Study of two neural feed-forward structures for digital image compression
DOI:
https://doi.org/10.17533/udea.redin.14178Keywords:
digital image compression, lossy compression, feedforward neuronal networs, peak signal noise relation, compression rateAbstract
This document shows and explains the process for compressing images, in grayscale and in color, using two neural topologies: image function and funnel. For the analysis of neuronal schemes, the number of neurons and layers, type of image, size and number of blocks during training are considered; in order to give experimental support to neural architectures. Quality criteria of the image obtained are also analyzed, such as peak signal-to-noise ratio (PSNR) and compression rate. The importance of the selection of parameters evaluated in quality and compression time is evident. The experimentation process shows that the funnel-type architecture allows to achieve values higher than 35dB in terms of PSNR and 2 bits per pixel in gray images or 3 bpp in color images, with times less than 3 seconds for images smaller than 1 mega pixel. Finally, some recommendations are made based on the methodology used when it is desired to understand images with feed-through networks around the selection of architecture parameters, the pre-processing of the image and the training of the network.
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