Compression of Noisy Images Taking into Account Visual Quality: A Comprehensive Study

Vladimir Lukin, Bogdan Kovalenko, Sergii Kryvenko, Nikolay Ponomarenko, Karen Egiazarian, Jaakko Astola

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

This chapter deals with lossy compression of images that have been corrupted by additive noise. The chapter's key contribution is that the analysis is done from the perspective of compressed image visual quality. Several coders are explored for which the compression ratio is regulated in various ways. Lossless coding usually does not produce sufficient compression ratios for many practical applications.  Visual quality metrics that are the most adequate for the considered application (WSNR, MS-SSIM, PSNR-HVS-M and PSNR-HVS) are used. The objective is to analyze is optimal operation point (OOP) possible according to visual quality metrics. It is demonstrated that, under certain conditions, visual quality of compressed images can be slightly better than quality of original noisy images due to image filtering through lossy compression, i.e., OOP might exist. The “optimal” parameters of coders for which this positive effect can be observed depend upon standard deviation of the noise. We propose an algorithm to determine coder parameters in OOP. This enables the development of automated techniques for compressing noisy images at the vicinity of the optimal operation point, i.e. when visual quality improves or declines insufficiently.  Another advantage is that compression ratio for this case is quite high. The results of a series of grayscale test images with various noise variations are shown.
Original languageEnglish
Title of host publication Research Developments in Science and Technology
Pages89-109
Number of pages21
ISBN (Electronic)978-93-5547-508-4
DOIs
Publication statusPublished - 22 Jun 2022
Publication typeA3 Book chapter

Publication series

NameResearch Developments in Science and Technology Vol. 8

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