Block　compressed　sensing　based　on　mixed　variational　inequality　(BCS-MVI)　is　proposed　to　improve　the　performance　of　current　reconstruction　algorithms　for　block-based　compressed　sensing.　In　the　measurement　phase,　an　image　is　sampled　block　by　block.　In　the　recovery　period,　BCS-MVI　takes　the　sparse　regularization　of　the　natural　image　as　prior　knowledge　and　approaches　the　target　function　within　the　entire　image　through　the　modified　augmented　Lagrange　method　(ALM)　and　alternating　direction　method　(ADM)　of　multipliers.　Moreover,　for　the　reconstruction　problem　including　two　regularization　terms,　an　adaptive　weight　(−AW)　strategy　based　on　the　gray　entropy　of　the　initialized　image　is　studied.　BCS-MVI　achieves　an　average　PSNR　gain　of　0.5–2.0 dB　and　an　SSIM　gain　of　0.02–0.05　over　previous　block-based　compressed　sensing　methods,　and　the　reconstructing　time　only　slightly　fluctuates　with　the　sampling　rate.　The　algorithm　is　suitable　for　applications　in　multimedia　data　processing　with　fixed　transmission　delays.　©　2015,　Springer　Science+Business　Media　New　York.