Loss Less Image firmness comparision by DPCM and DPCM with LMS Algorithm
In this paper we compare the compressed image for 1 and 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 more reduction in transmitted image 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 DPCM and DPCM with LMS, reconstructed image distortion is reduced and the prediction mean square error is reduced using DPCM with LMS.The LMS algorithm is easy to implement and computationally inexpensive. This feature makes the LMS algorithm attractive for image compression compare to only DPCM. The LMS Filter length was taken to be fixed taps. The parameter of LMS algorithm µ was set to be .0006 and this provide the good results. This is presenting the performance of using DPCM, using DPCM with LMS algorithm for loss less image compression. Keywords-Compression; DPCM; LMS; Average square distortion.