Improved LZW Compression Technique using Difference Method
Dibyendu Barman1, Md. Bakash Ahamed2
1Dibyendu Barman*, Department of Computer Science & Engineering, Government College of Engineering & Textile Technology Berhampore, WB, India.
2Md Bakash Ahamed, Department of Computer Science & Engineering, Government College of Engineering & Textile Technology Berhampore, WB, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 23, 2020. | Manuscript published on March 10, 2020. | PP: 87-92 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2216039520/2020©BEIESP | DOI: 10.35940/ijitee.E2216.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This work attempts to give a best approach for selecting one of the popular image compression algorithm. The proposed method is designed to find the best performance approach amongst the several compression algorithms. In this work existing lossless compression technique LZW (Lempel-Ziv-Welch) is redesigned to achieve better compression ratio. LZW compression technique works on the basis of repetition of data. In a situation where all the values are distinct or repetition of data does not present the LZW can’t work properly. To avoid this problem, difference method called difference matrix method which is actually calculate the difference between two consequence data and store it in a resultant matrix is used. In this case the matrix contains repetitive data which is more effective compared to LZW technique. Another problem of LZW is dictionary overflow, because of LZW works on ASCII character there is a limit of 256 dictionary length initially. In this work dynamic dictionary method is used without using the ASCII rather than this static method. As a result, this dictionary can contain the initial value anything in a range of -256 to 255. Here ASCII values are not used because the proposed method is applicable grayscale image, where the pixel values are between in range 0 to 255. Using these two changes the proposed improved LZW method becomes more powerful that can compress a non-repetitive set of data significantly. The proposed method is applied on many standard gray images found in the literature achieved 7% to 18% more compression the normal LZW keeping quality of the image same as existing.
Keywords: Image Compression, Lossless Compression, Lossy Compression, LZW, Difference Method.
Scope of the Article: Data Mining Methods, Techniques, and Tools