A key consideration in the design of image and video processing systems is the ever increasing spatial resolution of the captured images, which has a major impact on the performance requirements of the memory subsystem. This is further amplified by the facts that the memory bandwidth requirements and energy consumption of accessing the captured images have started to become the bottlenecks in the design of high-performance image processing systems. Inspired by the successful application of progressive image sampling techniques in various image processing tasks, this work proposes the concept of Context-based Image Acquisition for hardware systems that efficiently trades image quality for reduced cost of the image acquisition process. Based on the proposed framework, a hardware architecture is developed which alters the conventional memory access pattern, to progressively and adaptively access pixels from a memory subsystem. The sampled pixels are used to reconstruct an approximation to the ground truth, which is stored in a high-performance image buffer for further processing. An instance of the architecture is prototyped on an FPGA and its performance evaluation shows that a saving of up to 85 % of memory accessing time and 33 %/45 % of image acquisition time/energy are achieved on a set of benchmarks while maintaining a high PSNR.