Visual　saliency　detection　is　useful　in　carrying　out　image　compression,　image　segmentation,　image　retrieval,　and　other　image　processing　applications.　Majority　of　existing　saliency　detection　algorithms　are　presented　for　distortion-free　images.　However,　this　situation　is　not　always　the　case.　In　this　paper,　we　first　evaluate　the　performances　of　state-of-the-art　saliency　detection　algorithms　against　different　distortion　types　and　levels.　A　machine　learning-based　framework　for　saliency　detection　is　proposed　for　two　common　types　of　distortions,　noise　and　JPEG　compression.　First,　a　machine　learning　method　is　proposed　to　predict　the　distortion　level,　and　then　the　distortion　is　removed　using　the　parameter　setting　that　is　tuned　for　that　distortion　level.　Finally,　the　saliency　map　is　calculated　by　using　saliency　detection　algorithms.　We　evaluate　the　saliency　detection　algorithms　on　Tampere　Image　Database　(TID2013),　which　is　proposed　for　image　quality　assessment　application.　We　manually　label　the　salient　objects　in　each　image　and　obtain　its　ground　truth　saliency　map　in　order　to　adapt　TID2013　for　visual　saliency　detection　application.　Experimental　results　demonstrate　that　the　distortions　usually　decrease　the　performances　of　the　saliency　detection　algorithms,　particularly　in　high　levels　of　distortions.　The　performance　rankings　of　the　saliency　detection　algorithms　for　the　distortion-free　images　and　distorted　images　are　different.　Moreover,　our　proposed　machine　learning-based　framework　for　saliency　detection　improves　the　performances　of　saliency　detection　algorithms　in　distorted　images　in　most　of　the　distortion　levels,　particularly　in　high　levels　of　distortions.