Progressive lossy-to-lossless coding of hyperspectral images through regression wavelet analysis

Progressive Lossy-to-Lossless (PLL) coding techniques enable a gradual quality improvement of the recovered images, starting from a coarse approximation up to a perfect reconstruction. PLL is becoming a widespread approach in several scenarios, in particular, for compression of hyperspectral images. In this paper we assess the suitability of Regression Wavelet Analysis (RWA) for hyperspectral image progressive lossy-to-lossless coding. RWA is a recent spectral transform that combines a wavelet transform with a regression stage, providing excellent coding performance for lossless compression. When coupled with a pyramidal predictive weighting scheme, RWA also yields very competitive coding results for PLL at a low computational cost. Coding performance is assessed within the framework of Joint Photographic Experts Group (JPEG) 2000 standard, comparing RWA against state-of-the-art spectral transforms, including reversible Karhunen-Loeve Transform (rKLT) and Pairwise Orthogonal Transform (POT). Comparison with respect to Multiband Context-based Adaptive Lossless/Near-Lossless Image Coding (M-CALIC) technique is also provided. Experiments are conducted on uncalibrated and calibrated hyperspectral images from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), satellite-borne Hyperion and Infrared Atmospheric Sounding Interferometer (IASI) sensors. Discussion embraces rate-distortion performance, bit-per-pixel-per-component rate distribution and classification outcome.