Compressed sensing has received significant attention in recent years, primarily as a mathematical phenomenon. There has been, on the other hand, significantly less attention paid towards incorporation of compressed-sensing methodology into practical signal-processing applications. In this talk, we consider compressed sensing in the context of several image and video applications. The foundations of our discussion rest on a recent block-based strategy for compressed-sensing recovery of a single still image. In our approach, block-based random image sampling is coupled with a projection-driven compressed-sensing recovery that encourages sparsity in the domain of an image transform simultaneously with a smooth reconstructed image. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. This still-image reconstruction is then extended to multiview image sets, incorporating inter-image disparity compensation into the image-recovery process in order to take advantage of the high degree of inter-image correlation common to multiview scenarios. Finally, a similar approach is adopted for the reconstruction of video in which each frame has been subject to block-based random projections, and motion estimation and motion compensation across an entire group of frames informs the compressed-sensing recovery process.