Traditional　full-reference　image　quality　assessment　(IQA)　metrics　generally　predict　the　quality　of　the　distorted　image　by　measuring　its　deviation　from　a　perfect　quality　image　called　reference　image.　When　the　reference　image　is　not　fully　available,　the　reduced-reference　and　no-reference　IQA　metrics　may　still　be　able　to　derive　some　characteristics　of　the　perfect　quality　images,　and　then　measure　the　distorted　image＇s　deviation　from　these　characteristics.　In　this　paper,　contrary　to　the　conventional　IQA　metrics,　we　utilize　a　new　＂reference＂　called　pseudo-reference　image　(PRI)　and　a　PRI-based　blind　IQA　(BIQA)　framework.　Different　from　a　traditional　reference　image,　which　is　assumed　to　have　a　perfect　quality,　PRI　is　generated　from　the　distorted　image　and　is　assumed　to　suffer　from　the　severest　distortion　for　a　given　application.　Based　on　the　PRI-based　BIQA　framework,　we　develop　distortion-specific　metrics　to　estimate　blockiness,　sharpness,　and　noisiness.　The　PRI-based　metrics　calculate　the　similarity　between　the　distorted　image＇s　and　the　PRI＇s　structures.　An　image　suffering　from　severer　distortion　has　a　higher　degree　of　similarity　with　the　corresponding　PRI.　Through　a　two-stage　quality　regression　after　a　distortion　identification　framework,　we　then　integrate　the　PRI-based　distortion-specific　metrics　into　a　general-purpose　BIQA　method　named　blind　PRI-based　(BPRI)　metric.　The　BPRI　metric　is　opinion-unaware　(OU)　and　almost　training-free　except　for　the　distortion　identification　process.　Comparative　studies　on　five　large　IQA　databases　show　that　the　proposed　BPRI　model　is　comparable　to　the　state-of-the-art　opinion-aware-　and　OU-BIQA　models.　Furthermore,　BPRI　not　only　performs　well　on　natural　scene　images,　but　also　is　applicable　to　screen　content　images.　The　MATLAB　source　code　of　BPRI　and　other　PRI-based　distortion-specific　metrics　will　be　publicly　available.