Implementation of Lossless Image Compression Analysis Using PCLZ Algorithm with Multiwavelet Transform
V. Manohar1, G. Laxminarayana2, T. Satya Savithri3
1V. Manohar, Research Scholar, Department of ECE, Jawaharlal Nehru Technological University, Hyderabad (Telangana), India.
2G. Laxminarayana, Department of ECE, Jawaharlal Nehru Technological University, Hyderabad (Telangana), India.
3T. Satya Savithri, Department of ECE, Jawaharlal Nehru Technological University, Hyderabad (Telangana), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 112-116 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6356048419/19©BEIESP
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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: Image compression is the method of encoding less information than original bits of representation and to aid this fact, various associated methods are being reviewed. In this paper author(s) proposes an improvement over Pixel Compressed Lempel-Ziv (PCLZ) a compression algorithm, by employing a new technique that uses multiwavelet decomposition (MWD). The loss-less compression applications are used for quality data transfers, social media, medical imaging, and digital camera technology emerge. In previous loss-less image compression techniques effort to find the smallest specific pixel intensity levels of image quality, while the PCLZ compression used the maximum levels of LL band to compress the image. Moreover, the reconstruction of the image by using three-level Daubechies wavelet decomposition creates a number of levels of gray vector matrixes. The proposed method results in good quality metrics for the compress ratio (CR), peak signal to noise ratio (PSNR), mean square error (MSE) and bits per pixels (BPP) to exhibits a large set of different standard test images.
Keywords: Data Compression, Daubechies Transformation, Discrete Wavelet Transformation, Pixel-Based Compression, Lossless Image Coding.
Scope of the Article: Image Processing