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Research Article Open access CC BY 4.0

Enhanced Image Compression and Processing Scheme

I. Manga, E. J. Garba, A. S. Ahmadu

Current Journal of Applied Science and Technology · pp. 1–11 · Published 14 Dec 2021

10.9734/cjast/2021/v40i3831586

Abstract

Image compression refers to the process of encoding image using fewer number of bits. The major aim of lossless image compression is to reduce the redundancy and irreverence of image data for better storage and transmission of data in the better form. The lossy compression scheme leads to high compression ratio while the image experiences lost in quality. However, there are many cases where the loss of image quality or information due to compression needs to be avoided, such as medical, artistic and scientific images. Efficient lossless compression become paramount, although the lossy compressed images are usually satisfactory in divers’ cases. This paper titled Enhanced Lossless Image Compression Scheme is aimed at providing an enhanced lossless image compression scheme based on Bose, Chaudhuri Hocquenghem- Lempel Ziv Welch (BCH-LZW) lossless image compression scheme using Gaussian filter for image enhancement and noise reduction. In this paper, an efficient and effective lossless image compression technique based on LZW- BCH lossless image compression to reduce redundancies in the image was presented and image enhancement using Gaussian filter algorithm was demonstrated. Secondary method of data collection was used to collect the data. Standard research images were used to validate the new scheme. To achieve these, an object approach using Java net beans was used to develop the compression scheme. From the findings, it was revealed that the average compression ratio of the enhanced lossless image compression scheme was 1.6489 and the average bit per pixel was 5.416667. Gaussian filter image enhancement was used for noise reduction and the image was enhanced eight times the original.

Enhanced image lossless compression processing.

Cited by 1

Fast Attack Algorithm for JPEG Image Encryption with Block Position Shuffle

Shanshan Li, Yali Guo, Jiaxin Huang · Journal of Shanghai Jiaotong University (Science) · 2023

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