4D remote sensing image coding with JPEG2000

Munoz-Gomez, Juan; Bartrina-Rapesta, Joan; Blanes, Ian; Jimenez-Rodriguez, Leandro; Aulí-Llinàs, Francesc; Serra-Sagristà, Joan

doi:10.1117/12.860545

Cited by 6 publications

(2 citation statements)

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“…Mamun et al proposed a 4D lossless compression algorithm in [24], albeit lacking details on the prediction algorithms. In [25], a combination of Karhunen-Loève Transform (KLT), Discrete Wavelet Transform (DWT), and JPEG 2000 was applied to reduce the spectral and temporal redundancy of 4D remote sensing image data. However, the method can only achieve lossy compression.…”

Section: Introductionmentioning

confidence: 99%

Efficient Lossless Compression of Multitemporal Hyperspectral Image Data

2018

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Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.

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Section: Introductionmentioning

confidence: 99%

Efficient Lossless Compression of Multitemporal Hyperspectral Image Data

2018

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“…Various types of wavelet transforms have been reported to analyze the geometry of 4D images [23], 4D hyperspectral images [24], 4D medical volumetric data [8,9], 4D light field data [25], and 4D color images [26]. However, most of them use the separable 4D WT that contains a large amount of rounding noise.…”

Section: Introductionmentioning

confidence: 99%

Reduction of rounding noise and lifting steps in non-separable four-dimensional quadruple lifting integer wavelet transform

Hamzah

Minewaki

Yoshida

et al. 2018

J Image Video Proc.

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The wavelet transform (WT)-based JPEG 2000 is a standard for the compression of digital images that uses a separable lifting structure in which a multidimensional image signal is transformed separately along its horizontal and vertical dimensions. A non-separable three-dimensional (3D) structure is used to minimize the number of lifting steps in existing methods and can reduce the delay between input and output as each process is implemented by cascading in lifting calculation. This structure reduces rounding noise and the number of steps of the lifting scheme in the transform. The non-separable 3D structure in the 5/3-type transform for lossless coding reduces rounding noise, but it increases in the 9/7-type transform for lossy coding in the structure. A combination of 2D and 3D non-separable structures for 4D integer WT has been proposed to solve this problem, but the original filter arrangements need to be preserved to reduce rounding noise. Therefore, in this study, a non-separable 2D structure for the integer implementation of a 4D quadruple lifting WT with a 9/7 filter is proposed. The proposed wavelet transform has the same output signal as the conventional separable structure except for the rounding noise. As the order of the original lifting scheme is preserved, rounding noise in pixels of the decoded image can be significantly reduced, and the upper bounds of quality and lossy decoded 4D medical images can be improved.

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