Traditional　blind　image　quality　assessment　(IQA)　measures　generally　predict　quality　from　a　sole　distorted　image　directly.　In　this　paper,　we　first　introduce　multiple　pseudo　reference　images　(MPRIs)　by　further　degrading　the　distorted　image　in　several　ways　and　to　certain　degrees,　and　then　compare　the　similarities　between　the　distorted　image　and　the　MPRIs.　Via　such　distortion　aggravation,　we　can　have　some　references　to　compare　with,　i.e.,　the　MPRIs,　and　utilize　the　full-reference　IQA　framework　to　compute　the　quality.　Specifically,　we　apply　four　types　and　five　levels　of　distortion　aggravation　to　deal　with　the　commonly　encountered　distortions.　Local　binary　pattern　features　are　extracted　to　describe　the　similarities　between　the　distorted　image　and　the　MPRIs.　The　similarity　scores　are　then　utilized　to　estimate　the　overall　quality.　More　similar　to　a　specific　pseudo　reference　image　(PRI)　indicates　closer　quality　to　this　PRI.　Owning　to　the　availability　of　the　created　multiple　PRIs,　we　can　reduce　the　influence　of　image　content,　and　infer　the　image　quality　more　accurately　and　consistently.　Validation　is　conducted　on　four　mainstream　natural　scene　image　and　screen　content　image　quality　assessment　databases,　and　the　proposed　method　is　comparable　to　or　outperforms　the　state-of-the-art　blind　IQA　measures.　The　MATLAB　source　code　of　the　proposed　measure　will　be　publicly　available.