Image　interpolation　is　a　key　technique　of　image　super-resolution.　Four　two　dimensional　(2-D)　autoregressive　(AR)　modeling-based　image　interpolation　algorithms　have　been　reported　to　have　better　performance　in　edge　and　texture　preservation　than　conventional　image　polynomial　interpolation　algorithms.　However,　there　is　lack　of　performance　comparison　among　them.　For　super-resolution　reconstruction　quality,　this　paper　is　going　to　fill　up　the　gap　by　a　comparison　study　on　the　four　2-D　AR　modeling-based　interpolation　methods:　novel　edge-directed　interpolation　(NEDI),　soft-decision　adaptive　interpolation　(SAI),　sparse　representation　interpolation　with　nonlocal　autoregressive　modeling　(SR-NARM),　and　adaptive　super-pixel-guided　AR　modeling　(ASARM).　Furthermore,　the　four　interpolation　algorithms　are　compared　in　the　light　of　peak　signal　to　noise　ratio,　feature　similarity　index,　mean　squared　error　and　structural　similarity　index.　From　comparative　results　we　observe　that　ASARM　method　has　relatively　better　performance　than　other　three　methods　but　is　more　time-consuming　than　the　NEDI　and　SAI　methods.