Exploiting JPEG Compression for Image Retrieval

Content-based image retrieval (CBIR) has been an active research area for many years, yet much of the research ignores the fact that most images are stored in compressed form which affects retrieval both in terms of processing speed and retrieval accruacy. In this paper, we address various aspects of JPEG compressed images in the context of image retrieval. We first analyse the effect of JPEG quantisation on image retrieval and present a robust method to address the resulting performance drop. We then compare various retrieval methods that work in the JPEG compressed domain and finally propose two new methods that are based solely on information available in the JPEG header. One of these is using optimised Huffman tables for retrieval, while the other is based on tuned quantisation tables. Both techniques are shown to give retrieval performance comparable to existing methods while being magnitudes faster.