Improved compression ratio prediction in DCT-based lossy compression of remote sensing images

This paper deals with prediction of compression ratio (CR) in lossy compression of noisy remote sensing images using techniques based on discrete cosine transform (DCT). Properties of noise assumed additive (in original data or after proper variance stabilizing transform) are taken into account by setting quantization step (QS) proportional to noise standard deviation. It is shown that simple statistics of DCT coefficients in 8×8 blocks can be used for rather accurate prediction of CR. Functions employed in prediction are obtained in advance using curve regression into scatter-plots. The factors that have impact on prediction accuracy are studied. It is demonstrated that percentage of DCT coefficients that become zeroes after quantization can be a good input parameter for prediction. Applicability of the proposed CR prediction approach is confirmed by experiments with real-life multi- and hyperspectral data.