Super-Resolution　(SR)　algorithms　aim　to　enhance　the　resolutions　of　images.　Massive　deep-learning-based　SR　techniques　have　emerged　in　recent　years.　In　such　case,　a　visually　appealing　output　may　contain　additional　details　compared　with　its　reference　image.　Accordingly,　fully　referenced　Image　Quality　Assessment　(IQA)　cannot　work　well;　however,　reference　information　remains　essential　for　evaluating　the　qualities　of　SR　images.　This　poses　a　challenge　to　SR-IQA:　How　to　balance　the　referenced　and　no-reference　scores　for　user　perception?　In　this　paper,　we　propose　a　Perception-driven　Similarity-Clarity　Tradeoff　(PSCT)　model　for　SR-IQA.　Specifically,　we　investigate　this　problem　from　both　referenced　and　no-reference　perspectives,　and　design　two　deep-learning-based　modules　to　obtain　referenced　and　no-reference　scores.　We　present　a　theoretical　analysis　based　on　Human　Visual　System　(HVS)　properties　on　their　tradeoff　and　also　calculate　adaptive　weights　for　them.　Experimental　results　indicate　that　our　PSCT　model　is　superior　to　the　state-of-the-arts　on　SR-IQA.　In　addition,　the　proposed　PSCT　model　is　also　capable　of　evaluating　quality　scores　in　other　image　enhancement　scenarios,　such　as　deraining,　dehazing　and　underwater　image　enhancement.　The　source　code　is　available　at　https://github.com/kekezhang112/PSCT.