Underwater　sonar　imaging　is　a　key　technology　in　modern　ocean　exploration　and　maritime　defense.　However,　distortions　often　occur　during　transmission　or　compression　due　to　the　harsh　underwater　communication　environment.　These　distortions　can　severely　degrade　image　quality.　Therefore,　implementing　Sonar　Image　Quality　Assessment　(SIQA)　of　transmitted　images　is　crucial　to　ensure　their　reliable　performance　in　downstream　tasks.　In　practice,　collecting　large-scale　annotated　sonar　datasets　is　both　costly　and　challenging,　and　high-quality　reference　images　are　often　unavailable.　Therefore,　SIQA　methods　must　be　reference-free　and　independent　of　training　data.　To　address　this　challenge,　we　introduce　a　novel　zero-shot　SIQA　paradigm,　where　task-oriented　image　quality　is　defined　by　the　magnitude　of　semantic　shift　under　contour　degradation.　High-quality　images,　with　clearer　contours,　exhibit　larger　semantic　shifts　when　perturbed;　in　contrast,　low-quality　images　with　blurred　structures　show　smaller　shifts.　We　simulate　contour　degradation　by　filtering　the　mid-frequency　components　and　quantify　the　resulting　semantic　shift　using　cosine　similarity.　This　approach　bypasses　conventional　feature　engineering　and　regression-based　modeling,　directly　using　the　measured　semantic　shift　as　the　quality　score,　thereby　enabling　zero-shot　evaluation.　Experiments　on　sonar　datasets　show　that　our　method　outperforms　existing　zero-shot　IQA　baselines　and　demonstrates　strong　potential　for　practical　application.