• Pallavi Sunil Phadatare
• Prof.Pratibha Pramod Chavan

— This paper presents a hybrid (loss less and lossy) technique for image vector quantization. The codebook is generated in two steps and first step is training set is sorted based on the magnitudes of the training vectors and step 2 is from the sorted list, training vector from every nth position is selected to for the code vectors. Followed by that, centroid computation with clustering is done by repeated iterations to improve the optimality of the codebook. The code book thus generated is compressed (Iossy) to reduce the memory needed to store the codebook with the slight degradation in the quality of the reconstructed image. The future wireless networks, such as Centralized Radio Access Network (C-RAN), c will requirement to deliver data rate about 100 times to 1000 times the current 4G technology. For C-RAN based network layout, there is a pressing The future wireless networks, such as Centralized Radio Access Network (C-RAN), will need to deliver data rate about 100 times to 1000 times the ongoing 4G technology. Compression of CPRI data is one of the prospect l enhancements. We introduce a vector quantization used compression algorithm for CPRI links, utilizing Lloyd algorithm.

"Survey on Vector Quantization for Image Compression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.1376-1378, June 2020, Available :http://www.jetir.org/papers/JETIR2006531.pdf

"Survey on Vector Quantization for Image Compression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp1376-1378, June 2020, Available at : http://www.jetir.org/papers/JETIR2006531.pdf