Abstract
Over the last years, wavelet theory has been used with great success in a wide range of applications as signal de-noising and image compression. An ideal image compression system must yield high-quality compressed image with high compression ratio. This paper attempts to find the most useful wavelet function to compress an image among the existing members of wavelet families. Our idea is that a backpropagation neural network is trained to select the suitable wavelet function between the two families: orthogonal (Haar) and biorthogonal (bior4.4), to be used to compress an image efficiently and accurately with an ideal and optimum compression ratio. The simulation results indicated that the proposed technique can achieve good compressed images in terms of peak signal to noise ratio (PSNR) and compression ratio (t) in comparison with random selection of the mother wavelet.
Highlights
Digital image compression is a topical research area in the field of image processing due to its large number of application such as aerial surveillance, reconnaissance, medicine and multimedia communications
The backpropagation neural network BPNN using for this work is a supervised learning network which involves a teacher who provides it with the answers in the form of the target output matrix
The neural network used in this work was a backpropagation neural network based on the scaled conjugate gradient algorithm training
Summary
Digital image compression is a topical research area in the field of image processing due to its large number of application such as aerial surveillance, reconnaissance, medicine and multimedia communications. It has received significant attention of researchers whose major focus is to develop different compression schemes that provide good visual quality with fewer bits to represent digital images in order to secure and facilitate the data transmission by reducing the memory required for its storage [1], [2]. The main core of image compression technology consists of three important processing stages: pixel transforms, vector quantization and entropy coding. Several wavelet families are available for image compression [9] and selecting the appropriate one is very important as many works have proved that the choice of the best wavelet has significant impact on the quality of compression [10]-[12]
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