Differential compression and optimal caching methods for content-based image search systems

Compression and caching are two important issues for a large on-line image server. In this paper, we propose a new approach to compression by exploring similarity in large image archives. An adaptive vector quantization approach using content categorizations, including both the semantic level and the feature level, is developed to provide a differential compression scheme. We show that this scheme is able to support flexible and optimal caching strategies. The experimental results demonstrate that the proposed technique can improve the compression rate by about 20 percent compared to JPEG compression, and can improve the retrieval response by 5 percent to 20 percent under different typical access scenarios.