Classified image compression using optimally structured auto-association networks

Here, an application of a set of auto-association networks with linear output neurons and sigmoidal hidden neurons for classified image compression is carried out. Simulations and statistical analysis of this type of network have shown that, at convergence, the hidden neurons operate mainly in their linear region. The nearly linear behaviour of the hidden neurons is exploited in finding out the minimum number of hidden neurons needed to reconstruct image data within a certain error threshold. Four optimally structured auto-association networks are set up so that each network is trained to encode a certain variance-based class of image blocks. Results have shown excellent performance of the proposed architecture in reproducing high-quality images at a low bit rate.