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_aAranzado, John Dylan R.; Barboza, Gian Karlo J.; Linget, Karl Francis.
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_aEnhancement of the huffman algorithm with discrete wavelet transform applied to lossless image compression /
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_cAranzado, John Dylan R.; Barboza, Gian Karlo J.; Linget, Karl Francis.
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_cJune 2023.46
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_c28 cm.
_a54 pp.
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_aunmediated
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_aUndergraduate Thesis: (Bachelor of Science in Computer Science) Pamantasan ng Lungsod ng Maynila, 2023.
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_aABSTRACT: Huffman coding is a data compression technique that allows for efficient encoding and decoding of data without any loss of information. David Huffman initially developed it. Typically, Huffman coding is effective for compressing data containing recurring characters. This study aims to improve the basic Huffman algorithm for compressing and decompressing grayscale images. This study presents an enhanced approach for compressing and decompressing grayscale images using an advanced algorithm version. Instead of relying on the basic Huffman Coding Algorithm, the researchers adopted a more efficient method. They employed the discrete wavelet transform to decompose the input image into distinct sets of signals. Each group represents a series of coefficients that describe the signal's evolution over time in the corresponding frequency band. After decomposing the image, we apply an improved version of the base Huffman Coding Algorithm to convert the decomposed signals into a bit stream, effectively achieving further compression. The researchers conducted a decompression process to restore the compressed image to its original form. By employing this approach, the proposed algorithm outperforms the base algorithm and alternative image compression techniques regarding compression ratio, compression time, and bits per pixel. The researchers selected these performance metrics to assess and compare the effectiveness of the proposed algorithm against other image compression methods. As a result, this study provides a starting point for future researchers to enhance further and optimize the improved Huffman algorithm in combination with the discrete wavelet transform (DWT). They can explore different variations and modifications to improve its performance, compression efficiency, and computational complexity. Additionally, these advancements can contribute to the development of image compression technology, benefiting various fields such as digital imaging, multimedia and data storage.
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