Image compression using a neural network with learning capability of variable function of a neural unit
Ryuji Kohno, Mitsuru Arai, Hideki Imai · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
This paper proposes image compression using an advanced neural network in which a variable input-output function of a neural unit can be learnt as well as a weight coefficient of a neural connection corresponding to information source and application. Since the neural network has the improved learning capability for local nonlinearity of information source, its application to compression of nonlinear information such as image is investigated. A learning algorithm and adaptive controlling schemes of input-output functions are derived. Simulation results show that the neural network can achieve higher SNR and shorter learning time than a conventional network having only variable weights.