SIFT-Based Image Compression

Abstract

This paper proposes a novel image compression scheme based on the local feature descriptor - Scale Invariant Feature Transform (SIFT). The SIFT descriptor characterizes an image region invariantly to scale and rotation. It is used widely in image retrieval. By using SIFT descriptors, our compression scheme is able to make use of external image contents to reduce visual redundancy among images. The proposed encoder compresses an input image by SIFT descriptors rather than pixel values. It separates the SIFT descriptors of the image into two groups, a visual description which is a significantly sub sampled image with key SIFT descriptors embedded and a set of differential SIFT descriptors, to reduce the coding bits. The corresponding decoder generates the SIFT descriptors from the visual description and the differential set. The SIFT descriptors are used in our SIFT-based matching to retrieve the candidate predictive patches from a large image dataset. These candidate patches are then integrated into the visual description, presenting the final reconstructed images. Our preliminary but promising results demonstrate the effectiveness of our proposed image coding scheme towards perceptual quality. Our proposed image compression scheme provides a feasible approach to make use of the visual correlation among images.