Near-lossless compression by relaxation-labeled 3D prediction

In this work, near-lossless compression, i.e., yielding strictly bounded reconstruction error, is proposed for high-quality data compression. An interframe causal DPCM scheme is presented for interframe compression of remotely sensed optical data, both multispectral and hyperspectral, as well as of volumetric medical data. The proposed encoder relies on a classified linear-regression prediction, followed by context-based arithmetic coding of the outcome prediction errors. It provides outstanding performances, both for reversible and for irreversible, i.e., near-lossless, compression. Coding time are affordable thanks to fast convergence of training. Decoding is always performed in real time.