Prediction of Compression Ratio for DCT-Based Coders With Application to Remote Sensing Images

Abstract

A problem of predicting compression ratio (CR) for lossy image compression methods based on discrete cosine transform (DCT) is considered for remote sensing imaging as a main target application. We demonstrate that the noise presence in images leads to specific requirements to lossy image compression as well as to criteria used, and ways to meet these requirements. In particular, it is often desired to compress images in the neighborhood of optimal operation point (OOP) where a compressed image might be closer to the corresponding noise-free one compared to noisy (original, uncompressed) counterpart. In this paper, first, we consider a problem of predicting CR for compressing noisy images in OOP neighborhood. Several statistical parameters calculated in DCT domain in 8 × 8 pixel blocks are used for this. The factors that influence a prediction accuracy and the ways to improve this accuracy are discussed. Next, we show that there is a statistical parameter that does not require any a priori information on noise properties, and that can be used to predict CR with a high accuracy without any necessity to apply multiple image compression/decompression procedures. The proposed methods are thoroughly tested for two DCT-based image coders applied to component-wise lossy compression of hyperspectral data. Finally, a modification of the prediction approach for 3-D compression of multichannel images is proposed.