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»)

Custom Lossless Compression and High-Quality Lossy Compression of White Blood Cell Microscopy Images for Display and Machine Learning Applications

This master's thesis investigates both custom lossless compression and high-quality lossy compression of microscopy images of white blood cells produced by CellaVision's blood analysis systems. A number of different compression strategies have been developed and evaluated, all of which are taking advantage of the specific color filter array used in the sensor in the cameras in the analysis systems. Lossless compression has been the main focus of this thesis. The lossless compression method, of those developed, that gave best result is based on a statistical autoregressive model. A model is constructed for each color channel with external information from the other color channels. The difference between the predictions from the statistical model and the original is further Huffman coded. The method achieves an average bit-rate of 3.0409 bits per pixel on the test set consisting of 604 images. The proposed lossy method is based on taking the difference between the image compressed with an ordinary lossy compression method, JPEG 2000, and the original image. The JPEG 2000 image is saved, as well as the differences at the foreground (i.e. locations with cells), in order to keep the cells identical to the cells in the original image, but allow loss of information for the, not so important, background. This method achieves a bit-rate of 2.4451 bits per pixel, with a peak signal-to-noise-ratio (PSNR) of 48.05 dB

Reversible integer KLT for progressive-to-lossless compression of multiple component images

In this paper, we presented a method for integer reversible implementation of KLT for multiple component image compression. The progressive-to-lossless compression algorithm employed the JPEG-2000 transform coding strategy using the multiple component transform (MCT) across the components, followed by a 2-dimensional wavelet transform on individual eigen images. The linear MCTs we tested and compared are KLT, discrete wavelet transform (DWT), and a tasselled cap transform (TCT) for TM satellite images only. The computational complexity of the reversible integer implementation is no more than that of naïve transformation, and the overhead data is very small. Its effectiveness was evaluated using two 6-band Landsat TM satellite images and an 80component hyper-spectral remotely-sensed image. Experiments with KLT and wavelet based JPEG-2000 show that reversible KLT (RKLT) outperforms other approaches for all of the test images in the case of both lossy and lossless compression. 1

リフティング構造を利用した非分離型ウェーブレット変換のノイズ低減に関する研究

国立大学法人長岡技術科学大

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