Electrophoretic　Display　(EPD)　is　a　reflective　technology　that　closely　mimics　traditional　paper,　making　it　a　popular　choice　in　E-readers,　IoT　devices,　and　wearables.　However,　color　quantization,　which　is　a　critical　step　to　display　natural　images　on　EPD　with　reduced　color　scales,　usually　leads　to　grayscale　distortion　and　edge　loss.　In　this　paper,　we　propose　a　Dynamic-Clustering-based　E-paper　Color　Quantization　(DCECQ)　method　to　address　the　above　issue.　First,　it　employs　a　dynamically　adjustable　Particle　Swarm　Optimization　(PSO)　clustering,　facilitating　adaptive　threshold　optimization　for　diverse　image　content.　Second,　it　introduces　a　Human　Visual　System　(HVS)　based　model　to　quantify　visual　errors　and　compensates　for　grayscale　ghosting,　effectively　reducing　artifacts　such　as　edge　blurring　and　color　distortion.　Third,　it　implements　a　validation　platform　for　EPD　to　assess　performance　under　real-world　conditions.　Experimental　results　demonstrate　that　our　approach　outperforms　existing　methods　across　multiple　metrics,　which　attests　to　its　effectiveness　and　practical　applicability.　Source　code　will　be　made　publicly　available　after　the　peer　review　process.　　©　2025　IEEE.