The CCSDS 123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression Standard: A comprehensive review

The Consultative Committee for Space Data Systems (CCSDS) published the CCSDS 123.0-B-2, “Low- Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression” standard. This standard extends the previous issue, CCSDS 123.0-B-1, which supported only lossless compression, while maintaining backward compatibility. The main novelty of the new issue is support for near-lossless compression, i.e., lossy compression with user-defined absolute and/or relative error limits in the reconstructed images. This new feature is achieved via closed-loop quantization of prediction errors. Two further additions arise from the new near lossless support: first, the calculation of predicted sample values using sample representatives that may not be equal to the reconstructed sample values, and, second, a new hybrid entropy coder designed to provide enhanced compression performance for low-entropy data, prevalent when non lossless compression is used. These new features enable significantly smaller compressed data volumes than those achievable with CCSDS 123.0-B-1 while controlling the quality of the decompressed images. As a result, larger amounts of valuable information can be retrieved given a set of bandwidth and energy consumption constraints

Técnicas de compresión de imágenes hiperespectrales sobre hardware reconfigurable

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 18-12-2020Sensors are nowadays in all aspects of human life. When possible, sensors are used remotely. This is less intrusive, avoids interferces in the measuring process, and more convenient for the scientist. One of the most recurrent concerns in the last decades has been sustainability of the planet, and how the changes it is facing can be monitored. Remote sensing of the earth has seen an explosion in activity, with satellites now being launched on a weekly basis to perform remote analysis of the earth, and planes surveying vast areas for closer analysis...Los sensores aparecen hoy en día en todos los aspectos de nuestra vida. Cuando es posible, de manera remota. Esto es menos intrusivo, evita interferencias en el proceso de medida, y además facilita el trabajo científico. Una de las preocupaciones recurrentes en las últimas décadas ha sido la sotenibilidad del planeta, y cómo menitoirzar los cambios a los que se enfrenta. Los estudios remotos de la tierra han visto un gran crecimiento, con satélites lanzados semanalmente para analizar la superficie, y aviones sobrevolando grades áreas para análisis más precisos...Fac. de InformáticaTRUEunpu

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

Lossy Multi/Hyperspectral Compression HW Implementation at high data rate

Image compression is becoming more and more important, as new multispectral and hyperspectral instruments are going to generate very high data rates due to the increased spatial and spectral resolutions. Transmitting all the acquired data to the ground segment is a serious bottleneck, and compression techniques are a feasible solution to this problem. The CCSDS has established a working group (WG) on multispectral and Hyperspectral Data Compression (MHDC), which has the purpose of standardizing compression techniques to be used onboard. The WG has already standardized a lossless compression algorithm for multispectral and hyperspectral images, and has started working on a lossy compression algorithm. The complexity of lossless compression algorithms is typically larger than that of lossy ones, leading to potentially lower throughputs. Therefore, a careful assessment is required in order to identify techniques that are able to sustain very high data rates. The increased complexity can also lead to increased resource occupancy on a hardware device such as an FPGA. Lossy compression introduces information losses in the images, and these losses must be accurately characterized, and their effect on the applications investigated. For these reasons, developing a lossy algorithm requires a more elaborate process. Under an ESA contract primed by Politecnico of Torino, TSD is currently designing an IP core for FPGA and/or ASIC implementation of a lossy compression algorithm that is being proposed for CCSDS standardization. In addition to the IP core, TSD is developing a HW platform based on the Xilinx Virtex-5 XQR5VFX130, the industry's first high performance rad-hard reconfigurable FPGA for processing-intensive for space systems. Advanced results along with details of electronic platform design will be presented in this paper

Compression algorithm and implementation for the PRISMA mission

In this paper we describe the image compression algorithm and its implementation to be used for the PRISMA mission of the Italian Space Agency. The mission payload includes a pushbroom hyperspectral instrument as well as a medium resolution panchromatic camera

An Hardware Implementation of a Novel Algorithm For Onboard Compression of Multispectral and Hyperspectral Images

New multispectral and hyperspectral instruments are going to generate very high data rates due to the increased spatial and spectral resolution. In this context, the compression is a very important part of any onboard data processing system for Earth observation and astronomical missions. More recently, lossless compression has started to be routinely used for spaceborne Earth observation satellites. The CCSDS has established a working group (WG) on Multispectral and Hyperspectral Data Compression (MHDC), which has the purpose of standardizing compression techniques to be used onboard. The WG has already standardized a lossless compression algorithm for multispectral and hyperspectral images, and has started working on a lossy compression algorithm. Under an ESA contract, aimed to investigate new techniques for Lossy multi/hyperspectral compression for very high data rate instruments (HYDRA), TSD in collaboration with Politecnico of Torino, designed an IP core for FPGA and/or ASIC implementation of a lossy compression algorithm. In addition to the IP core, TSD developed a HW platform based on the Xilinx Virtex-5 XQR5VFX130, the industry's first high performance rad-hard reconfigurable FPGA for processing-intensive for space systems. Advanced results along with details of electronic platform design will be presented in this paper

Reducing data dependencies in the feedback loop of the CCSDS 123.0-B-2 predictor

Altres ajuts: European Space Agency (ESA) (Grant Number: 4000136723/22/NL/CRS)On-board multi- and hyperspectral instruments acquire large volumes of data that need to be processed with the limited computational and storage resources. In this context, the CCSDS 123.0-B-2 standard emerges as an interesting option to compress multi- and hyperspectral images on-board satellites, supporting both lossless and near-lossless compression with low complexity and reduced power consumption. Nonetheless, the inclusion of a feedback loop in the CCSDS 123.0-B-2 predictor to support near-lossless compression introduces significant data dependencies that hinder real-time processing, particularly due to the presence of a quantization stage within this loop. This work provides an analysis of the aforementioned data dependencies and proposes two strategies aiming at maximizing throughput in hardware implementations and thus enabling real-time processing. In particular, through an elaborate mathematical derivation, the quantization stage is removed completely from the feedback loop. This reduces the critical path, which allows for shorter initiation intervals in a pipelined hardware implementation and higher throughput. This is achieved without any impact in the compression performance, which is identical to the one obtained by the original data flow of the predictor

Digital FPGA Circuits Design for Real-Time Video Processing with Reference to Two Application Scenarios

In the present days of digital revolution, image and/or video processing has become a ubiquitous task: from mobile devices to special environments, the need for a real-time approach is everyday more and more evident. Whatever the reason, either for user experience in recreational or internet-based applications or for safety related timeliness in hard-real-time scenarios, the exploration of technologies and techniques which allow for this requirement to be satisfied is a crucial point. General purpose CPU or GPU software implementations of these applications are quite simple and widespread, but commonly do not allow high performance because of the high layering that separates high level languages and libraries, which enforce complicated procedures and algorithms, from the base architecture of the CPUs that offers only limited and basic (although rapidly executed) arithmetic operations. The most practised approach nowadays is based on the use of Very-Large-Scale Integrated (VLSI) digital electronic circuits. Field Programmable Gate Arrays (FPGAs) are integrated digital circuits designed to be configured after manufacturing, "on the field". They typically provide lower performance levels when compared to Application Specific Integrated Circuits (ASICs), but at a lower cost, especially when dealing with limited production volumes. Of course, on-the-field programmability itself (and re-programmability, in the vast majority of cases) is also a characteristic feature that makes FPGA more suitable for applications with changing specifications where an update of capabilities may be a desirable benefit. Moreover, the time needed to fulfill the design cycle for FPGA-based circuits (including of course testing and debug speed) is much reduced when compared to the design flow and time-to-market of ASICs. In this thesis work, we will see (Chapter 1) some common problems and strategies involved with the use of FPGAs and FPGA-based systems for Real Time Image Processing and Real Time Video Processing (in the following alsoindicated interchangeably with the acronym RTVP); we will then focus, in particular, on two applications. Firstly, Chapter 2 will cover the implementation of a novel algorithm for Visual Search, known as CDVS, which has been recently standardised as part of the MPEG-7 standard. Visual search is an emerging field in mobile applications which is rapidly becoming ubiquitous. However, typically, algorithms for this kind of applications are connected with a high leverage on computational power and complex elaborations: as a consequence, implementation efficiency is a crucial point, and this generally results in the need for custom designed hardware. Chapter 3 will cover the implementation of an algorithm for the compression of hyperspectral images which is bit-true compatible with the CCSDS-123.0 standard algorithm. Hyperspectral images are three dimensional matrices in which each 2D plane represents the image, as captured by the sensor, in a given spectral band: their size may range from several millions of pixels up to billions of pixels. Typical scenarios of use of hyperspectral images include airborne and satellite-borne remote sensing. As a consequence, major concerns are the limitedness of both processing power and communication links bandwidth: thus, a proper compression algorithm, as well as the efficiency of its implementation, is crucial. In both cases we will first of all examine the scope of the work with reference to current state-of-the-art. We will then see the proposed implementations in their main characteristics and, to conclude, we will consider the primary experimental results

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

High-Level Synthesis of a Single/Multi-Band Optical and SAR Image Compression and Encryption Hardware Accelerator

Transmitting images from earth observation satellites to ground is a major challenge, and a compression/encryption stage is actually mandatory. Development of hardware accelerators is highly recommended, both to relieve the software from such demanding task, and to improve performance, aiming at quasi-real-time data processing. To this end, we discuss the design, development, deployment and test of a FPGA-based accelerator, featuring a lossless and lossy (near-lossless) compression, including the data encryption too. Its architecture is well suited for different image types, including single- and multi-band optical and SAR images and can be fully run-time configurable. Measured performance showed a throughput of 10 Msamples/s, in agreement with related state-of-the-art works, focused on lossless compression only