Spatially-weighted nonnegative matrix factorization with application to temporal psychovisual modulation

Nonnegative Matrix Factorization (NMF), which decomposes a target matrix into the product of two matrices with nonnegative elements, has been widely used in various fields of signal processing. In visual signal processing, the spatially nonuniformed distribution of perceptually meaningful information in image and video frames calls for a kind of Spatially-Weighted NMF (swNMF) that applies location dependent weights into the decomposition problem. In this paper we introduce swNMF solution based on the hierarchical alternating least squares (HALS) approach. Then we exemplify its application to a new information display diagram named temporal psychovisual modulation (TPVM) with comparison with traditional HALS method and baseline algorithm of multiplicative update (MU).