360-degree　images/videos　have　been　dramatically　increasing　in　recent　years.　But　the　high　resolution　makes　it　difficult　to　be　transported,　compressed　and　stored,　and　thus　constrains　the　development　of　360-degree　images/videos.　Therefore,　it　is　important　to　study　how　popular　coding　technologies　influence　the　quality　of　360-degree　images.　In　this　paper,　we　present　a　study　on　subjective　assessment　of　compressed　360-degree　images　and　investigate　whether　existing　objective　image　quality　assessment　(IQA)　methods　can　effectively　evaluate　the　quality　of　compressed　360-degree　images.　We　first　construct　the　largest　compressed　360-degree　image　database　(CVIQD2018)　including　16　source　images　and　528　compressed　ones　with　three　prevailing　coding　technologies.　Then,　we　implement　16　full　reference　(FR)　IQA　metrics,　which　include　10　traditional　IQA　metrics　for　2D　images　and　3　PSNR-based　metrics　for　360-degree　images,　as　well　as　5　no　reference　(NR)　IQA　metrics　and　calculate　the　correlation　between　each　above　metric　and　subjective　assessment　in　terms　of　three　commonly　used　performance　indices.　The　experiment　results　reveal　structure　information,　visual　saliency　information　and　compensation　for　geometric　distortion　are　crucial　for　evaluating　the　quality　of　compressed　360-degree　images.