GPU lossless hyperspectral data compression system for space applications

Nonnegative tensor Tucker decomposition (NTD) in a transform domain (e.g., 2D-DWT, etc) has been used in the compression of hyper-spectral images because it can remove redundancies between spectrum bands and also exploit spatial correlations of each band. However, the use of a NTD has a very high computational cost. In this paper, we propose a low complexity NTD-based compression method of hyper-spectral images. This method is based on a pair-wise multilevel grouping approach for the NTD to overcome its high computational cost. The proposed method has a low complexity under a slight decrease of the coding performance compared to conventional NTD. We experimentally confirm this method, which indicates that this method has the less processing time and keeps a better coding performance than the case that the NTD is not used. The proposed approach has a potential application in the loss compression of hyper-spectral or multi-spectral images

There has recently been a strong interest towards low-complexity approaches for hyperspectral image compression, also driven by the standardization activities in this area and by the new hyperspectral missions that have been deployed. This chapter overviews the state-of-the-art of lossless and near-lossless compression of hyperspectral images, with a particular focus on approaches that comply with the requirements typical of real-world mission, in terms of low complexity and memory usage, error resilience and hardware friendliness. In particular, a very simple lossless compression algorithm is described, which is based on block-by-block prediction and adaptive Golomb coding, can exploit optimal band ordering, and can be extended to near-lossless compression. We also describe the results obtained with a hardware implementation of the algorithm. The compression performance of this algorithm is close to the state-of-the-art, and its low degree of complexity and memory usage, along with the possibility to compress data in parallel, make it a very good candidate for onboard hyperspectral image compression.

Algorithms for compression of hyperspectral data are commonly evaluated on a readily available collection of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. The calibrated images collected in 1997 show sample value distributions which contain artificial regularities introduced by the conversion of raw data values to radiance units. Being optimal on images having flat histograms, classical DPCM methods do not work in their best conditions. Conversely, the lower lossless bit rates are achieved by algorithms based on lookup-table (LUT) that significantly exploit these artifacts. This singular behavior has not been widely reported and may not be widely recognized. The main consequence is these performances can be misleading if they are extrapolated to images that lack such artifacts. In fact, LUT-based algorithms are not able to achieve the best compression performances on a set of more recent (2006) AVIRIS images that do not contain appreciable calibration-induced artifacts. This is due to a different scaling factor in the calibration procedure. Goal of this paper is to provide a thorough comparison of advanced classical DPCM and LUT-based methods both on the 1997 and the 2006 AVIRIS datasets. In the 2006 data set, both calibrated data (radiances) and raw data (digital counts) have been compressed. Results strengthen the conclusion that even the most developed LUT-based methods do not show improvements over the state of the art when calibration induced artifacts are missing. Concerning classical DPCMs, the methods based on a classified spectral prediction, whose idea was originally developed by the authors in 2001, provide the best compression results.

As scientists endeavor to learn more about the world's ecosystems, engineers are pushed to develop more sophisticated instruments. With these advancements comes an increase in the amount of data generated. For satellite based instruments the additional data requires sufficient bandwidth be available to transmit the data. Alternatively, compression algorithms can be employed to reduce the bandwidth requirements. This work is motivated by the proposed HyspIRI mission, which includes two imaging spectrometers measuring from visible to short wave infrared (VSWIR) and thermal infrared (TIR) that saturate the projected bandwidth allocations. We present a novel investigation into the capability of using FPGAs integrated with embedded PowerPC processors to adequately perform the predictor function of the Fast Lossless (FL) compression algorithm for multispectral and hyperspectral imagery. Furthermore, our design includes a multi-PowerPC implementation which incorporates recently developed Radiation Hardening by Software (RHBSW) techniques to provide software-based fault tolerance to commercial FPGA devices. Our results show low performance overhead (4–8%) while achieving a speedup of 1.97× when utilizing both PowerPCs. Finally, the evaluation of the proposed system includes resource utilization, performance metrics, and an analysis of the vulnerability to Single Event Upsets (SEU) through the use of a hardware based fault injector.

This study explores the utilization of Binary Embedded Zero Tree Wavelet Algorithms (BEZW) to compress hyperspectral images. The primary goal is to enhance the representation of spectral data while minimizing storage and transmission requirements. The BEZW algorithm employs wavelet transformations to leverage both spectral and spatial redundancies found in hyperspectral data cubes. Its embedded zero tree structure ensures efficient encoding of small coefficients with minimal overhead. The study evaluates the performance of the BEZW method utilizing various hyper-spectral datasets and compression settings, comparing it to other compression techniques. The results illustrate that the BEZW algorithm offers a promising approach to hyperspectral image compression, achieving competitive compression ratios while preserving spectral accuracy. This makes it a valuable option for applications where efficient hyperspectral data storage and transmission are crucial. The research contributes to the field of hyperspectral imaging by introducing an effective compression method that enhances data accessibility, simplifying the utilization of hyperspectral data in resource-constrained environments.

In order to improve the detection and recognition ability of multiscale hyperspectral images with weak edges, a lossless compression algorithm of multiscale hyperspectral images with weak edges based on improved deep learning is proposed. Using deep-level feature information extraction method to extract fine-grained features of cancer cells from multiscale hyperspectral images with weak edges, constructing 3D surface structure reconstruction model of multiscale hyperspectral images with weak edges, combining with variational Bayesian filter detection method to decompose multiscale color structure features of multiscale hyperspectral images with weak edges, and constructing gradually refined output structure model of multiscale hyperspectral images with weak edges, Fuzzy image coarse-scale neural network learning method is used to realize block matching and edge detection of multi-scale hyperspectral images with weak edges. Ultra-fine-grained features of multi-scale hyperspectral images with weak edges are extracted. Deep learning algorithm is used to adaptively optimize the lossless compression process of multi-scale hyperspectral images with weak edges, and the optimal design of lossless compression algorithm for multi-scale hyperspectral images with weak edges is realized. The simulation results show that the adaptive convergence of lossless compression of multi-scale weak edge hyperspectral images with this method is good, the feature resolution is strong, and the hyperspectral feature detection and recognition ability is good.

본 논문에서는 고해상도 영상의 효과적인 처리를 위한 블록 버퍼 기반의 저 복잡도 무손실 프레임 메모리 (frame memory) 압축 방법을 제안한다. 제안하는 압축 방법은 공간적 상관도를 제거하기 위하여 블록단위 MHT (modified Hadamard transform)를 사용하고, 엔트로피 부호화를 위하여 AGR (adaptive Golomb-Rice) 부호화 기법을 적용하여 저 복잡도 무손실 압축 및 효과적인 하드웨어 구현을 달성한다. MHT는 가산기와 1비트 오른쪽 시프트(1-bit right shift) 연산만으로 구성되어 있고, AGR은 별도의 메모리 공간 및 메모리 접근 동작(memory access operation)을 포함하지 않아 저 복잡도 구현이 용이하다. 기존의 저 복잡도 무손실 압축 방법과 비교하여 제안한 알고리즘은 압축률 측면에서 우수한 성능을 나타내고, 기존 코덱(codec)의 구조를 크게 수정하지 않으면서 화질의 열화없이 하드웨어 장치에 적용될 수 있음을 다양한 영상에 대한 실험 및 복잡도 분석을 통해 보인다. 또한 제안한 방법은 메모리 접근 동작을 필요로 하지 않아 하드웨어 구현을 위한 비용을 최소화 할 수 있어, Fill HD급 이상의 고해상도 영상을 효과적으로 처리하는데 유용하다. This study addresses a low complexity and lossless frame memory compression algorithm based on block-buffer structure for efficient high resolution video processing. Our study utilizes the block-based MHT (modified Hadamard transform) for spatial decorrelation and AGR (adaptive Golomb-Rice) coding as an entropy encoding stage to achieve lossless image compression with low complexity and efficient hardware implementation. The MHT contains only adders and 1-bit shift operators. As a result of AGR not requiring additional memory space and memory access operations, AGR is effective for low complexity development. Comprehensive experiments and computational complexity analysis demonstrate that the proposed algorithm accomplishes superior compression performance relative to existing methods, and can be applied to hardware devices without image quality degradation as well as negligible modification of the existing codec structure. Moreover, the proposed method does not require the memory access operation, and thus it can reduce costs for hardware implementation and can be useful for processing high resolution video over Full HD.

This paper describes an algorithm for creating pseudo stereo image generation (2D-to-3D conversion) and its real-time hardware implementation. The proposed method is developed to fulfill the growing need for high quality generic 3D contents. The algorithm intends to approximate 3D structures of natural scenes and to generate visually pleasing stereo images. The depth data is generated with simple models and by warm/cool color theories. The algorithm has advantages in picture quality, low numerical complexity, and no need of tuning of parameters for different types of pictures. The hardware implemented with the simple algorithm does not need any large memory such as frame memory and gives very low latency. This technology had attracted many people's attentions at demonstrations including CEATEC 2008.

Receiver architectures for a computationally simple image-compression scheme based on an explicit feature-visibility criterion are discussed. The compression algorithms use the feature-visibility criterion to remove image detail not visible under normal viewing conditions. The algorithms are of low complexity, use integer arithmetic operations, and are well suited to simple real-time implementation in hardware. The algorithms break the image into square regions of uniform intensity. A lossless code has been developed that efficiently encodes the region size and organization information. The receiver stages used to decode the lossless code, to reconstruct the compressed image, and to postfilter the image for smoothing are described.

A parallel implementation of sequential minimal optimization on FPGA

Remotely sensed hyperspectral imagery has vast data volume, for which data compression is a necessary processing step. Spectral decorrelation is critical to successful hyperspectral-image compression. Principal component analysis (PCA) is well-known for its superior performance in data decorrelation, and it has been demonstrated that using PCA for spectral decorrelation can yield rate-distortion and data-analysis performance superior to other widely used approaches, such as the discrete wavelet transform (DWT). However, PCA is a data-dependent transform, and its complicated implementation in hardware hinders its use in practice. In this paper, schemes for low-complexity PCA are discussed, including spatial down-sampling, the use of non-zero mean data, and the adoption of a simple PCA neural-network. System-design issues are also investigated. Experimental results focused on the fidelity of pixel values and pixel spectral signatures demonstrate that the proposed schemes achieve a trade-off between compression performance and system-design complexity.

A low complexity block-based adaptive lossless image compression

An efficient compression of hyperspectral images on-board satellites is mandatory in current and future space missions in order to save bandwidth and storage space. Reducing the data volume in space is a challenge that has been faced with a twofold approach: to propose new highly efficient compression algorithms; and to present technologies and strategies to execute the compression in the hardware available on-board. The Consultative Committee for Space Data Systems (CCSDS), a consortium of the major space agencies in the world, has recently issued the CCSDS 123 standard for multispectral and hyperspectral image (MHI) compression, with the aim of facilitating the inclusion of on-board compression on satellites by the space industry. In this paper, we present a low-complexity feld programmable gate arrays (FPGAs) implementation of this recent CCSDS 123 standard, which demonstrates its main features in terms of compression efficiency and suitability for an implementation on the available on-board technologies. A hardware architecture is conceived and designed with the aim of achieving low hardware occupancy and high performance on a space-qualified FPGA from the Microsemi RTAX family. The resulting FPGA implementation is therefore suitable for on-board compression. The effect of the several CCSDS-123 configuration parameters on the compression efficiency and hardware complexity is taken into consideration to provide flexibility in such a way that the implementation can be adapted to different application scenarios. Synthesis results show a very low occupancy of 34% and a maximum frequency of 43 MHz on a space-qualified RTAX1000S. The benefits of the proposed implementation are further evidenced by a demonstrator, which is implemented on a commercial prototyping board from Xilinx. Finally, a comparison with other FPGA implementations of on-board data compression algorithms is provided.

The use of next-generation and high-resolution imaging sensors is gaining interest for space missions, because of their properties for identification and exploration purposes. It is expected that the demand for video sensors in the space industry will increase during the next years, mainly for monitoring and exploration missions. In this context, onboard video compression techniques emerge as mandatory, because memory resources are available on space missions and the downlink bandwidth with ground prevents for the transmission of raw video, especially when near real-time capabilities are required. The complexity of the current commercial video encoders supposes a challenge for their implementation on the hardware used on space missions, because of their high architectural complexity and computational requirements, deriving in an area overhead and an unacceptable power consumption. In this context, we propose the use of the CCSDS-123.0-B-2 standard, originally focused on the near-lossless on-board compression of multispectral and hyperspectral images, for compressing panchromatic video. The proposed solution presents low complexity, because it is specifically designed for working on space missions and also has the capability of compressing both 3-D images and panchromatic video by using a single processing core. Results demonstrate that this solution achieves high compression ratios, maintaining a considerable video quality after reconstruction on ground and achieving a fine-grain control of the losses introduced in the compression chain.

Hyperspectral (HS) image is a three dimensional data image where the 3rd dimension carries the wealth of spectrum information. HS image compression is one of the areas that has attracted increasing attention for big data processing and analysis. HS data has its own distinguishing feature which differs with video because without motion, also different with a still image because of redundancy along the wavelength axis. The prediction based method is playing an important role in the compression and research area. Reflectance distribution of HS based on our analysis indicates that there is some nonlinear relationship in intra-band. The Multilayer Propagation Neural Networks (MLPNN) with backpropagation training are particularly well suited for addressing the approximation function. In this paper, an MLPNN based predictive image compression method is presented. We propose a hybrid Adaptive Prediction Mechanism (APM) with MLPNN model (APM-MLPNN). MLPNN is trained to predict the succeeding bands by using current band information. The purpose is to explore whether MLPNN can provide better image compression results in HS images. Besides, it uses less computation cost than a deep learning model so we can easily validate the model. We encoded the weights vector and the bias vector of MLPNN as well as the residuals. That is the only few bytes it then sends to the decoder side. The decoder will reconstruct a band by using the same structure of the network. We call it an MLPNN decoder. The MLPNN decoder does not need to be trained as the weights and biases have already been transmitted. We can easily reconstruct the succeeding bands by the MLPNN decoder. APM constrained the correction offset between the succeeding band and the current spectral band in order to prevent HS image being affected by large predictive biases. The performance of the proposed algorithm is verified by several HS images from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) reflectance dataset. MLPNN simulation results can improve prediction accuracy; reduce residual of intra-band with high compression ratio and relatively lower bitrates.