<title>Content-based image compression for ATR applications</title>

Abstract

Conventional image compression methods compress all regions of an image with a roughly uniform compression ratio. This means that any regions of special interest are degraded on an equal basis as the remainder of the image. Content-Based Image Compression (CBIC) methods assign different compression rates to different regions of an image according to their priorities, or according to the relative importance of the regions for certain applications. For example, for visual perception, we can assign different compression rates to different objects so that after compression the objects of interest satisfy certain MSE (Mean Square Error) requirements regardless of the overall compression rate. For optimal ATR (Automatic Target Recognition) performance, the recognition error rate might be optimized instead of MSE so that target recognition performance will be guaranteed at some desired level, and held constant throughout the entire image. In this paper, we introduce a content-based image encoder based on the popular DCT and wavelet transforms. Instead of selecting the DCT/wavelet coefficients that minimize the MSE to achieve optimum visual effects, we propose an algorithm to preserve those coefficients that minimize the recognition error. For any ATR system that utilizes the resulting compressed images, the recognition error is bounded by the information-theoretic distances. We employ Chernoff distances to compute the cost function of the recognition error. Compared to image compression methods optimized for visual perception, our results show that this CBIC method for ATR is able to achieve significantly more uniform ATR performance by assigning different compression rates to different regions.