In　this　paper,　we　present　a　multi-channel　convolution　neural　network　(CNN)　for　blind　360-degree　image　quality　assessment　(MC360IQA).　To　be　consistent　with　the　visual　content　of　360-degree　images　seen　in　the　VR　device,　our　model　adopts　the　viewport　images　as　the　input.　Specifically,　we　project　each　360-degree　image　into　six　viewport　images　to　cover　omnidirectional　visual　content.　By　rotating　the　longitude　of　the　front　view,　we　can　project　one　omnidirectional　image　onto　lots　of　different　groups　of　viewport　images,　which　is　an　efficient　way　to　avoid　overfitting.　MC360IQA　consists　of　two　parts,　multi-channel　CNN　and　image　quality　regressor.　Multi-channel　CNN　includes　six　parallel　ResNet34　networks,　which　are　used　to　extract　the　features　of　the　corresponding　six　viewport　images.　Image　quality　regressor　fuses　the　features　and　regresses　them　to　final　scores.　The　results　show　that　our　model　achieves　the　best　performance　among　the　state-of-art　full-reference　(FR)　and　no-reference　(NR)　image　quality　assessment　(IQA)　models　on　the　available　360-degree　IQA　database.　©　2019　IEEE