LOW-COMPLEXITY PREDICTIVE LOSSY COMPRESSION OF HYPERSPECTRAL AND ULTRASPECTRAL IMAGES
Presented by: Enrico Magli, Author(s): Andrea Abrardo, Mauro Barni, University of Siena, Italy; Enrico Magli, Politecnico di Torino, Italy
Lossy compression of hyperspectral and ultraspectral images is traditionally performed using 3D transform coding. This approach yields good performance, but its complexity and memory requirements are unsuitable for onboard compression. In this paper we propose a low-complexity lossy compression scheme based on prediction, uniform-threshold quantization, and rate-distortion optimization. Its performance is competitive with that of state-of-the-art 3D transform coding schemes, but the complexity is immensely lower. Moreover, the algorithm is able to limit the scope of errors and packet losses, and is amenable to parallel implementation, making it suitable for onboard compression at high throughputs.