Grayscale Images and RGB Video: Compression by Morphological Neural Network

Abstract

Abstract. This paper investigates image and RGB video compression by a supervised morphological neural network. This network was originally designed to compress grayscale image and was then extended to RGB video. It supports two kinds of thresholds: a pixel-component threshold and pixel-error counting threshold. The activation function is based on an adaptive morphological neuron, which produces suitable compression rates even when working with three color channels simultaneously. Both intra-frame and interframe compression approaches are implemented. The PSNR level indicates that the compressed video is compliant with the desired quality levels. Our results are compared to those obtained with commonly used image and video compression methods. Network application results are presented for grayscale images and RGB video with a 352 × 288 pixel size.

Pattern recognition on FPGA for aerospace applications

“…Before the implementation in hardware, the mathematical model has been exhaustively tested on Matlab/Simulink (Matlab-Simulink, 2015) environment in different applications. The main results were show up in previous pattern recognition works (Silva, 1998;Silva & Banon, 1999;Silva & Silva, 2004;Filho et al, 2014;Filho et al, 2015) and in image/video compression tasks (Souza et al, 2012;Souza et al, 2013). However the present work shows the first hardware implementation of the morphological operators based on ELUTs using templates generated using machine learning scheme adapted from Silva (1998) and Silva (2006) and implemented on simulated form in previous works (Filho et al, 2014;Filho et al, 2015).…”

“…In addition to the tasks of pattern recognition proposed in this paper, we expect that the same operators implemented in FPGA can also be adapted for image compression tasks from previous works (Souza et al, 2012;Souza et al, 2013)…”

“…These operators are the mathematical basis to build adaptive controlled threshold template matching (Silva, 1998) without traditional convolution operations. These operators allowed the development of several different applications as described in previous works (Silva & Banon, 1999;Rempel & Silva, 2001;Silva & Silva, 2004;Souza et al, 2012;Souza et al, 2013;Filho et al, 2014;Filho et al, 2015). The present work proposes a new standalone device for near real time pattern recognition based on Mathematical Morphology-MM operators defined by ELUTs, taking advantages from an adaptive engine applied to morphological operators adapted from previous works (Silva, 1998;Silva, 2006;Filho et al, 2014).…”