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| 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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