Quality of Image Compression of Block Adaptive Models for FPGA Implementation
Keywords:
Multi-level Block Truncation Code (ML-BTC), Bit Map, Multi-level Quantization (MLQ), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE)Abstract
With the intention of resolving this issue, image compression has become very important for efficient archiving and transmission of images. Compression is the process of coding that will effectively reduce the total number of bits needed to represent certain information. Currently, research in medical image compression concentrates on the implementation of methods such as Run length coding, Lempel-Ziv Welch (LZW), Huffman coding, Vector Quantization (VQ) and so on for improved quality of image. Among the various spatial domain image compression techniques, multi-level Block partition Coding (ML-BTC) is one of the best methods which has the least computational complexity. The parameters such as Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are measured and it is found that the implemented methods of BTC are superior to the traditional BTC. This paves the way for a nearly error free and compressed transmission of the images through the communication channel.
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