Compression of image ensembles using tensor decomposition
In this paper we address the problem of compressing a collection of still images of the same type, such as an image database of faces or any other visual objects of similar shapes. We refer to such collection of images as an image ensemble. The compression of image ensembles presents a unique set of requirements such as random access to any image within the collection without the need to reconstruct other images in the same ensemble. Such requirement is readily met by any still image compression standard, simply by encoding each image in isolation of other images within the same ensemble. However, traditional approaches of still image compression do not exploit the strong correlation that might exist among images within a given ensemble. In this paper we argue that a tensor-decomposition framework can achieve both: (a) random access to any image within an ensemble and (b) the exploitation of the correlation among all images within the same ensemble. To that end, we propose a progressive tensor-factorization approach that decomposes an image ensemble into a set of block-wise rank-one tensors. We derive and encode the rank-one tensors of an image ensemble using an optimization method for rank allocation among the original block tensors. Our simulation results show the viability of the proposed tensor framework when applied to image ensembles of faces.