Compressive imaging exploits sparsity/compressibility of natural scenes to reduce the detector count/read-out bandwidth in a focal plane array by effectively implementing compression during the acquisition process. How-ever, realizing the full potential of compressive imaging entails several practical challenges, such as measurement design, measurement quantization, rate allocation, non-idealities inherent in hardware implementation, scalable imager architecture, system calibration and tractable image formation algorithms. We describe an information-theoretic approach for compressive measurement design that incorporates available prior knowledge about natural scenes for more efficient projection design relative to random projections. Compressive measurement quantization and rate-allocation problem are also considered and simulation studies demonstrate the performance of random and information-optimal projection designs for quantized compressive measurements. Finally we demonstrate the feasibility of optical compressive imaging with a scalable compressive imaging hardware implementation that addresses system calibration and real-time image formation challenges. The experimental results highlight the practical effectiveness of compressive imaging with system design constraints, non-ideal system components and realistic system calibration.