Due　to　the　rapid　development　of　deep　learning　techniques,　no-reference　image　quality　assessment　(NR-IQA)　has　achieved　significant　improvement.　NR-IQA　aims　to　predict　a　real-valued　variable　for　image　quality,　using　the　image　in　question　as　the　sole　input.　Existing　deep　learning-based　NR-IQA　models　are　formulated　as　a　regression　problem　and　trained　by　minimising　the　mean　squared　error.　The　error　measurement　does　not　consider　the　relative　ordering　between　different　ratings　on　the　quality　scale,　which　consequently　affects　the　efficacy　of　the　model.　To　account　for　this　problem,　we　reformulate　NR-IQA　learning　as　an　ordinal　regression　problem　and　propose　a　simple　yet　effective　framework　using　deep　convolutional　neural　networks　(DCNN)　and　Transformers.　NR-IQA　learning　is　achieved　by　a　deep　ordinal　loss　and　using　a　soft　ordinal　inference　to　transform　the　predicted　probabilities　to　a　continuous　variable　for　image　quality.　Experimental　results　demonstrate　the　superiority　of　our　proposed　NR-IQA　model　based　on　deep　ordinal　regression.　In　addition,　this　framework　can　be　easily　extended　with　various　DCNN　architectures　to　build　advanced　IQA　models.