Automatically　evaluating　the　quality　of　image　captions　can　be　very　challenging　since　human　language　is　quite　flexible　that　there　can　be　various　expressions　for　the　same　meaning.　Most　current　captioning　metrics　rely　on　token-level　matching　between　candidate　caption　and　the　ground　truth　label　sentences.　It　usually　neglects　the　sentence-level　information.　Motivated　by　the　auto-encoder　mechanism　and　contrastive　representation　learning　advances,　we　propose　a　learning-based　metric　I2CE　(Intrinsic　Image　Captioning　Evaluation).　For　learning　the　evaluation　metric,　we　develop　three　progressive　model　structures　capturing　the　sentence　level　representations-single　branch　model,　dual　branches　model,　and　triple　branches　model.　For　evaluation　of　the　proposed　metric,　we　select　one　automatic　captioning　model　and　collect　human　scores　on　the　quality　of　the　generated　captions.　We　introduce　a　statistical　test　on　the　correlation　between　human　scores　and　metric　scores.　Our　proposed　metric　I2CE　achieves　the　Spearman　correlation　value　of　51.42,　which　is　better　than　the　score　of　41.95　achieved　by　one　recently　proposed　BERT-based　metric.　The　result　is　also　better　than　the　conventional　rule-based　metrics.　Extensive　results　on　the　Composite-coco　dataset　and　PASCAL-50S　also　validate　the　effectiveness　of　our　proposed　metric.　The　proposed　metric　could　serve　as　a　novel　indicator　of　the　intrinsic　information　between　captions,　which　complements　the　existing　ones.　(C)　2022　Published　by　Elsevier　Inc.