Lossless hyper-spectral image compression based on XCJRCT, discrete wavelet transform and set partitioning in hierarchical trees coding

Li, Changcheng; Xie, Chengjun; Li, Shuang; Chen, Dong; Ma, Xuechao; Zhang, Hongxia

doi:10.1109/mec.2011.6025636

Cited by 3 publications

(4 citation statements)

References 1 publication

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“…The removal of redundancy is achieved by applying the spectral, spatial transform to the weak electrical signals of ginseng molecule with DWT transform as the spectral transform and CDF (2,5) wavelet transform (based on lifting scheme) as frame transform, and then the transform results are encoded. The original ginseng weak electrical signal is shown in Fig.…”

Section: The Simulation Resultsmentioning

confidence: 99%

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Lossless Compression of Weak Electrical Signal of Ginseng Molecule Based on Discrete Wavelet Transform and Siesta Program

Yin

Chen

et al. 2014

AMR

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This paper proposed the electron density of time series by using the Siesta software to calculate the weak electrical signals of ginseng molecule, combining with the lifting scheme DWT to remove ginseng molecular spatial redundancy. For the acquisition and identification of weak electrical signals of ginseng molecule in physical environment , based on the analysis of collection and identification’s principles, the noise coefficient is removed to reconstruct the signal and retain the useful signal components through applying the multi-decomposition of DWT transform to divide weak electrical signals of ginseng molecule into wavelet coefficients of different scales. The experimental results show that the multi-resolution analysis of DWT transform is performed for the weak electrical signal of ginseng molecule with different rhythms and different frequency ranges, and the weak electrical signal size of ginseng molecule before and after compression, the percentage of high frequency coefficients set to zero, and the average energy percentage after compression are, respectively, increased to 77.73%, 46.88%, and 99.99%. This algorithm operates fast enough to ease hardware implementation, providing an effective method for lossless compression of the weak electrical signals of ginseng molecule.

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Section: The Simulation Resultsmentioning

confidence: 99%

“…The transforms of S, ST, and S＋P may be seen as the special cases of Sweden's lifting scheme [5]. The process of wavelet transform based on lifting scheme may be divided into splitting, predicting, updating and optimizing:…”

Section: Lifting Scheme Of Wavelet Transformmentioning

confidence: 99%

Lossless Compression of Weak Electrical Signal of Ginseng Molecule Based on Discrete Wavelet Transform and Siesta Program

Yin

Chen

et al. 2014

AMR

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“…Wavelet (wavelet transform), a small region of the wave, is a special kind of finite length and an average of 0 waveforms. Ψ(t) A square integrable function, that areΨ(t)=L2(R), if the Fourier transform Ψ(ω) to meet the conditions [6]:…”

Section: Continuous Wavelet Functionmentioning

confidence: 99%

Plant Electrical signals based on wavelet transform and self similarity feature detection

Li¹,

Yin²,

Cai³

2015

Advances in Intelligent Systems Research

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According to the self similarity of plant electrical signal (fractal feature), changes of plant electrical signal amplitude a moment with the physical environment and mutation, causing plant electrical signal is not continuous. Electrical signal fractal characteristics of plant changes along with the time development, but at some point, it does not change with time change. This paper adopts the wavelet coefficient and self similarity relationship, through the index of self similarity calculation between plant electrical signal and wavelet to obtain the wavelet decomposition. Self similarity index is large, and plant electrical of the self similar degree are high. The simulation experiment results show that the self similarity index diagram after wavelet decomposition display can be found in many scales, the wavelet coefficients are very similar looking, providing a new idea for the detection of plant electrical signal characteristics of the physical environment.

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“…The transforms of S, ST, and S＋P may be seen as the special cases of Sweden's lifting scheme. The process of wavelet transform based on lifting scheme may be divided into splitting, predicting, updating and optimizing [7]: Splitting (Lazy wavelet or Polyphase wavelet transform): The original signal S j, k is divided into two disjoint subsets: S j+1, k and d j+1, k , and the original signal S j, k is generally split into even and odd series, that is split (S j, k ) = (S j, 2k , S j, 2k+1 ) = (S j+1, k , d j+1, k ).…”

Section: Lifting Scheme Of Wavelet Transformmentioning

confidence: 99%

Study on Plant Pest Images Identification Based on Lifting Wavelet Transform

Yin

Chen

2014

AMM

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Aiming at the weak noise signals the existence of plant diseases and insect pest images, pathological activity regulation of identification of the plant, so that between the signal molecules exist in plants can regulate mutually, collaborative work. Therefore, identification of weak signal molecules in plants is significant to the study of plant life activities. Taking corn pest images as the research object, using the identification method of lifting wavelet transform, combined with image identification technology, calculated the original plant diseases and insect pest images by not detect the break point of signal. Simulation results show that, the analysis of lifting wavelet of plant disease image identification technology reliability is about 71.65%; the accuracy of edge detection is about 76.21%. The operation speed of this algorithm is fast, easy for hardware implementation, provides an effective method for plant disease images identification.

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Lossless hyper-spectral image compression based on XCJRCT, discrete wavelet transform and set partitioning in hierarchical trees coding

Cited by 3 publications

References 1 publication

Lossless Compression of Weak Electrical Signal of Ginseng Molecule Based on Discrete Wavelet Transform and Siesta Program

Lossless Compression of Weak Electrical Signal of Ginseng Molecule Based on Discrete Wavelet Transform and Siesta Program

Plant Electrical signals based on wavelet transform and self similarity feature detection

Study on Plant Pest Images Identification Based on Lifting Wavelet Transform

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