firstly, normalized local raw image patches are extracted as local features through a regular grid and a 100 codeword codebook is constructed by K-means clustering. Secondly, the soft weighted differences between local features and codewords are aggregated to the global quality aware representation. Finally, support vector regression (SVR) is utilized to learn the mapping between features and subjective opinion scores. The proposed method is evaluated on two large image databases and achieves comparable performance to state-of-the-art BIQA methods.