Accurately　predicting　the　quality　of　depth-image-based-rendering　(DIBR)　synthesized　images　is　of　great　significance　in　promoting　DIBR　techniques.　Recently,　Many　DIBR-synthesized　image　quality　assessment　(IQA)　algorithms　based　on　quantifying　the　distortion　existed　in　texture　images　have　been　proposed.　However,　these　methods　ignore　the　damage　of　DIBR　algorithms　on　the　depth　structure　of　DIBR-synthesized　images　and　thus　fail　to　evaluate　the　visual　quality　of　DIBR-synthesized　images　accurately.　To　this　end,　we　present　a　DIBR-synthesized　image　quality　assessment　metric　with　multi-modal　information　(i.e.　integrating　texture　and　depth　information),　dubbed　as　MMI.　MMI　predicts　the　quality　of　DIBR-synthesized　images　by　jointly　measuring　the　texture　structure　and　depth　structure　of　the　synthesized　image.　The　design　principle　of　our　MMI　is　that　the　local　geometric　distortion,　introduced　by　DIBR　techniques　in　the　hole-filling　process,　destroys　the　texture　structure　and　the　depth　structure　of　DIBR-synthesized　images.　Thus,　we　can　accurately　evaluate　DIBR-synthesized　image　quality　by　a　joint　representation　of　texture　structure　and　depth　structure.　Experiments　show　that　our　MMI　is　better　than　the　competing　state-of-the-art　IQA　algorithms　in　predicting　DIBR-synthesized　image　quality.　©　2021　IEEE