We investigate transform coding to efficiently store and transmit SIFT and SURF image descriptors. We show that image and feature matching algorithms are robust to significantly compressed features. We achieve nearperfect image matching and retrieval for both SIFT and SURF using ∼2 bits/dimension. When applied to SIFT and SURF, this provides a 16× compression relative to conventional floating point representation. We establish a strong correlation between MSE and matching error for feature points and images. Feature compression enables many application that may not otherwise be possible, especially on mobile devices.