Efficient Filtering of JPEG Images
With image databases growing rapidly, efficient methods for content-based image retrieval (CBIR) are highly sought after. In this paper, we present a very fast method for filtering JPEG compressed images to discard irrelevant pictures. We show that compressing images using individually optimised quantisation tables not only maintains high image quality and therefore allows for improved compression rates, but that the quantisation tables themselves provide a useful image descriptor for CBIR. Visual similarity between images can thus be expressed as similarity between their quantisation tables. As these are stored in the JPEG header, feature extraction and similarity computation can be performed extremely fast, and we consequently employ our method as an initial filtering step for a subsequent CBIR algorithm. We show, on a benchmark dataset of more than 30,000 images, that we can filter 80% or more of the images without a drop in retrieval performance while reducing the online retrieval time by a factor of at about 5.