In　this　paper,　we　propose　an　improved　DCT-based　just　noticeable　difference　(JND)　model　incorporating　the　spatial　contrast　sensitivity　function　(CSF),　the　luminance　adaptation　(LA)　effect　and　the　contrast　masking　(CM)　effect.　For　the　CM　model,　we　propose　a　novel　image　block　texture　classification　method　based　on　mean　value　and　variance　of　AC　coefficients　which　better　represent　the　texture　characteristics　than　conventional　model.　Experimental　results　confirm　that　the　proposed　model　yields　invisible　distortions　for　test　images　with　average　PSNR　of　28.24　dB,　which　is　almost　3　dB　lower　than　the　other　models　for　comparison.　It　is　proved　that　the　proposed　model　inject　more　redundancy　into　images　in　unperceived　way　and　thus　more　consistent　with　human　visual　features.