Evaluation of Different Algorithms of Nonnegative Matrix Factorization in Temporal Psychovisual Modulation

Abstract

Temporal PsychoVisual Modulation (TPVM) is a newly proposed information display paradigm, which can be implemented by nonnegative matrix factorization (NMF) with additional upper bound constraints on the variables. In this paper, we study all the state-of-the-art algorithms in NMF, extend them to incorporate the upper bounds and discuss their potential use in TPVM. By comparing all the NMF algorithms with their extended versions, we find that: 1) the factorization error of the truncated alternating least squares algorithm always fluctuates throughout the iterations, 2) the alternating nonnegative least squares based algorithms may slow down dramatically under the upper bound constraints, 3) the hierarchical alternating least squares (HALS) algorithm converges the fastest and its final factorization error is often the smallest among all the algorithms. Based on the experimental results of the HALS, we propose a guideline of determining the parameter setting of TPVM, i.e., the number of viewers to support and the scaling factor for adjusting the light intensity of the images formed by TPVM. Our work will facilitate the applications of TPVM.

Publication
IEEE Transactions on Circuits and Systems for Video Technology
Jianzhou Feng
Jianzhou Feng
PH.D Student
Xiaoming Huo
Xiaoming Huo
Professor
Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.