Contrast　Sensitivity　(CS),　Luminance　Adaptation　(LA)　and　Contrast　Masking　(CM)　are　important　contributing　factors　for　Just　Noticeable　Difference　(JND)　in　images.　Most　of　the　existing　pixel　domain　JND　algorithms　are　based　only　on　LA　and　CM.　Research　shows　that　the　human　vision　depends　significantly　on　CS,　and　an　underlying　assumption　in　the　existing　algorithms　is　that　CS　cannot　be　estimated　in　the　pixel　domain　JND　algorithms.　However,　in　the　case　of　natural　images,　this　assumption　is　not　true.　Studies　on　human　vision　suggest　that　CS　can　be　estimated　using　the　Root　Mean　Square　(RMS)　contrast　in　the　pixel　domain.　With　this　in　perspective,　we　propose　the　first　pixel-based　JND　algorithm　that　includes　a　very　important　component　of　the　human　vision,　namely　CS　by　measuring　RMS　contrast.　This　RMS　contrast　is　combined　with　LA　and　CM　to　form　a　comprehensive　pixel-domain　model　to　efficiently　estimate　JND　in　the　low　frequency　regions.　For　high　frequency　regions　(edge　and　texture),　a　feedback　mechanism　is　proposed　to　efficiently　alleviate　the　over-and　under-estimation　of　CM.　To　facilitate　this,　a　prediction　based　algorithm　is　used　to　classify　an　image　into　low　(smooth)　and　high　frequency　regions.　This　feed-back　mechanism　is　based　on　the　relationship　between　the　CS　and　RMS　contrast.　Experiments　validate　that　the　proposed　JND　algorithm　efficiently　matches　with　human　perception　and　produces　significantly　better　results　when　compared　to　existing　pixel　domain　JND　algorithms.　(c)　2017　Elsevier　B.V.　All　rights　reserved.