The　video　compression　sensing　method　based　on　multi　hypothesis　has　attracted　extensive　attention　in　the　research　of　video　codec　with　limited　resources.　However,　the　formation　of　high-quality　prediction　blocks　in　the　multi　hypothesis　prediction　stage　is　a　challenging　task.　To　resolve　this　problem,　this　paper　constructs　a　novel　compressed　sensing-based　high-quality　adaptive　video　reconstruction　optimization　method.　It　mainly　includes　the　optimization　of　prediction　blocks　(OPBS),　the　selection　of　search　windows　and　the　use　of　neighborhood　information.　Specifically,　the　OPBS　consists　of　two　parts:　the　selection　of　blocks　and　the　optimization　of　prediction　blocks.　We　combine　the　high-quality　optimization　reconstruction　of　foreground　block　with　the　residual　reconstruction　of　the　background　block　to　improve　the　overall　reconstruction　effect　of　the　video　sequence.　In　addition,　most　of　the　existing　methods　based　on　predictive　residual　reconstruction　ignore　the　impact　of　search　windows　and　reference　frames　on　performance.　Therefore,　Block-level　search　window　(BSW)　is　constructed　to　cover　the　position　of　the　optimal　hypothesis　block　as　much　as　possible.　To　maximize　the　availability　of　reference　frames,　Nearby　reference　frame　information　(NRFI)　is　designed　to　reconstruct　the　current　block.　The　proposed　method　effectively　suppresses　the　influence　of　the　fluctuation　of　the　prediction　block　on　reconstruction　and　improves　the　reconstruction　performance.　Experimental　results　show　that　the　proposed　compressed　sensing-based　high-quality　adaptive　video　reconstruction　optimization　method　significantly　improves　the　reconstruction　performance　in　both　objective　and　supervisor　quality.