A Novel Hexadata Encoding Method for 2D Image Crypto-Compression

RODRIGUES, Marcos and SIDDEQ, Mohammed (2019). A Novel Hexadata Encoding Method for 2D Image Crypto-Compression. Multimedia Tools and Applications.

[img]
Preview
PDF
Siddeq-Rodrigues2019_Article_ANovelHexaDataEncodingMethodFo.pdf - Published Version
Creative Commons Attribution.

Download (2MB) | Preview
Official URL: https://link.springer.com/article/10.1007/s11042-0...
Open Access URL: https://link.springer.com/content/pdf/10.1007/s110... (Published)
Link to published version:: https://doi.org/10.1007/s11042-019-08405-3

Abstract

We proposed a novel method for 2D image compression-encryption whose quality is demonstrated through accurate 2D image reconstruction at higher compression ratios. The method is based on the DWT-Discrete Wavelet Transform where high frequency sub-bands are connected with a novel Hexadata crypto-compression algorithm at compression stage and a new fast matching search algorithm at decoding stage. The novel crypto-compression method consists of four main steps: 1) A five-level DWT is applied to an image to zoom out the low frequency sub-band and increase the number of high frequency sub-bands to facilitate the compression process; 2) The Hexa data compression algorithm is applied to each high frequency sub-band independently by using five different keys to reduce each sub-band to1/6of its original size; 3) Build a look up table of probability data to enable decoding of the original high frequency subbands, and 4) Apply arithmetic coding to the outputs of steps (2) and (3). At decompression stage a fast matching search algorithm is used to reconstruct all high frequency sub-bands. We have tested the technique on 2D images including streaming from videos (YouTube). Results show that the proposed crypto-compression method yields high compression ratios up to 99% with high perceptual quality images.

Item Type: Article
Uncontrolled Keywords: 0803 Computer Software; 0805 Distributed Computing; 0806 Information Systems; 0801 Artificial Intelligence and Image Processing; Software Engineering; Artificial Intelligence & Image Processing
Identification Number: https://doi.org/10.1007/s11042-019-08405-3
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 07 Aug 2019 13:58
Last Modified: 16 Dec 2019 10:00
URI: http://shura.shu.ac.uk/id/eprint/24929

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics