The　projection　matrix　in　compressive　light　field　acquisition　has　specific　structural　features　that　can　be　utilized　for　more　efficient　compressive　sensing.　In　this　paper,　we　propose　a　new　mask　design　scheme　for　projection　optimization　in　light　field　compressing　with　a　novel　circulant　matrix　structure.　A　learning　circulant　kernel　algorithm　is　developed　and　applied　to　compute　the　projection　matrix　optimization　from　a　known　dictionary　of　the　compressed　light　field.　Experimental　results　show　that　the　new　approach　can　improve　the　reconstruction　quality　of　the　light　field　significantly　compared　with　the　commonly　used　I.I.D　Guass　random　mask.