Preserving SIFT features in JPEG-encoded images
For image compression applications where the information sink is not a person but a computer algorithm, the image encoder should control the encoding process in such a way that the important and relevant features of the image are preserved after compression. In this paper, our goal is to preserve the strongest SIFT features for JPEG-encoded images. We analyze the relevant characteristics of SIFT features and categorize the image Macroblocks into several groups. Then we propose a novel rate-distortion model which is based on the SIFT feature matching score. The dependency between the quantization table in the JPEG file and the common Lagrange multiplier is obtained from a training image database. Then for a given image quality we exploit this relationship to perform R-D optimization for each group. Our results show that the proposed algorithm achieves better feature preservation when compared to standard JPEG encoding. The proposed approach is fully standard compatible.