Image　quality　assessment　of　authentically　distorted　images　constitutes　a　indispensable　part　of　numerous　computer　vision　tasks.　Despite　the　substantial　progress　in　recent　years,　accuracy　and　generalization　performance　is　still　unsatisfactory.　These　challenges　are　primarily　attributed　to　the　scarcity　of　labeled　images.　In　order　to　increase　the　amount　of　images　for　training,　we　use　semi-supervised　learning　to　combine　labeled　images　and　specifically　selected　unlabeled　images.　In　our　new　training　paradigm,　denominated　Selected　Data　Retrain　under　Regularization,　the　selection　criteria　of　unlabeled　images　is　based　on　the　supposition　that　an　image　and　a　certain　of　its　patches　ought　to　have　approximate　image　quality　scores.　Unlabeled　images　that　meets　the　aforementioned　criteria,　named　as　Highly　Credible　Unlabeled　Images,　mitigate　the　problem　of　scarcity,　thus,　improve　accuracy.　However　generalization　may　be　compromised　due　to　selection　procedure’s　reliance　on　labeled　images　and　presence　of　coherent　variance　existed　between　labeled　images　and　unlabeled　images.　Therefore　we　incorporate　a　sorting　loss　function　to　reduce　variation　within　the　new　dataset　of　labeled　images　and　specifically　selected　unlabeled　images,　and　thus　achieve　better　generalization.　The　effectiveness　of　our　proposed　paradigm　is　empirically　validated　using　public　datasets.　Codes　are　available　at　https://github.com/dvstter/SDRR_IQA.　©　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature　2024.