A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images

Predictive coding is attractive for compression onboard of spacecrafts thanks to its low computational complexity, modest memory requirements and the ability to accurately control quality on a pixel-by-pixel basis. Traditionally, predictive compression focused on the lossless and near-lossless modes of operation where the maximum error can be bounded but the rate of the compressed image is variable. Rate control is considered a challenging problem for predictive encoders due to the dependencies between quantization and prediction in the feedback loop, and the lack of a signal representation that packs the signal's energy into few coefficients. In this paper, we show that it is possible to design a rate control scheme intended for onboard implementation. In particular, we propose a general framework to select quantizers in each spatial and spectral region of an image so as to achieve the desired target rate while minimizing distortion. The rate control algorithm allows to achieve lossy, near-lossless compression, and any in-between type of compression, e.g., lossy compression with a near-lossless constraint. While this framework is independent of the specific predictor used, in order to show its performance, in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless compression standard, obtaining an extension that allows to perform lossless, near-lossless and lossy compression in a single package. We show that the rate controller has excellent performance in terms of accuracy in the output rate, rate-distortion characteristics and is extremely competitive with respect to state-of-the-art transform coding

Adaptive multispectral GPU accelerated architecture for Earth Observation satellites

In recent years the growth in quantity, diversity and capability of Earth Observation (EO) satellites, has enabled increase’s in the achievable payload data dimensionality and volume. However, the lack of equivalent advancement in downlink technology has resulted in the development of an onboard data bottleneck. This bottleneck must be alleviated in order for EO satellites to continue to efficiently provide high quality and increasing quantities of payload data. This research explores the selection and implementation of state-of-the-art multidimensional image compression algorithms and proposes a new onboard data processing architecture, to help alleviate the bottleneck and increase the data throughput of the platform. The proposed new system is based upon a backplane architecture to provide scalability with different satellite platform sizes and varying mission’s objectives. The heterogeneous nature of the architecture allows benefits of both Field Programmable Gate Array (FPGA) and Graphical Processing Unit (GPU) hardware to be leveraged for maximised data processing throughput

Research on Lossless Compression of Hyperspectral Images Based on Lookup Tables and Wiener Prediction

高光谱图像的数据量非常庞大，给数据的存储和传输带来困难，同时高光谱图像的数据非常宝贵，有损压缩会对后续应用造成无法估量的影响，因而无损压缩成为首选方案。高光谱图像同时具有空间相关和谱间相关特性，且谱间相关性远大于空间相关性，针对高光谱图像的上述特点，论文研究高光谱图像无损压缩算法，以尽可能的去除数据间的冗余性，提高高光谱数据存储与传输效率。 论文首先从相关性的角度对高光谱图像的特点进行分析，然后介绍了针对高光谱图像的一般无损压缩方法。其中基于预测的高光谱图像无损压缩算法原理简单，且易于实现，特别是针对高光谱图像谱间相关性大的特点，预测编码的算法能够高效的去除谱间冗余度，因此论文的重点是研究基...The amount of data generated by hyperspectral imaging spectrometers is enormous, storage and transmission of such huge data becomes very difficult. Hyperspectral images are important data sources, they are primarily intended for automatic analysis by computers, any distoration caused by the lossy compression will have inestimable influence on subsequent application, so lossless compression becomes...学位：工程硕士院系专业：信息科学与技术学院通信工程系_电子与通信工程学号：2332009115277

АЛГОРИТМ СЖАТИЯ ГИПЕРСПЕКТРАЛЬНЫХ ДАННЫХ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ ЗЕМЛИ

The evaluation results of hyperspectral data correlation in spatial and spectral domains are presented by the example of the hypercube AVIRIS Moffett Field, and the key features of hyperspectral data are formulated. The basic approaches to lossless compression and the algorithms, which can be applied in Earth remote sensing, are considerеd. They are the prediction (linear prediction, fast lossless, spectral oriented least squares, correlation-based conditional average prediction, M-CALIC), the lookup tables (lookup table, locally averaged interband scaling lookup tables), the 3D wavelets (3D-SPECK). A compression algorithm of hyperspectral data is proposed with regard to the advantages and disadvantages of specific implementations of the analyzed algorithms in remote sensing. The main algorithm stages are the preprocessing (for each spectral channel, it is executed independently), the reduction of a correlation level in the spectral area and the entropy coder. The test results of the developed algorithm are given in comparison to the alternative codecs on the AVIRIS test set (Cuprite, Jasper Ridge, Low Altitude, Moffet Field) that prove the efficiency of the proposed algorithm: parallel processing, low computing cost (low latency instructions are used, no division and multiplication), small random access memory requirements (the memory is used only for storage of the hypercube). In the context of the above advantages, the hardware implementation of the algorithm is allowed for on board the aircraft. Представлены результаты оценки корреляции гиперспектральных данных в пространственной и спектральной областях на примере гиперкуба AVIRIS Moffett Field. На их основе сформулированы ключевые особенности гиперспектральных данных. Приведены основные подходы к сжатию без потерь, выделены алгоритмы, относящиеся к тому или иному классу и применяемые в области дистанционного зондирования, показаны достоинства и недостатки конкретных реализаций на основе предсказания (linear prediction, fast lossless, spectral oriented least squares, correlation-based сonditional аverage рrediction, M-CALIC), поиска по таблице (lookup table, locally averaged interband scaling lookup tables) и вей- влет-преобразования (3D-SPECK). С учетом выявленных недостатков разработан алгоритм сжатия гиперспектральных данных, включающий следующие этапы обработки: предобработка (для каждого спектрального канала выполняется независимо), понижение степени корреляции в спектральной области и энтропийный кодер. Приведены результаты тестирования предложенного алгоритма в сравнении с альтернативными кодеками. В качестве тестовых данных использовались гиперкубы, входящие в тестовый набор AVIRIS (Cuprite, Jasper Ridge, Low Altitude, Moffet Field), который является общепризнанным стандартом при исследовании гиперспектральных данных. Полученные результаты свидетельствуют о соответствии разработанного алгоритма альтернативным подходам к сжатию без потерь, применяемым в дистанционном зондировании Земли. Достоинствами указанного алгоритма являются обеспечение параллельной обработки, вычислительная простота (отсутствие операций с высокой латентностью, например, умножения и деления), минимальные требования к объему оперативной памяти (память используется только для хранения гиперкуба и соответствует его объему). С учетом всего вышесказанного допускается схемотехническая реализация алгоритма на борту летательного аппарата.

Multiband and Lossless Compression of Hyperspectral Images

Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (i.e., hyperspectral sensors, etc.). We present an approach that relies on a three-dimensional predictive structure. Our predictive structure, 3D-MBLP, uses one or more previous bands as references to exploit the redundancies among the third dimension. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images