Image Compression Using Feedforward Neural Networks - Hierarchical Approach
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Haar image compression using a neural network
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
A neural networks approach to image data compression
Applied Soft Computing
Adaptive data hiding for images based on harr discrete wavelet transform
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Lossless image compression with multiscale segmentation
IEEE Transactions on Image Processing
Wavelet-based color image compression: exploiting the contrast sensitivity function
IEEE Transactions on Image Processing
Efficient architectures for 3D HWT using dynamic partial reconfiguration
Journal of Systems Architecture: the EUROMICRO Journal
A new recognition method for natural images
WSEAS Transactions on Computers
HOSVD based image processing techniques
AIKED'11 Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
HOSVD based data representation and LPV model complexity reduction
AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
A Bayesian approach of wavelet based image denoising in a hyperanalytic multi-wavelet context
WSEAS Transactions on Signal Processing
International Journal of Computer Applications in Technology
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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 for compressing images. An ideal image compression system must yield good quality compressed images with good compression ratio, while maintaining minimal time cost. With Wavelet transform based compression, the quality of compressed images is usually high, and the choice of an ideal compression ratio is difficult to make as it varies depending on the content of the image. Therefore, it is of great advantage to have a system that can determine an optimum compression ratio upon presenting it with an image. We propose that neural networks can 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. Two neural networks receiving different input image sizes are developed in this work and a comparison between their performances in finding optimum Haar-based compression is presented.