Near-lossless compression of 3-D optical data

In this work, near-lossless compression yielding strictly bounded reconstru ction error is proposed for high-quality compression of remote sensing imag es. A classified causal DPCM scheme is presented for optical data, either m ulti/hyperspectral three-dimensional (3-D) or panchromatic two-dimensional (2-D) observations. It is based on a classified linear-regression predictio n, followed by context-based arithmetic coding of the outcome prediction er rors and provides excellent performances, both for reversible and for irrev ersible (near-lossless) compression. Coding times are affordable thanks to fast convergence of training. Decoding is always real time. If the reconstr uction errors fall within the boundaries of the noise distributions, the de coded images will be virtually lossless even though encoding was not strict ly reversible.