Empirical Analysis of Image Compression through Wave Transform and Neural Network

Images have large data quantity, for storage and transmission of images, high efficiency image compression methods are under wide attention. In this Paper we proposed and implemented a wavelet transform and neural network model for image compression which combines the advantage of wavelet transform and neural network. Images are decomposed using Haar wavelet filters into a set of sub bands with different resolution corresponding to different frequency bands. Scalar quantization and Huffman coding schemes are used for different sub bands based on their statistical properties. The coefficients in low frequency band are compressed by Differential Pulse Code Modulation (DPCM) and the coefficients in higher frequency bands are compressed using neural network. Using this scheme we can achieve satisfactory reconstructed images with increased bit rate, large compression ratios and PSNR. To find the high compression ratio, Peak Signal to Noise ratio (PSNR) with increased bit rate by implementing the Discrete Wavelet Transform (DWT), DPCM and Neural Network. Image compression using cosine transform results a blocking artifacts, where as in image compression using wavelet transform over comes drawbacks associated with cosine transform, using neural network we reduce mean square error. Empirically analyze through Mat Lab and our software tool .We are also calculated and analyzed[12,13] the OO Metrics in this paper.