Compressing　videos　while　maintaining　an　acceptable　level　of　Quality　of　Experience　(QoE)　is　indispensable.　To　this　aim,　a　feasible　method　is　to　further　increase　the　Quantization　Parameter　(QP)　of　video　stream　to　eliminate　visual　redundancy,　simultaneously　utilizing　perceptual　characteristics　of　Human　Visual　System　(HVS)　to　impose　a　threshold　constraint　on　the　maximum　QP.　In　this　paper,　we　employ　Just　Noticeable　Distortion　(JND)　to　characterize　the　aforementioned　threshold　constraint,　thereby　avoiding　perceptual　loss　during　QP　refinement　process.　We　propose　an　effective　JND-based　algorithm　for　QP　optimization,　in　which　a　video　saliency　detection　is　introduced　to　extract　regions　of　interest,　a　refinement　model　based　on　a　lightweight　network　is　designed　to　predict　QP　value　and　an　ensemble　learning　method　to　improve　generalization　performance.　Theoretical　analysis　and　experimental　results　demonstrate　that　the　proposed　algorithm　has　been　successfully　applied　to　Versatile　Video　Coding　(VVC)　to　achieve　significant　bitrate　reduction　without　sacrificing　perceived　quality.