Lossless JPEG-Huffman Model for Digital Image Compression

Abstract

The transmission of digital images has been bedevilled with limitation of storage and bandwidth capacities. One of the common strategies to resolving this limitation is to perform pre-transmission compression on the images. In this research, a lossless Joint Photography Expert Group (JPEG) and Huffman algorithms-based model for digital image compression is proposed. The lossless JPEG component of the model was used to perform Differential Pulse Coding Modulation (DPCM) on the pixels while adaptive Huffman coding was used for quality improvement and standardization. The implementation was carried out in an environment characterized by Windows 10 with Visual Basic as frontend on Personal Computer with 4 GB RAM, 500 GB ROM and 2.2 Ghz Core i3 Processor. The experimental images used for testing the algorithms were acquired from Signal and Image Processing Institute in the University of Southern California (USC-SIPI). Camera (Nikon D7000) and Geographical Information System (GIS) images were also used. Quantitative analyses of the experimental results and performance evaluation using Compression Ratio (CR), Bits per pixel (Bpp), Maximum Difference (MD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Peak Signal Noise Ratio (PSNR), Average Difference (AD) and Structural Content (SC) were carried out. The analyses showed good compression rates and ratios for the proposed model. The superiority of the integrated model over some existing and related ones is also established.