Semantic　segmentation　plays　a　critical　role　in　image　understanding.　Recently,　Fully　Convolutional　Network　(FCN)-based　models　have　made　significant　progress　in　semantic　segmentation.　However,　achieving　the　full　utilization　of　contextual　information　and　recovery　of　lost　spatial　details　remains　a　huge　challenge.　In　this　paper,　we　present　a　semantic　segmentation　model　based　on　pyramid　context　contrast　and　a　subpixel-aware　dense　decoder.　We　propose　first　using　the　pyramid　context　contrast　to　exploit　the　capability　of　contextual　information　by　aggregating　multi-scale　foreground　representations　in　different　background　regions　via　the　pyramid　context　contrast　module.　Then,　we　add　a　subpixel-aware　dense　decoder　architecture　to　reuse　features　extracted　from　different　decoder　levels　by　pixel　shuffle,　which　can　reasonably　resolve　resolution　inconsistency　between　feature　maps.　Next,　we　refine　the　boundary　by　utilizing　spatial　visual　information　about　low-level　features　via　a　boundary　refinement　branch　with　addition　of　auxiliary　supervision.　The　presented　model　was　evaluated　using　the　PASCAL　VOC　2012　semantic　segmentation　benchmark　and　achieved　a　performance　of　86.9%,　demonstrating　that　the　proposed　model　achieves　considerable　improvement　over　most　state-of-the-art　models.