Haar image compression using a neural network
Adnan Khashman, Kamil Dimililer · 2008
Wavelet Transform is one of the most popular methods applied in image compression. Wavelet-based image compression provides substantial improvements in picture quality at higher compression ratios. Haar wavelet transform based compression is one of the methods that can be applied to images. An ideal image compression system must yield good quality compressed images with good compression ratio, while maintaining minimum time cost. A neural network will be trained to establish the non-linear relationship between the image intensity and its compression ratios in search for an optimum ratio. This paper suggests that a neural network could be trained to recognize an optimum ratio for Haar wavelet compression of an image upon presenting the image to the network. Experimental results suggest that a trained neural network can simulate such non-linear relationship and thus can be successfully used to provide an intelligent optimum image compression system.