Lossless Huffman coding image compression implementation in spatial domain by using advanced enhancement techniques
Ali Tariq Bhatti, Jung Ho Kim · 2016
Images are basic source of information for almost all scenarios that degrades its quality both in visually and quantitatively way. Now-a-days, image compression is one of the demanding and vast researches because high Quality image requires larger bandwidth. Raw images need larger memory space. In this paper, read an image of equal dimensional size (width and length) from MATLAB. Initialize and extract M-dimensional vectors or blocks from that image. However, initialize and design a code-book of size N for the compression. Quantize that image by using Huffman coding Algorithm to design a decode with table-lookup for reconstructing compressed image of different 8 scenarios. In this paper, several enhancement techniques were used for lossless Huffman coding in spatial domain such as Laplacian of Gaussian filter. Use laplacian of Gaussian filter to detect edges of lossless Huffman coding best quality compressed image(scenario#8) of block size of 16 and codebook size of 50. Implement the other enhancement techniques such as pseudo-coloring, bilateral filtering, and water marking for the lossless Huffman coding c based on best quality compressed image. Evaluate and analyze the performance metrics (compression ratio, bit-rate, PSNR, MSE and SNR) for reconstructed compress image with different scenarios depending on size of block and code-book. Once finally, check the execution time, how fast it computes that compressed image in one of the best scenarios. The main aim of Lossless Huffman coding using block and codebook size for image compression is to convert the image to a form better that is suited for analysis to human.