Image Compression Using Dpcm with Lms Algorithm

The image compression in telecommunication is very demanding application to control the distortion in image in channel after transmitting the image. The Differential pulse code modulation (DPCM) may be used to remove the unused bit in the image for image compression. In this thesis we compare the compressed image for 1, 2, 3, bits (2, 4 and 8 quantization levels, respectively), estimation error and average square distortion using DPCM with fixed coefficient and using DPCM with LMS algorithm. The LMS algorithm may be used to adapt the coefficients of an adaptive prediction filter for image source encoding. Results are presented which show LMS may provide almost 2 bit per pixel reduction in transmitted bit rate compare to DPCM when distortion levels are approximately the same for both methods. Alternatively, LMS can be used in fixed bit-rate environments to decrease the reconstructed image distortion and prediction mean square error. When compared with fixed coefficient DPCM and adaptive coefficient LMS, reconstructed image distortion is reduced and the prediction mean square error is reduced using DPCM with LMS. This thesis is presenting the performance of using DPCM, using DPCM with LMS algorithm for image compression. Real data for leena.PNG image signal. The performance of these methods for image compression has been verified via computer simulations using MATLAB.