Book ChapterDOI
Lossy compression of images with additive noise
Nikolay N. Ponomarenko,Vladimir V. Lukin,Mikhail Zriakhov,Karen Egiazarian,Jaakko Astola +4 more
- pp 381-386
TLDR
Image pre-filtering is shown to be expedient for coded image quality improvement and/or increase of compression ratio and some recommendations on how to set the compression ratio to provide quasioptimal quality of coded images are given.Abstract:
Lossy compression of noise-free and noisy images differs from each other. While in the first case image quality is decreasing with an increase of compression ratio, in the second case coding image quality evaluated with respect to a noise-free image can be improved for some range of compression ratios. This paper is devoted to the problem of lossy compression of noisy images that can take place, e.g., in compression of remote sensing data. The efficiency of several approaches to this problem is studied. Image pre-filtering is shown to be expedient for coded image quality improvement and/or increase of compression ratio. Some recommendations on how to set the compression ratio to provide quasioptimal quality of coded images are given. A novel DCT-based image compression method is briefly described and its performance is compared to JPEG and JPEG2000 with application to lossy noisy image coding.read more
Citations
More filters
Journal ArticleDOI
Lossy compression of noisy images based on visual quality: a comprehensive study
TL;DR: 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.
Journal ArticleDOI
Methods and automatic procedures for processing images based on blind evaluation of noise type and characteristics
Vladimir V. Lukin,Sergey K. Abramov,Nikolay N. Ponomarenko,Mikhail L. Uss,Mikhail L. Uss,Mikhail Zriakhov,Benoit Vozel,Kacem Chehdi,Jaakko Astola +8 more
TL;DR: Possible structures of automatic procedures are presented and discussed for several typical applications of image processing as remote sensing data preprocessing and compression.
Proceedings ArticleDOI
Lossy compression of images without visible distortions and its application
TL;DR: The proposed methodology of lossy compression can be successfully exploited in remote sensing and medical imaging with producing CR by several times larger than the best lossless image compression techniques.
Journal ArticleDOI
Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform
Alexander N. Zemliachenko,Ruslan Kozhemiakin,Mikhail L. Uss,Sergey K. Abramov,Nikolay N. Ponomarenko,Vladimir V. Lukin,Benoit Vozel,Kacem Chehdi +7 more
TL;DR: It is demonstrated that the compression ratio of about 15–20 can be provided for hyperspectral image compression in the neighborhood of OOP for 3-D coders, which is sufficiently larger than for component-wise compression and lossless coding.
Proceedings ArticleDOI
Estimation of accessible quality in noisy image compression
TL;DR: It is shown that this can be done in automatic mode with appropriate accuracy and the proposed approach can be applied to automatic selection of compression ratio for lossy compression of noise-free images.
References
More filters
Journal ArticleDOI
Adaptive wavelet thresholding for image denoising and compression
TL;DR: An adaptive, data-driven threshold for image denoising via wavelet soft-thresholding derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution widely used in image processing applications.
Book
MPI: The Complete Reference
TL;DR: MPI: The Complete Reference is an annotated manual for the latest 1.1 version of the standard that illuminates the more advanced and subtle features of MPI and covers such advanced issues in parallel computing and programming as true portability, deadlock, high-performance message passing, and libraries for distributed and parallel computing.
Journal Article
Data compression
D.R. Helman,Glen G. Langdon +1 more
TL;DR: The applications of digital data compression and the major components of compression systems are described and data modeling is discussed, and the role of entropy and data statistics is examined.
Book
Data compression
TL;DR: This chapter tries to achieve two purposes: its main aim is to present the principles of compressing different types of data, such as text, images, and sound, and its secondary goal is to outline the Principles of the most important compression algorithms.
Journal ArticleDOI
Lossy compression of noisy images
TL;DR: To reduce the effect of the noise on compression, the distortion is measured with respect to the original image not to the input of the coder, to design the optimal coder.