Scalable Coding of Image Collections with Embedded Descriptors

With the increasing popularity of repositories of personal images, the problem of effective encoding and retrieval of similar image collections has become very important. In this paper we propose an efficient method for the joint scalable encoding of image-data and visual-descriptors, applied to collections of similar images. From the generated compressed bit stream, it is possible to extract and decode the visual information at different granularity levels, enabling the so called ldquomidstream content accessrdquo. The proposed approach is based on the appropriate combination of vector quantization (VQ) and JPEG2000 image coding. Specifically, the images are encoded at a first draft level using an optimal visual-codebook, while the residual errors are encoded using a JPEG2000 approach. In this way, the codebook of the VQ is freely available as an efficient visual descriptor of the considered image collection. This scalable representation supports fast browsing and retrieval of image collections providing also a coding efficiency comparable with those of standard image coding methods.