An enhancement of LZW Algorithm applied in image compression and decompression .

By: Austria, Elvind Marius Y. and Veraces, Kristopher N
Language: English . . c2015Description: Undergraduate Thesis: (BSCS major in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2015Content type: text Media type: unmediated Carrier type: volumeGenre/Form: .DDC classification: . LOC classification: QA76.9 A87 2015
Contents:
ABSTRACT: One big problem of a photographer or a person than loves photos was to how to store their images without exhausting all its disk space. Data compression is the process of reducing the size and number of bits needed to store and transmit data. This process is used to reduce the size of the file and to reduce the time needed for the transmission of data. Lempel-Ziv-Welch algorithm or also known as the LZW algorithm is a popular general algorithm used in compression and decompression. It is capable of working on almost any type of data. However, the proponents encountered the following problems with regards to the said algorithm. First is that the less the repetitive data for each pixel, the longer the compression time. For the algorithm to solve this problem, the algorithm must read 16 bits at a time for it to fasten the compression time. Second problem is that the compression and decompression of complicated image files result into a bigger file than the original. And last, there is a loss of data when it exceeds the maximum limit of the dictionary. In order to solve the stated problems, the proponents suggested the following objectives: To be able to compress colored images with the same speed as with the grayscale image. Since compression of grayscale images is much faster compare to compressing to colored images. Thus, compressing both images should have least minimum time margin or for best to compressed with same speed. Next is to improve the compression percentage of when compressing colored images or images with high color noise. And last, to be able to read multiple characters at a time and to reduce the overall size of the dictionary and to make it as small as possible and to be able to prevent loss of data. The researches recommend people to use the enhanced application, and recommend those that are interested to further enhance the system, better improving the size of the dictionary.
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ABSTRACT: One big problem of a photographer or a person than loves photos was to how to store their images without exhausting all its disk space. Data compression is the process of reducing the size and number of bits needed to store and transmit data. This process is used to reduce the size of the file and to reduce the time needed for the transmission of data. Lempel-Ziv-Welch algorithm or also known as the LZW algorithm is a popular general algorithm used in compression and decompression. It is capable of working on almost any type of data. However, the proponents encountered the following problems with regards to the said algorithm. First is that the less the repetitive data for each pixel, the longer the compression time. For the algorithm to solve this problem, the algorithm must read 16 bits at a time for it to fasten the compression time. Second problem is that the compression and decompression of complicated image files result into a bigger file than the original. And last, there is a loss of data when it exceeds the maximum limit of the dictionary. In order to solve the stated problems, the proponents suggested the following objectives: To be able to compress colored images with the same speed as with the grayscale image. Since compression of grayscale images is much faster compare to compressing to colored images. Thus, compressing both images should have least minimum time margin or for best to compressed with same speed. Next is to improve the compression percentage of when compressing colored images or images with high color noise. And last, to be able to read multiple characters at a time and to reduce the overall size of the dictionary and to make it as small as possible and to be able to prevent loss of data. The researches recommend people to use the enhanced application, and recommend those that are interested to further enhance the system, better improving the size of the dictionary.


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