Prediction of Optimal Operation Point Existence and Its Parameters in BPG-Based Automatic Lossy Compression of Noisy Images| Chapter 1 | Current Overview on Science and Technology Research Vol. 9

 This branch focuses on lossy compression of noisy concepts. In opposite to lossy compression of clamor-free images, there are few peculiarities relating to cacophony filtering effect and analysis of condensation technique act. As a result of specific noise cleaning, the so-called optimal movement point (OOP) may survive. OOP is related to such limits of a coder that quality of a compressed figure is closer to the matching noise-free (true) concept compared to quality of uncompressed (original, boisterous) image where feature is characterized by the metric, conventional or optical one. If OOP for a given cacophonous image lives, it is expedient to automatically compress an countenance under interest in OOP or, at least, its surroundings. However, since the valid image is not at disposal in essence, it occurs impossible to decide does OOP exist or not. In this affiliate, we demonstrate that it is, usually, possible to predict OOP life (according to different versification) before image condensation. It is also possible to fairly set the coder parameters for the better compact graphics (BPG) coder, which command a price of the main attention. The first part concerns a basic case of additive silver Gaussian noise in one-component (grayscale) countenances. We demonstrate that increase (decline) of the conventional metric PSNR and able to be seen with eyes quality metric PSNR-HVS-M maybe predicted. If increase is anticipated, the decision on OOP existence is tackled and vice versa. For the case of OOP absence, a habit for setting the parameter Q that controls condensation in BPG is proposed. Thus, it enhances possible to carry out lossy condensation automatically. The extensions of the projected approach to joint compression of three-component concepts are proposed. OOP existence in this place case is even more probable. Image sub-examining in color components is studied. The chance of OOP existence for ocular quality metrics PSNR-HA and MDSI is illustrated.

Author(s) Details:

Vladimir Lukin,
National Aerospace University, Kharkov, Ukraine.

Bogdan Kovalenko,
National Aerospace University, Kharkov, Ukraine.

Sergii Kryvenko,
National Aerospace University, Kharkov, Ukraine.

Victoriya Naumenko,
National Aerospace University, Kharkov, Ukraine.

Benoit Vozel,
University of Rennes 1, Lannion, France.

Please see the link here: https://stm.bookpi.org/COSTR-V9/article/view/8755

Compression of Noisy Images Taking into Account Visual Quality: A Comprehensive Study | Chapter 6 | Research Developments in Science and Technology Vol. 8

This chapter discusses the lossy compression of pictures that include additive noise damage. The examination is done from the standpoint of compressed picture visual quality, which is the chapter's major contribution. The compression ratio for a number of coders is investigated and is controlled in various ways. Lossless coding typically doesn't yield compression ratios high enough for many real-world uses. The visual quality metrics WSNR, MS-SSIM, PSNR-HVS-M, and PSNR-HVS are employed since they are the best suitable for the application under consideration. The goal is to determine the optimal operating point (OOP) based on measurements for visual quality. It is shown that, under some circumstances, picture filtering caused by lossy compression, or the possibility of OOP, may cause compressed images to have slightly higher visual quality than the original noisy images. The "optimal" coder settings that allow for the observation of this beneficial impact depend on the noise standard deviation. In OOP, we suggest a technique for determining coder parameters. This makes it possible to create automatic methods for compressing noisy pictures close to the ideal operating point, or if visual quality increases or decreases inadvertently. Another benefit is that this case's compression ratio is fairly high. The outcomes of a collection of test photos in grayscale with different noise variations are displayed.

Author(s) Details:

Vladimir Lukin,

National Aerospace University, Kharkov, Ukraine.

Bogdan Kovalenko,

National Aerospace University, Kharkov, Ukraine.

Sergii Kryvenko,

National Aerospace University, Kharkov, Ukraine.

Nikolay Ponomarenko,

National Aerospace University, Kharkov, Ukraine and Tampere University, Tampere, Finland.

Karen Egiazarian,

Tampere University, Tampere, Finland.

Jaakko Astola,

Tampere University, Tampere, Finland.

Please see the link here: https://stm.bookpi.org/RDST-V8/article/view/7399