Keypoint encoding and transmission for improved feature extraction from compressed images

In many mobile visual analysis scenarios, compressed images are transmitted over a communication network for analysis at a server. Often, the processing at the server includes some form of feature extraction and matching. Image compression has been shown to have an adverse effect on feature matching performance. To address this issue, we propose to signal the feature keypoints as side information to the server, and extract only the feature descriptors from the compressed images. To this end, we propose an approach to efficiently encode the locations, scales, and orientations of keypoints extracted from the original image. Furthermore, we propose a new approach for selecting relevant yet fragile keypoints as side information for the image, thus further reducing the data volume. We evaluate the performance of our approach using the Stanford mobile augmented reality dataset. Results show that the feature matching performance is significantly improved for images at low bitrate.