Compression of High-Dimensional Multispectral Image Time Series Using Tensor Decomposition Learning

Abstract

Multispectral imaging is widely used in many fields, such as in medicine and earth observation, as it provides valuable spatial, spectral and temporal information about the scene. It is of paramount importance that the large amount of images collected over time, and organized in multidimensional arrays known as tensors, be efficiently compressed in order to be stored or transmitted. In this paper, we present a compression algorithm which involves a training process and employs a symbol encoding dictionary. During training, we derive specially structured tensors from a given image time sequence using the CANDECOMP/PARAFAC (CP) decomposition. During runtime, every new image time sequence is quantized and encoded into a vector of coefficients corresponding to the learned CP decomposition. Experimental results on sequences of real satellite images demonstrate that we can efficiently handle higher-order tensors and obtain the decompressed data by composing the learned tensors by means of the received vector of coefficients, thus achieving a high compression ratio.