A fully embedded two-stage coder for hyperspectral near-lossless compression

This letter proposes a near-lossless coder for hyperspectral images. The coding technique is fully embedded and minimizes the distortion in the l2 norm initially and in the l∞ norm subsequently. Based on a two-stage near-lossless compression scheme, it includes a lossy and a near-lossless layer. The novelties are: the observation of the convergence of the entropy of the residuals in the original domain and in the spectral-spatial transformed domain; and an embedded near-lossless layer. These contributions enable a progressive transmission while optimising both SNR and PAE performance. The embeddedness is accomplished by bitplane encoding plus arithmetic encoding. Experimental results suggest that the proposed method yields a highly competitive coding performance for hyperspectral images, outperforming multi-component JPEG2000 for l∞ norm and pairing its performance for l2 norm, and also outperforming M-CALIC in the near-lossless case -for PAE ≥5-

Reversible integer approximation of color space transforms for lossless compression of big color raster data

Обратимые целочисленные преобразования имеют большое значение для алгоритмов сжатия без потерь. Для выполнения обратимой декорреляции цветовых каналов предложен алгоритм вычисления параметров обратимого целочисленного преобразования, аппроксимирующего такие непрерывные отображения, как дискретное преобразование Карунена–Лоэва. Предложен способ оценивания ошибок аппроксимации, позволяющий выбрать оптимальную аппроксимацию исходного преобразования, минимизирующую эти ошибки. На примере формата файлов MRG, предназначенного для хранения больших объёмов целочисленных растровых данных, показано, что после применения декорреляции получается повысить степень сжатия многоканальных растровых изображений при использовании алгоритма сжатия без потерь.Работа выполнена в рамках гранта № 075-15-2020-787 Министерства науки и высшего образования РФ на выполнение крупного научного проекта по при-оритетным направлениям научно-технологического развития (проект «Фундаментальные основы, методы и технологии цифрового мониторинга и прогнозиро-вания экологической обстановки Байкальской при-родной территории»)

Isorange pairwise orthogonal transform

Spectral transforms are tools commonly employed in multi- and hyperspectral data compression to decorrelate images in the spectral domain. The Pairwise Orthogonal Transform (POT) is one such transform that has been specifically devised for resource-constrained contexts like those found on board satellites or airborne sensors. Combining the POT with a 2D coder yields an efficient compressor for multi- and hyperspectral data. However, a drawback of the original POT is that its dynamic range expansion -i.e., the increase in bit depth of transformed images- is not constant, which may cause problems with hardware implementations. Additionally, the dynamic range expansion is often too large to be compatible with the current 2D standard CCSDS 122.0-B-1. This paper introduces the Isorange Pairwise Orthogonal Transform, a derived transform that has a small and limited dynamic range expansion, compatible with CCSDS 122.0-B-1 in almost all scenarios. Experimental results suggest that the proposed transform achieves lossy coding performance close to that of the original transform. For lossless coding, the original POT and the proposed isorange POT achieve virtually the same performance

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Matrix Factorizations for Reversible Integer Mapping

Reversible integer mapping is essential for lossless source coding by transformation. A general matrix factorization theory for reversible integer mapping of invertible linear transforms is developed in this paper. Concepts of the integer factor and the elementary reversible matrix (ERM) for integer mapping are introduced, and two forms of ERM---triangular ERM (TERM) and single-row ERM (SERM)---are studied. We prove that there exist some approaches to factorize a matrix into TERMs or SERMs if the transform is invertible and in a finite-dimensional space. The advantages of the integer implementations of an invertible linear transform are i) mapping integers to integers, ii) perfect reconstruction, and iii) in-place calculation. We find that besides a possible permutation matrix, the TERM factorization of an-bynonsingular matrix has at most three TERMs, and its SERM factorization has at most +1SERMs. The elementary structure of ERM transforms is the ladder structure. An executable factorization algorithm is also presented. Then, the computational complexity is compared, and some optimization approaches are proposed. The error bounds of the integer implementations are estimated as well. Finally, three ERM factorization examples of DFT, DCT, and DWT are given