| Paper: | MLSP-P5.10 | ||
| Session: | Image and Video Processing | ||
| Time: | Thursday, May 20, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Image and Video Processing Applications | ||
| Title: | A COMPLEXITY COMPARISON BETWEEN MULTILAYER PERCEPTRONS APPLIED TO ON-SENSOR IMAGE COMPRESSION | ||
| Authors: | José Gabriel R. C. Gomes; University of California, Santa Barbara | ||
| Sanjit K. Mitra; University of California, Santa Barbara | |||
| Rui J. P. de Figueiredo; University of California, Irvine | |||
| Abstract: | 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 Kernel Lattice Vector Quantizer (KLVQ) and a clustering MLP known as 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. | ||
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