An Efficient Compression Algorithm for Hyperspectral Images Based on Correlation Coefficients Adaptive Three Dimensional Wavelet Zerotree Coding

The data size of hyperspectral image is too large for storage and transmission, and it has become a bottleneck restricting its applications. So it is necessary to study a high efficiency compression method for hyperspectral image. Prediction encoding is easy to realize and has been studied widely in the hyperspectral image compression field. Fractal coding has the advantages of high compression ratio, resolution independence, and a fast decoding speed, but its application in the hyperspectral image compression field is not popular. In this paper, we propose a novel algorithm for hyperspectral image compression based on hybrid prediction and fractal. Intraband prediction is implemented to the first band and all the remaining bands are encoded by modified fractal coding algorithm. The proposed algorithm can effectively exploit the spectral correlation in hyperspectral image, since each range block is approximated by the domain block in the adjacent band, which is of the same size as the range block. Experimental results indicate that the proposed algorithm provides very promising performance at low bitrate. Compared to other algorithms, the encoding complexity is lower, the decoding quality has a great enhancement, and the PSNR can be increased by about 5 dB to 10 dB.

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.

Health monitoring of CNC machining processes using machine learning and wavelet packet transform

DSC的超光谱图像无损压缩算法

The compression method, first proposed in 2012, is based on the non-negative tensor decomposition for interference hyper-spectral image data. As a tensor is generated by a huge amount of interference hyper-spectral images, the multiplicative update algorithm is made extremely complicated, and even unfeasible. To reduce the computational cost and speed up the convergence, this paper, based on the characteristics of interference hyper-spectral images, develops a new algorithm using different down-sampling factors for different non-negative wavelet sub-band tensors. The experimental results showed that this algorithm could significantly shorten the running time, while maintaining a good compression performance compared with the conventional methods.

In this paper a new lossless image compression method named DPCM-IWPT is proposed. First, wavelet packets transform (WPT) and lifting scheme (LS) are described. Then an application of the LS to WPT is presented which leads to the generation of integer wavelet packets transform (IWPT). After analyzing and implementing the effect of differential pulse code modulation (DPCM) on lossless image compression, a new method combining DPCM and IWPT (DPCM-IWPT) is proposed. Comparing with IWT and IWPT, the method DPCM-IWPT shows better performance in lossless image compression.

Graphics Processing Units (GPU) are becoming a widespread tool for general-purpose scientific computing, and are attracting interest for future onboard satellite image processing payloads due to their ability to perform massively parallel computations. This paper describes the GPU implementation of an algorithm for onboard lossy hyperspectral image compression, and proposes an architecture that allows to accelerate the compression task by parallelizing it on the GPU. The selected algorithm was amenable to parallel computation owing to its block-based operation, and has been optimized here to facilitate GPU implementation incurring a negligible overhead with respect to the original single-threaded version. In particular, a parallelization strategy has been designed for both the compressor and the corresponding decompressor, which are implemented on a GPU using Nvidia's CUDA parallel architecture. Experimental results on several hyperspectral images with different spatial and spectral dimensions are presented, showing significant speed-ups with respect to a single-threaded CPU implementation. These results highlight the significant benefits of GPUs for onboard image processing, and particularly image compression, demonstrating the potential of GPUs as a future hardware platform for very high data rate instruments.

Molecular beam epitaxial growth of HgCdTe mid-wave infrared dual-band detectors

Molecular beam epitaxy (MBE) has been utilized to fabricate high performance HgCdTe infrared detectors with sensitivity to midwave infrared radiation in adjacent spectral bands for two-color thermal imaging applications. Growth of a multilayer HgCdTe device structure by MBE enables the use of an n-p-n device architecture that facilitates pixel-level registration of images in two separate spectral bands. Device structures were grown on CdZnTe(211)(B) substrates using CdTe, Te, and Hg sources with in situ In and As doping. The composition of the HgCdTe alloy layers was adjusted to achieve detection of infrared radiation in adjacent spectral bands in the 3.5–4.5 μm wavelength range. As-grown device structures were characterized with x-ray diffraction, wet chemical defect etching, and secondary ion mass spectrometry. Mesa type devices were patterned using reactive ion etching and ohmic contacts were made to the two n-type layers for operation of the detectors in a sequential detection mode. The spectral response characteristics of the devices are highly uniform across a 64×64 element array, with standard deviation in cutoff wavelength less than 0.01 μm and external quantum efficiencies greater than 70% in both bands. Sharp detector cutoffs enable spectral crosstalk less than 1% to be obtained for spectral bands with as little as 0.6 μm separation. Junction reverse-breakdown voltages in excess of 500 mV and 80 K dynamic resistance-area products for each component diode in excess of 1×106 Ω cm2 at ±100 mV operating bias have been demonstrated.

A novel method named WPTRBF is presented in this paper. This method is based on radial basis function neural network (RBFN) with wavelet packet transform (WPT) as a pre-processing tool for the simultaneous differential pulse stripping voltammetric determination of Pb(II), Ni(II) and Cd(II). Wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of noise removal can be improved. Radial basis function network was applied for overcoming the convergence problem met in back propagation training and for facilitating nonlinear calculation. In this case, through optimization, the number of wavelet function, decomposition level, the number of hidden nodes and the width (sigma) of RBFN for the WPTRBFN method were selected as Daubechies 4, 3, 8 and 0.7 respectively. The relative standard errors of prediction for all elements with WPTRBFN and RBFN were 5.8% and 6.89% respectively.

Diabetic retinopathy is the leading cause of blindness in the adult population. Mass-screening efforts, during which high resolution images of the retina are captured, are therefore underway in order to detect the disease in its early stages. In this paper we evaluate the compression performance of several lossless image compression algorithms that could be employed in a retina Picture Archiving and Communications System to lessen the demand on computing resources. The algorithms we analyse are TIFF PackBits, Lossless JPEG, JPEG-LS, and JPEG2000 all of which are incorporated in the current DICOM standard together with the non-standard CALIC algorithm for benchmark comparison. Compression performance is evaluated in terms of compression ratio, compression speed, and decompression speed. Based on a large dataset of more than 800 colour retinal images, divided into groups according to retinal region (nasal, posterior, and temporal) and image size, JPEG-LS is found to be the most suitable compression algorithm, offering good compression ratios combined with high compression and decompression speed. Compression ratios can be further improved through the application of a reversible colour space transformation prior to compression as a second set of experiments show.

Future high resolution instruments planned by CNES for space remote sensing missions will lead to higher bit rates because of the increase in resolution and dynamic range. For example, the ground resolution improvement induces a data rate multiplied by 8 from SPOT4 to SPOT5 and by 28 to PLEIADES-HR. Lossy data compression with low complexity algorithms is then needed while compression ratio must considerably rise. New image compression algorithms have been used to increase their compression performance while complying with image quality requirements from the community of users and experts. Thus, DPCM algorithm used on-board SPOT4 was replaced by a DCT-based compressor on-board SPOT5. Recent compression algorithms such as PLEIADES-HR one use wavelet-transforms and bit-plane encoders. But future compressors will have to be more powerful to reach higher compression ratios. New transforms have been studied by CNES to exceed the DWT but other techniques as selective compression are required in order to obtain a significant performance gap. This chapter gives an overview of CNES past, present and future studies of on-board compression algorithms for high-resolution images.

An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine

Image Compression plays a critical role in several important and diverse applications and has motivated the need for better image compression algorithms. Wavelets provide a powerful method for image compression; wavelet transform and wavelet packet transform have proved to be efficient and useful tools for image processing in the recent years. This paper aims at comparing different parameters of some well known wavelets at various threshold values concerning the quality of the reconstructed image. This paper attempts to compare compression performance using wavelet transform and wavelet packet transform for level 5 decomposition and highlights most desirable characteristics of wavelets.

Remote sensing Hyperspectral Image (HSI) comprises significant information about the earth’s surface which is actually acquired by hundred of narrow and adjacent spectral bands. The intended performance of classification accuracy does not attain due to the volume of the original HSI dataset and the enormous quantity of spectral bands. As such, dimensionality reduction approaches using feature extraction and selection are typically adopted to enhance classification performance. The unsupervised Principal Component Analysis (PCA), as well as the supervised Linear Discriminant Analysis (LDA), are commonly used as linear feature extraction methods for feature subspace detection. However, due to considering the effects of global variation, both PCA and LDA fail to extract local characteristics of HSI. In this paper, we propose a segmented LDA-based (SLDA) feature extraction where we apply the LDA in a segmented way to extract better local characteristics as well as global characteristics from the HSI. Per-pixel classification using a Support Vector Machine (SVM) is applied to our proposed SLDA method, PCA, Segmented-PCA (SPCA), and LDA on the Indian Pines agricultural HSI dataset. The experimental results show that the overall classification performance of SLDA (90.60%) remarkably outperforms all the other investigated methods: PCA (85.55%), SPCA (86.96%), LDA (86.45%), and the complete original dataset without employing any feature reduction method (83.10%). The proposed SLDA also requires the least amount of space complexity in different implementation scenarios.