Image coding with face descriptors embedding

Abstract

Content descriptors, useful for browsing and retrieval tasks, are generally extracted and treated as a separate entity with respect to the nature of the content itself. At the same time, conventional coding processes do not take into account information carried out by content descriptors. Content descriptors are closely related to the content itself, and they potentially can be used to exploit redundancy in entropy coding processes. Embedding content descriptors in the bitstream can reduce content description extraction load, and at the same time, it can reduce the rate associated to the compressed content and its description.In this paper an effective implementation of this approach is presented, where image descriptors are actively used in the coding process for exploiting redundancy. First of all, image areas containing faces are detected and encoded using a scalable method, where the base layer is represented by the corresponding eigenface, and the enhancement layer is formed by the prediction error. The remaining areas are then encoded by using a traditional approach. Simulations show that achievable compression performances are comparable with those provided by conventional, making the proposed approach very convenient for source coding and content description.

Compression of photo collections using geometrical information

“…In [10] a descriptor-based approach is used for the compression of faces. The authors of [11] exploit the idea that low frequency content is less affected by the variations between the different views and extract a low frequency template from a set of images that is then used for the prediction of the different images.…”