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

@article{Zemliachenko2016ImprovedCR,
  title={Improved compression ratio prediction in DCT-based lossy compression of remote sensing images},
  author={Alexander N. Zemliachenko and Sergey K. Abramov and Vladimir V. Lukin and Benoit Vozel and Kacem Chehdi},
  journal={2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  year={2016},
  pages={6966-6969}
}
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… CONTINUE READING