Traditional　hybrid　video　coding　framework　using　block　based　predictive　coding　and　transform　coding,　such　as　the　High　Efficiency　Video　Coding　(HEVC),　cannot　further　dig　out　the　redundancy　remained　in　quantized　transformed　residual,　causing　extra　bits　consumption.　Measured　by　rate-distortion　(RD)　performance,　the　problem　of　higher　bits　consuming　can　be　solved　reversely　by　video　quality　enhancing.　In　this　work,　we　proposed　a　video　coding　scheme　that　solve　the　problem　by　enhancing　the　reconstructed　video　quality　using　supplementary　information　from　further　compressed　quantization　error.　Aiming　at　better　R-D　performance　for　near-lossless　video　coding,　we　propose　a　novel　video　coding　scheme　using　a　two-stage　framework　that　extracts　quantization　error　as　complementary　information　which　is　compressed　using　dictionary　learning　and　sparse　representation.　The　employed　over-complete　dictionary　is　learned　through　K-SVD　with　orthogonal　matching　pursuit　(OMP)　for　sparse　representation.　Statistically　reduandancy　is　further　removed　by　a　modified　context-adaptive　binary　arithmetic　coding　(CABAC)　with　adaptive　context　models.　This　approach　not　only　retains　the　advantages　of　the　traditionally　encoder　for　lossy　compression　but　also　exploits　the　redundancy　in　the　quantization　error　to　achieve　high-quality　near-lossless　compression.　Experimental　results　demonstrate　that　our　method　significantly　outperforms　traditional　HEVC　lossy　encoder　with　over　-20%　BD-BR　on　average　at　high　bitrate　range　for　near-lossless　coding,　while　the　method　is　also　proved　to　be　efficient　at　low　bitrate　range　achieving　over　-50%　BD-BR　on　average　with　average　PSNR　over　41dB,　which　retains　near-lossless　performance.　©　2024　IEEE.