Gradient preserving quantization

Local features are widely used for content-based image retrieval and object recognition. Most feature descriptors are calculated from the gradients of a canonical patch around repeatable keypoints in the image. In this paper, we propose a technique for designing quantization matrices that reduce the mean squared error distortion of the gradient derived from DCT-encoded canonical patches. Experimental results demonstrate that our proposed patch encoder greatly outperforms a JPEG encoder at the same encoding complexity. Moreover, our quantization matrices achieve lower gradient distortion and larger number of feature matches at the same bit-rate.