State-of-the-art　algorithms　for　blind　image　quality　assessment　(BIQA)　typically　have　two　categories.　The　first　category　approaches　extract　natural　scene　statistics　(NSS)　as　features　based　on　the　statistical　regularity　of　natural　images.　The　second　category　approaches　extract　features　by　feature　encoding　with　respect　to　a　learned　codebook.　However,　several　problems　need　to　he　addressed　in　existing　codebook-based　BIQA　methods.　First,　the　high-dimensional　codebook-based　features　are　memory-consuming　and　have　the　risk　of　over-fitting.　Second,　there　is　a　semantic　gap　between　the　constructed　codebook　by　unsupervised　learning　and　image　quality.　To　address　these　problems,　we　propose　a　novel　codebook-based　BIQA　method　by　optimizing　multistage　discriminative　dictionaries　(MSDDs).　To　be　specific,　MSDDs　are　learned　by　performing　the　label　consistent　K-SVD　(LC-KSVD)　algorithm　in　a　stage-by-stage　manner.　For　each　stage,　a　new　quality　consistency　constraint　called　＂quality-discriminative　regularization＂　term　is　introduced　and　incorporated　into　the　reconstruction　error　term　to　form　a　unified　objective　function,　which　can　be　effectively　solved　by　LC-KSVD　for　discriminative　dictionary　learning.　Then,　the　latter　stage　takes　the　reconstruction　residual　data　in　the　former　stage　as　input　based　on　which　LC-KSVD　is　repeatedly　performed　until　the　final　stage　is　reached.　Once　the　MSDDs　are　learned,　multistage　feature　encoding　is　performed　to　extract　feature　codes.　Finally,　the　feature　codes　are　concatenated　across　all　stages　and　aggregated　over　the　entire　image　for　quality　prediction　via　regression.　The　proposed　method　has　been　evaluated　on　five　databases　and　experimental　results　well　confirm　its　superiority　over　existing　relevant　BIQA　methods.