Lossless Compression Of Hyperspectral Images Research Articles

Lossless hyperspectral image compression by combining the spectral decorrelation techniques with transform coding methods

ABSTRACT 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.

Lossless Compression of Hyperspectral Imagery by Assimilating Decorrelation and Pre-processing with Efficient Displaying Using Multiscale HDR Approach

The instinctive essence of the Hyperspectral imagery cube is its immense information having both spatial and spectral correlation. In the interest of reducing the storage and bandwidth requisites, an effective lossless Hyperspectral compression system is proposed. The imagery is enforced to preprocessing stage preceding decorrelation. Preprocessing stage comprises band normalization and band ordering techniques. A technique named Greedy heap sorting is addressed to sort the bands. The proposed plan accords with a Compression ratio (CR) of 8.12 and bits per pixel (bpp) of 1.67. The performance of the system is comparable to earlier algorithms for lossless Hyperspectral image compression concerning compression ratio and bpp. An experiment conducted on AVIRIS images substantiates that the methodology presented surpasses the IP3-OBPS-BPS method by a percentage increase of 116.44 in CR and a percentage decrease of 58.49 in bpp. The multiscale High Dynamic Range (HDR) approach is used to project Hyperspectral images on devices with Low Dynamic Range (LDR) devices. The Bilateral filter is used for the decomposition of the image into multiple base layers and detail layer. The PSNR obtained is 37.4911 for the compressed HDR image, which signifies the better quality of the reconstructed image with a 4.8% compression ratio.

Lossless compression of hyperspectral images using recursive least square lattice filter group

Lossless compression of hyperspectral images using recursive least square lattice filter group

Design and Implementation of Lossless Compression System for CCSDS Hyperspectral Images

Based on the xc7k325tffg900 FPGA chip of Xilinx Company and combining the actual hyperspectral image compression task requirements, the calculating speed and compression efficiency of the algorithm module are optimized. In the design of the prediction module in this article, the two-clock solution is used to solve the problem that the algorithm speed is restricted due to the feedback of the weight update. And the RTL-level design is implemented on the FPGA. CCSDS 123.0-B-1 hyperspectral image lossless compression algorithm is achieved. The system uses block compression to deal with the data source to avoid the entire system compression data errors caused by a certain pixel compression error. So the system’s Error Resilience ability is improved. Finally the compressed code stream is decoded. The decompressed image is consistent with the raw one, which verifies the correctness of this design.

Spatially and Spectrally Concatenated Neural Networks for Efficient Lossless Compression of Hyperspectral Imagery.

To achieve efficient lossless compression of hyperspectral images, we design a concatenated neural network, which is capable of extracting both spatial and spectral correlations for accurate pixel value prediction. Unlike conventional neural network based methods in the literature, the proposed neural network functions as an adaptive filter, thereby eliminating the need for pre-training using decompressed data. To meet the demand for low-complexity onboard processing, we use a shallow network with only two hidden layers for efficient feature extraction and predictive filtering. Extensive simulations on commonly used hyperspectral datasets and the standard CCSDS test datasets show that the proposed approach attains significant improvements over several other state-of-the-art methods, including standard compressors such as ESA, CCSDS-122, and CCSDS-123.

Using Predictive and Differential Methods with K2-Raster Compact Data Structure for Hyperspectral Image Lossless Compression

This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k 2 -raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k 2 -raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding.

Lossless compression for hyperspectral image using deep recurrent neural networks

With the rapid development of hyperspectral remote sensing technology, the spatial resolution and spectral resolution of hyperspectral images are continually increasing, resulting in a continual increase in the scale of hyperspectral data. At present, hyperspectral lossless compression technology has reached a bottleneck. Simultaneously, the rise of deep learning has provided us with new ideas. Therefore, this paper examines the use of deep learning for the lossless compression of hyperspectral images. In view of the differential pulse code modulation (DPCM) method being insufficient for predicting spectral band information, the proposed method, called C-DPCM-RNN, uses a deep recurrent neural network (RNN) to improve the traditional DPCM method and improve the generalization ability and prediction accuracy of the model. The final experimental result shows that C-DPCM-RNN achieves better compression on a set of calibrated AVIRIS test images provided by the Multispectral and Hyperspectral Data Compression Working Group of the Consultative Committee for Space Data Systems in 2006. C-DPCM-RNN overcomes the limits of traditional methods in its performance on uncalibrated AVIRIS test images.

GPU Acceleration of Clustered DPCM for Lossless Compression of Hyperspectral Images

With the development of remote sensing technology, spatial and spectral resolutions of hyperspectral images have become increasingly dense. In order to overcome difficulties in the storage, transmission, and manipulation of hyperspectral images, an effective compression algorithm is requisite. The clustered differential pulse code modulation (C-DPCM), which is a prediction-based hyperspectral image lossless compression algorithm, can achieve a relatively high compression ratio, but its efficiency still requires improvement. This paper presents a parallel implementation of the C-DPCM algorithm on graphics processing units (GPUs) with the compute unified device architecture, which is a parallel computing platform and programming model developed by NVIDIA. Three optimization strategies are utilized to implement the C-DPCM algorithm in parallel, including a version that uses shared memory and registers, a version that employs multistream, and a version that uses multi-GPU. In addition, we studied how to assign all classes to each GPU to minimize the processing time. Finally, we reduced the compression time from approximately half an hour to an hour to several seconds, with almost no loss in accuracy.

New Results in Perceptually Lossless Compression of Hyperspectral Images

Hyperspectral images (HSI) have hundreds of bands, which impose heavy burden on data storage and transmission bandwidth. Quite a few compression techniques have been explored for HSI in the past decades. One high performing technique is the combination of principal component analysis (PCA) and JPEG-2000 (J2K). However, since there are several new compression codecs developed after J2K in the past 15 years, it is worthwhile to revisit this research area and investigate if there are better techniques for HSI compression. In this paper, we present some new results in HSI compression. We aim at perceptually lossless compression of HSI. Perceptually lossless means that the decompressed HSI data cube has a performance metric near 40 dBs in terms of peak-signal-to-noise ratio (PSNR) or human visual system (HVS) based metrics. The key idea is to compare several combinations of PCA and video/ image codecs. Three representative HSI data cubes were used in our studies. Four video/image codecs, including J2K, X264, X265, and Daala, have been investigated and four performance metrics were used in our comparative studies. Moreover, some alternative techniques such as video, split band, and PCA only approaches were also compared. It was observed that the combination of PCA and X264 yielded the best performance in terms of compression performance and computational complexity. In some cases, the PCA + X264 combination achieved more than 3 dBs than the PCA + J2K combination.

Superpixel based recursive least-squares method for lossless compression of hyperspectral images

Filtering based compression methods have become a popular research topic in lossless compression of hyperspectral images. Recursive least squares (RLS) based prediction methods provide better decorrelation performance among the filtering based methods. In this paper, two superpixel segmentation based RLS methods, namely SuperRLS and B-SuperRLS, are investigated for lossless compression of hyperspectral images. The proposed methods present a novel parallelization approach for RLS based prediction method. In the first step of SuperRLS, superpixel segmentation is applied to hyperspectral image. Afterwards, hyperspectral image is partitioned into multiple small regions according to the superpixel boundaries. Each region is predicted with RLS method in parallel, and prediction residuals are encoded via arithmetic encoder. Additionally, superpixel based prediction approach provides region of interest compression capability. B-SuperRLS, which is bimodal version of SuperRLS, evaluates both spectral and spatio-spectral correlations for prediction. The performance of the proposed methods are exhaustively analysed in terms of superpixel number, input vector length and number of parallel nodes, used in the prediction. Experimental results show that the proposed parallel architecture dramatically reduces the computation time, and achieves lower bit-rate performances among the state-of-the-art methods.

Lossless hyperspectral image compression using bimodal conventional recursive least-squares

ABSTRACTHyperspectral image compression is an important task, where the aim is to store or transmit data in an efficient way. Hyperspectral images are mostly captured by a sensor system that includes multiple imaging sensors covering different regions of the electromagnetic spectrum. Misalignment of multiple imaging sensors produces boresight effect, and this problem can degrade band prediction performance noticeably. Another problem is prediction of blurry band images. In order to gain robustness to these problems, bimodal conventional recursive least-squares (B-CRLS) prediction method is proposed for the lossless compression of hyperspectral images. Two prediction modes are defined: spectral and spatio-spectral. B-CRLS method has a two-step process. First, mode selection is carried out for each band. Afterwards, final band image is predicted by using the selected mode, and the residual images are encoded with an arithmetic encoder. The proposed method is compared to adaptive-length CRLS, fixed-length CRLS, and other well-known prediction methods. Experiments have been performed on uncalibrated and calibrated hyperspectral images. Obtained results show that the proposed method achieves competitive compression performance with the state-of-the-art while providing relatively lower-computation times.

Significance of pre-processing and its impact on lossless hyperspectral image compression

ABSTRACTThe primitive aspect of hyperspectral imagery is its inherent spatial and spectral correlation. This correlation is exploited by subjecting the imaging cube to compression. A new approach to accomplish lossless hyperspectral image compression has been proposed. The imaging cube is subjected to pre-processing stage prior to entropy coding. Pre-processing stage comprises band normalization, ordering of bands followed by image scanning. A new sorting technique entitled Greedy Heap Sorting is suggested. The proposed strategy yields an average compression ratio (CR) of 4.93 and average bits per pixel (bpp) of 3.08. The proficiency of the system is on par with the existing contemporary algorithms for lossless hyperspectral image compression in terms of CR, bpp and reduced complexity.

Lossless compression of hyperspectral images using conventional recursive least-squares predictor with adaptive prediction bands

An efficient lossless compression scheme for hyperspectral images using conventional recursive least-squares (CRLS) predictor with adaptive prediction bands is proposed. The proposed scheme first calculates the preliminary estimates to form the input vector of the CRLS predictor. Then the number of bands used in prediction is adaptively selected by an exhaustive search for the number that minimizes the prediction residual. Finally, after prediction, the prediction residuals are sent to an adaptive arithmetic coder. Experiments on the newer airborne visible/infrared imaging spectrometer (AVIRIS) images in the consultative committee for space data systems (CCSDS) test set show that the proposed scheme yields an average compression performance of 3.29 (bits/pixel) , 5.57 (bits/pixel) , and 2.44 (bits/pixel) on the 16-bit calibrated images, the 16-bit uncalibrated images, and the 12-bit uncalibrated images, respectively. Experimental results demonstrate that the proposed scheme obtains compression results very close to clustered differential pulse code modulation-with-adaptive-prediction-length, which achieves best lossless compression performance for AVIRIS images in the CCSDS test set, and outperforms other current state-of-the-art schemes with relatively low computation complexity.

Lossless Compression of Hyperspectral Imagery via Clustered Differential Pulse Code Modulation with Removal of Local Spectral Outliers

A high-order clustered differential pulse code modulation method with removal of local spectral outliers (C-DPCM-RLSO) is proposed for the lossless compression of hyperspectral images. By adaptively removing the local spectral outliers, the C-DPCM-RLSO method improves the prediction accuracy of the high-order regression predictor and reduces the residuals between the predicted and the original images. The experiment on a set of the NASA Airborne Visible Infrared Imaging Spectrometer (AVIRIS) test images show that the C-DPCM-RLSO method has a comparable average compression gain but a much reduced execution time as compared with the previous lossless methods.

Lossless compression of hyperspectral images with pre-byte processing and intra-bands correlation

This paper considers an approach to the compression of hyperspectral remote sensing data by an original multistage algorithm to increase the compression ratio using auxiliary data processing with its byte representation as well as with its intra-bands correlation. A set of the experimental results for the proposed approach of effectiveness estimation and its comparison with the well-known universal and specialized compression algorithms is presented.

高光谱图像的分布式近无损压缩

高光谱图像的分布式近无损压缩

Investigation into lossless hyperspectral image compression for satellite remote sensing

Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-infrared (IR), mid-IR and thermal IR portions of the spectrum, thus requiring large data storage on board the satellite and high bandwidth of the downlink transmission channel to ground stations. Image compression techniques are required to compensate for the limitations in terms of on-board storage and communication link bandwidth. In most remote-sensing applications, preservation of the original information is important and urges studies on lossless compression techniques for on-board implementation. This article first reviews hyperspectral spaceborne missions and compression techniques for hyperspectral images used on board satellites. The rest of the article investigates the suitability of the integer Karhunen–Loève transform (KLT) for lossless inter-band compression in spaceborne hyperspectral imaging payloads. Clustering and tiling strategies are employed to reduce the computational complexity of the algorithm. The integer KLT performance is evaluated through a comprehensive numerical experimentation using four airborne and four spaceborne hyperspectral datasets. In addition, an implementation of the integer KLT algorithm is ported to an embedded platform including a digital signal processor (DSP). The DSP performance results are reported and compared with the desktop implementation. The effects of clustering and tiling techniques on the compression ratio and latency are assessed for both desktop and the DSP implementation.

Lossless Compression of Hyperspectral Images Using Clustered Linear Prediction With Adaptive Prediction Length

This letter explores the use of adaptive prediction length in clustered differential pulse code modulation (C-DPCM) lossless compression method for hyperspectral images. In the C-DPCM method, linear prediction is performed using coefficients optimized for each spectral cluster separately. The difference between the predicted and original values is entropy coded using an adaptive range coder for each cluster. The results show that the C-DPCM-with-adaptive-prediction-length method has lower bit-per-pixel value than the original C-DPCM method for Consultative Committee for Space Data Systems 2006 AVIRIS test images. Both calibrated and uncalibrated image compression results are improved by adaptive prediction length.

Near lossless compression of hyperspectral images based on distributed source coding

Effective compression technique of on-board hyperspectral images has been an active topic in the field of hyperspectral remote sensintg. In order to solve the effective compression of on-board hyperspectral images, a new distributed near lossless compression algorithm based on multilevel coset codes is proposed. Due to the diverse importance of each band, a new adaptive rate allocation algorithm is proposed, which allocates rational rate for each band according to the size of weight factor defined for hyperspectral images subject to the target rate constraints. Multiband prediction is introduced for Slepian-Wolf lossless coding and an optimal quantization algorithm is presented under the correct reconstruction of Slepian-Wolf decoder, which minimizes the distortion of reconstructed hyperspectral images under the target rate. Then Slepian-Wolf encoder exploits the correlation of the quantized values to generate the final bit streams. Experimental results show that the proposed algorithm has both higher compression efficiency and lower encoder complexity than several existing classical algorithms.

Lossless compression of hyperspectral images using hybrid context prediction

In this letter a new algorithm for lossless compression of hyperspectral images using hybrid context prediction is proposed. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. The decorrelation stage supports both intraband and interband predictions. The intraband (spatial) prediction uses the median prediction model, since the median predictor is fast and efficient. The interband prediction uses hybrid context prediction. The hybrid context prediction is the combination of a linear prediction (LP) and a context prediction. Finally, the residual image of hybrid context prediction is coded by the arithmetic coding. We compare the proposed lossless compression algorithm with some of the existing algorithms for hyperspectral images such as 3D-CALIC, M-CALIC, LUT, LAIS-LUT, LUT-NN, DPCM (C-DPCM), JPEG-LS. The performance of the proposed lossless compression algorithm is evaluated. Simulation results show that our algorithm achieves high compression ratios with low complexity and computational cost.