State-of-the-art compression frameworks usually contain a prediction module and an error context modeling module to reduce redundancy among pixels and improve compression performance. Modern compression algorithms are context adaptive. While adaptive compression algorithms improve over a static context model, they are computationally prohibitive, as the model has to be learned per image during encoding. In this work, we formulate the problem of active context modeling where we train an approximated error context model using an actively selected subset of pixels to significantly speedup the error context modeling while minimizing the impact on compression rate. We investigate the proposed active context modeling framework for both single image compression and burst image compression where the goal is to compress a set of images (usually 6 to 12) captured at a very short time interval between each other. We find that our active context modeling framework is significantly faster than the state-of-the-art while achieving a comparable compression rate. These results indicate the utility of the proposed active context modeling framework for image compression.