Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals

Sio-Hoi Ieng (1) , Eero Lehtonen (2) , Ryad Benosman (1)

1 Institut de la Vision
2 University of Turku

This paper introduces an event-based methodology to perform arbitrary linear basis transformations that encompass a broad range of practically important signal transforms, such as the discrete Fourier transform (DFT) and the discrete wavelet transform (DWT). We present a complexity analysis of the proposed method, and show that the amount of required multiply-and-accumulate operations is reduced in comparison to frame-based method in natural video sequences, when the required temporal resolution is high enough. Experimental results on natural video sequences acquired by the asynchronous time-based neuromorphic image sensor (ATIS) are provided to support the feasibility of the method, and to illustrate the gain in computation resources.

Mots clés: event-based signal processing; AER; discrete basis transforms; DWT; DCT

Domaines: Neurosciences; Traitement du signal et de l'image [eess.SP]

Sio-Hoi Ieng, Eero Lehtonen, Ryad Benosman. Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals. Frontiers in Neuroscience, 2018, 12, pp.373. ⟨10.3389/fnins.2018.00373⟩. ⟨hal-02166146⟩