An Improved EZW Hyperspectral Image Compression

Cheng, Kai-Jen; Dill, J.C.

doi:10.4236/jcc.2014.22006

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…6. In contrast to the studies [1][2][3], thanks to the proposed algorithm, the best values of the compression ratio are achieved by choosing the number of channels in an ordered group, at 15>K>10 and taking into account the inter-channel correlation of the parameter, the Cog shows the highest values in the compression ratio of the channel number, at 170>N>0; -the obtained results of comparing the transformed hyperspectral AI with archivers and JPEG 2000 Lossless allow us to assert the effectiveness of the indexed conversion method (Fig. 7).…”

Section: Comparative Results Of the Dependence Of The Compression Rat...mentioning

confidence: 70%

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Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations

Sarinova

Dunayev

Bekbayeva

et al. 2022

EEJET

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The work is devoted to the description of the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery of 85 % or more, taking into account which an adaptive algorithm for compressing hyperspectral AI and the generated quantization table have been developed. The existing solutions to the lossless compression problem for hyperspectral aerospace images are analyzed. Based on them, a compression algorithm is proposed taking into account inter-channel correlation and the Walsh-Hadamard transformation, characterized by data transformation with a decrease in the range of the initial values by forming a set of channel groups [10–15] with high intra-group correlation [0.9–1] of the corresponding pairs with the selection of optimal parameters. The results obtained in the course of the research allow us to determine the optimal parameters for compression: the results of the compression ratio indicators were improved by more than 30 % with an increase in the size of the parameter channels. This is due to the fact that the more values to be converted, the fewer bits are required to store them. The best values of the compression ratio [8–12] are achieved by choosing the number of channels in an ordered group with high correlation.

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Section: Comparative Results Of the Dependence Of The Compression Rat...mentioning

confidence: 70%

“…The paper [2] presents the results of research that improved Shapiro's EZW algorithm. Hybrid transformations consist in the Karhunen-Loève Transform (KLT), which decode the spectral data of hyperspectral aerospace images, and DWT is applied to spatial data.…”

Section: Development Of Compression Algorithms For Hyperspectral Aero...mentioning

confidence: 99%

Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations

Sarinova

Dunayev

Bekbayeva

et al. 2022

EEJET

View full text Add to dashboard Cite

show abstract

“…It is a robust technique for image compression due to its multiresolution feature, scalability and flexibility. Many promising 3D image compression algorithms based on wavelet transforms were recently proposed [5][6][7][8][9][10][11][12][13][14]. In [15][16][17] authors suggest different techniques for 2D image compression method based on combining DWT and support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression

2019

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Hyperspectral imaging is known for its rich spatial-spectral information. The spectral bands provide the ability to distinguish substances spectra which is substantial for analyzing materials. However, highdimensional data volume of hyperspectal images is problematic for data storage. In this paper, we present a lossy hyperspectral image compression system based on the regression of 3D wavelet coefficients. The 3D wavelet transform is applied to sparsely represent the hyperspectral images (HSI). A support vector machine regression (SVR) is then applied on wavelet details and provides vector supports and weights which represent wavelet texture features. To achieve the best possible overall rate-distortion performance after regression, entropy encoding based on run-length encoding (RLE) and arithmetic encoding are used. To preserve the spatial pertinent information of the image, the lowest subband wavelet coefficients are furthermore encoded by a lossless coding with differential pulse code modulation (DPCM). Spectral and spatial redundancies are thus substantially reduced. Experimental tests are performed over several hyperspectral images from airborne and spaceborne sensors and compared with the main existing algorithms. The obtained results show that the proposed compression method has high performances in terms of rate-distortion and spectral fidelity. Indeed, high PSNRs and classification accuracies, which could exceed 40.65 dB and 75.8 % respectively, are observed for all decoded HSI images and overpass those given by

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“…Other popular compression algorithms for HS images using wavelets are EZW- Embedded Zero-trees Wavelet [ 28 ][ 29 ], the SPIHT ( Set Partitioning in Hierarchical Trees) [ 30 ] and the SPECK ( Set Partitioning Embedded Block) [ 31 ][ 32 ]. Modified SPIHT algorithms are also introduced for compressing multispectral images [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding

et al. 2016

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A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times the data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing “original pixel intensity”-based coding approaches using traditional image coders (e.g., JPEG2000) to the “residual”-based approaches using a video coder for better compression performance. A modified video coder is required to exploit spatial-spectral redundancy using pixel-level reflectance modelling due to the different characteristics of HS images in their spectral and shape domain of panchromatic imagery compared to traditional videos. In this paper a novel coding framework using Reflectance Prediction Modelling (RPM) in the latest video coding standard High Efficiency Video Coding (HEVC) for HS images is proposed. An HS image presents a wealth of data where every pixel is considered a vector for different spectral bands. By quantitative comparison and analysis of pixel vector distribution along spectral bands, we conclude that modelling can predict the distribution and correlation of the pixel vectors for different bands. To exploit distribution of the known pixel vector, we estimate a predicted current spectral band from the previous bands using Gaussian mixture-based modelling. The predicted band is used as the additional reference band together with the immediate previous band when we apply the HEVC. Every spectral band of an HS image is treated like it is an individual frame of a video. In this paper, we compare the proposed method with mainstream encoders. The experimental results are fully justified by three types of HS dataset with different wavelength ranges. The proposed method outperforms the existing mainstream HS encoders in terms of rate-distortion performance of HS image compression.

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An Improved EZW Hyperspectral Image Compression

Cited by 9 publications

References 11 publications

Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations

Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations

Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression

Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding

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