Feature-based image set compression

The biggest challenge in image set compression is how to efficiently remove the set redundancy among images as well as the redundancy inside a single image. Different from all the previous schemes, in this paper we are the first to propose a generic image set compression scheme which removes the set redundancy based on local features in addition to luminance values. The SIFT (Scale Invariant Feature Transform) descriptor which characterizes an image region invariant to scale and rotation is utilized in our scheme to measure and further enhance the correlation among images. Given an image set, we build a minimal cost prediction structure according to the SIFT-based prediction measure between images. We also utilize a SIFT-based global transformation to enhance the correlation between two images by aligning them to each other in terms of both geometry and intensity. The set redundancy and image redundancy are both further reduced by block-based motion estimation and rate-distortion optimal mechanism proposed in HEVC. Experimental results show that our new feature based scheme always produces the best result regardless the image set's properties.

1. INTRODUCTION

Image set compression has become increasingly important not only for transmission but also for storage due to the increased use of digital images. It deals with the problem of representing a set of correlated visual data in a highly compact way in the transmission and storage of specific highly correlated visual set, such as tomographic images and multispectral pictures [1]. Unlike image compression, which manages to reduce the redundancy inside a single image (denoted as image redundancy hereafter), the key problem in image set compression is efficiently reducing the redundancy inside one image set (denoted as set redundancy hereafter) in addition to the image redundancy.