A complexity comparison between multilayer perceptrons applied to on-sensor image compression

A multilayer perceptron (MLP) can be used to implement a vector quantizer (VQ) under severe constraints in the computational complexity allowed. Such constraints are typical in applications such as focal-plane image compression, in which we are interested in eliminating the analog-to-digital (A/D) converters and mapping the analog data directly into a compressed bit stream, to save energy and silicon area. We compare a nonlinear MLP called the kernel lattice vector quantizer (KLVQ) and a clustering MLP known as the cluster-detection-and-labeling (CDL) network, with regard to their hardware requirements. We show that for similar rate-distortion performances, the KLVQ has complexity smaller than that of the CDL network.