An Assessment of an Ideal Huffman Coding Related Approach for the Better Lossless Compression of Images Using IWT

This chapter delves into the challenges of image compression in the era of high-resolution photography, emphasizing the need for efficient compression techniques due to limited bandwidth and storage space. The study proposes a lossless image compression method that integrates linear prediction coding, Huffman coding, and integer wavelet transforms (IWT). The process begins with 2-D linear prediction to calculate integer pixel errors, followed by IWT to produce sparse coefficients. These coefficients are then processed to capture positions and eliminate unnecessary zeros and negative values, enhancing the efficiency of subsequent entropy coding. Huffman coding is employed to achieve the final compressed bitstream. The experimental results demonstrate the effectiveness of this approach, showing significant improvements in compression ratios and encoding times compared to traditional methods. The study also explores the frequency characteristics of different channels, proposing channel-adaptive arithmetic coding to further optimize the compression process. This innovative approach offers a promising solution for various applications, including image databases, communications, and remote sensing, where efficient image compression is crucial.