Modeling of neural image compression using GA and BP: a comparative approach

It is well known that the classic image compression techniques such as JPEG and MPEG have serious limitations at high compression rate; the decompressed image gets really fuzzy or indistinguishable. To overcome problems associated with conventional methods, artificial neural networks based method can be used. Genetic algorithm is a very powerful method for solving real life problems and this has been proven by applying to number of different applications. There is lots of interest to involve the GA with ANN for various reasons at various levels. Trapping in the local minima is one of the well-known problems of gradient decent based learning in ANN. The problem can be addressed using GA algorithm. But no work has been done to evaluate the performance of both learning methods from the image compression point of view. In this paper, we investigate the performance of ANN with GA in the application of image compression for obtaining optimal set of weights. Direct method of compression has been applied with neural network to get the additive advantage for security of compressed data. The experiments reveal that the standard BP with proper parameters provide good generalize capability for compression and is much faster compared to earlier work in the literature, based on cumulative distribution function. Further, the results obtained shows that general concept about GA, it performs better over gradient decent based learning, is not applicable for image compression.

@article{Rajput2011,
  title     = {Modeling of neural image compression using GA and BP: a comparative approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {1},
  number    = {1},
  year      = {2011},
  publisher = {The Science and Information Organization},
  note      = {Special Issue on Image Processing and Analysis},
  author    = {G G Rajput and Vrinda Shivashetty and Manoj Kumar singh},
  doi       = {10.14569/SpecialIssue.2011.010104},
  url       = {https://doi.org/10.14569/SpecialIssue.2011.010104}
}