08-02-2013, 02:25 PM
Hybrid Image Compression using DWT and Neural Networks
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Abstract:
Image compression is playing a key role in the development of various multimedia computer services and telecommunication applications. As image needs a huge amount of data to store it, there is pressing need to limit image data volume for transport along communication links. An ideal image compression system must yield good quality compressed images with good compression ratio, while maintaining minimal time cost. The goal of image compression techniques is to remove redundancy present in data in a way that enables image compression technique. There are numerous lossy and lossless image compression techniques. For the still digital image or video, a lossy compression is preferred. Wavelet-based image compression provides substantial improvements in picture quality at higher compression ratios. Contrary to traditional techniques for image compression, neural networks can also be used for data or image compression. In this paper both of these methods for compression of images to obtain better quality.
INTRODUCTION
Compression methods are being rapidly developed to compress large data files such as images, where data compression in multimedia applications has lately become more vital .With the increasing growth of technology and the entrance into the digital age, a vast amount of image data must be handled to be stored in a proper way using efficient methods usually succeed in compressing images, while retaining high image quality and marginal reduction in image size.
Image compression using Wavelet Transforms is a powerful method that is preferred by scientists to get the compressed images at higher compression ratios. It is a popular transform used for some of the image compression standards in lossy compression methods. Unlike the discrete cosine transform, the wavelet transform is not Fourier-based and therefore wavelets do a better job of handling discontinuities in data. 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. Image compression using wavelet transform and a neural network was suggested recently [1].
PROBLEM DEFINITION
One of the major difficulties encountered in image processing is the huge amount of data used to store an image. Thus, there is pressing need to limit the resulting data volume. Image compression techniques aim to remove the redundancy present in data in a way that makes image reconstruction possible. It is necessary to find the statistical properties of the image to design an appropriate compression transformation of the image; the more correlated the image data are, the more data items can be removed.
Numerous lossy image compression techniques have been developed in the past years. The transform-based coding techniques, and in particular the block transform coding, have proved to be the most effective in obtaining large compression ratios while retaining good visual quality. In particular, cosine-transform-based techniques (JEPG) have been found to obtain excellent results in many digital image compression applications.
CONCLUSION
All the disadvantages of Joint Photographic Expert Group (JPEG) have overcome in Neural Network based Hybrid image compression, (Hybrid technique concept proving Combining wavelets and neural network).
The implementation of the proposed method used biorthogonal image compression where the quality of the compressed images degrades at higher compression ratios due to the nature of the lossy wavelet compression. Noise on compressed data samples does not influence retrieval of original image using Neural Network (NN) techniques where as in Joint Photographic Expert Group (JPEG) technique noise effects decompression.