NEURAL NETWORK BASED IMAGE COMPRESSION WITH LIFTING SCHEME AND RLC

Image compression is a process that helps in fast data transfer and effective memory utilization. In effect, the objective is to reduce data redundancy of the image while retaining high image quality. This paper proposes an approach for Wavelet based Image Compression using MLFF Neural Network with Error Back Propagation (EBP) training algorithm for second level approximation component and modified RLC is applied on second level Horizontal and Vertical components with threshold to discard insignificant coefficients. All other sub-bands (i.e. Detail components of 1 st level and Diagonal component of 2 nd level) that do not affect the quality of image (both subjective and objective) are neglected. With the proposed method in this paper CR (27.899), PSNR (70.16 dB) and minimum MSE (0.0063) of still image obtained are better when compared with SOFM, EZW and SPIHT.

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