Considerable improvements in the technology and per-formance of SAT solvers has made their use possiblefor the resolution of various problems in artificial intel-ligence, and among them that of generating plans. Re-cently, promising Quantified Boolean Formula (QBF)solvers have been developed and we may expect that ina near future they become as efficient as SAT solvers.So, it is interesting to use QBF language that allows usto produce more compact encodings. We present in thisarticle a translation from STRIPS planning problemsinto quantified propositional formulas. We introducetwo new Compact Tree Encodings: CTE-EFA based onExplanatory frame axioms, and CTE-OPEN based oncausal links. Then we compare both of them to CTE-NOOP based on No-op Actions proposed in (Cashmore,Fox, and Giunchiglia 2012). In terms of execution timeover benchmark problems, CTE-EFA and CTE-OPENalways performed better than CTE-NOOP.