Open AccessA Novel Multilevel Lossy Compression Algorithm for Grayscale Images Inspired by the Synthesization of Biological Protein Sequences
Author(s) -
Mohammad Nassef,
Monagi H. Alkinani
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3125009
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Enormous number of images are generated daily in all areas of life, including social media, medical and navigation images. Moreover, the development of smart phones among other specialized media-capturing devices has witnessed great advances during the last decade. Consequently, the storage, transmission, and analysis of images become essential and frequent tasks. Thus, various research efforts tried to address the image compression problem from different computational perspectives. This article presents a novel multilevel lossy compression algorithm for grayscale images, namely Image-as-Protein (IaP ), that is inspired by the translation of DNA sequences into protein sequences that occurs inside live beings. Because of the high similarity of the resulting textual protein sequence, it can be tackled by general text compression techniques with competitive compression ratios. Various qualitative comparisons and quantitative measures such as BPP , SSIM and PSNR have been carried out on multiple grayscale image benchmark datasets. The experimental results showed that the proposed algorithm is promising compared to the famous JPEG lossy image compression standard.