Sonar　imagery　plays　a　significant　role　in　oceanic　applications　since　there　is　little　natural　light　underwater,　and　light　is　irrelevant　to　sonar　imaging.　Sonar　images　are　very　likely　to　be　affected　by　various　distortions　during　the　process　of　transmis.sion　via　the　underwater　acoustic　channel　for　further　analysis.　At　the　receiving　end,　the　reference　image　is　unavailable　clue　to　the　complex　and　changing　underwater　environment　and　our　unfamiliarity　with　it.　To　the　best　of　our　knowledge,　one　of　the　important　usages　of　sonar　images　is　target　recognition　on　the　basis　of　contour　information.　The　contour　degradation　degree　for　a　sonar　image　is　relevant　to　the　distortions　contained　in　it.　To　this　end,　we　developed　a　new　no-reference　contour　degradation　measurement　for　perceiving　the　quality　of　sonar　images.　The　sparsities　of　a　series　of　transform　coefficient　matrices,　which　are　descriptive　of　contour　information,　are　first　extracted　as　features　from　the　frequency　and　spatial　domains.　The　contour　degradation　degree　for　a　sonar　image　is　then　measured　by　calculating　the　ratios　of　extracted　features　before　and　after　filtering　this　sonar　image.　Finally,　a　bootstrap　aggregating　(bagging)-based　support　vector　regression　module　is　learned　to　capture　the　relationship　between　the　contour　degradation　degree　and　the　sonar　image　quality.　The　results　of　experiments　validate　that　the　proposed　metric　is　competitive　with　the　state-of-theart　reference-based　quality　metrics　and　outperforms　the　latest　reference-free　competitors.