Hyperspectral sounder data is used for retrieval of atmospheric temperature, moisture and trace gases profiles, surface temperature and emissivity, cloud and aerosol optical properties. The physical retrieval of these geophysical parameters is a mathematically ill-posed problem whose solution is sensitive to the error or noise in the data. Therefore, lossless or near lossless compression of hyp...

In this study, lossless grayscale image compression methods are compared on public palmprint image databases. Effect of lossy compression algorithms on biometric samples has been well studied. However, lossless compression algorithms on the compression ratios have little been appreciated. In this study, we review and the stateofart lossless compression algorithms and investigate the performance...

We present a general purpose lossless greyscale image compression method TMW that is based on the use of linear predictors and implicit seg mentation In order to achieve competitive com pression the compression process is split into an analysis step and a coding step In the rst step a set of linear predictors and other parameters suitable for the image is calculated which is in cluded in the co...

A spectral linear prediction compression scheme for lossless compression of hyperspectral images is proposed in this paper. Since hyperspectral images have a great deal of correlation from band to band, spectral linear prediction algorithm, which utilizes information from several bands, is very efficient for compression purposes. The proposed algorithm is compared to JPEG-LS and CALIC encoding ...

The joint source-channel coding system proposed in this paper has two aims: lossless compression with a progressive mode and the integrity of medical data, which takes into account the priorities of the image and the properties of a network with no guaranteed quality of service. In this context, the use of scalable coding, locally adapted resolution (LAR) and a discrete and exact Radon transfor...

Frequently, it is observed that the sequence of indexes generated by a vector quantizer (VQ) contains a high degree of correlation, and, therefore, can be further compressed using lossless data compression techniques. In this paper, we address the problem of codebook reordering regarding the compression of the image of VQ indexes by general purpose lossless image coding methods, such as JPEG-LS...

This paper sheds light on the recent least-square (LS)-based adaptive prediction schemes for lossless compression of natural images. Our analysis shows that the superiority of the LS-based adaptation is due to its edge-directed property, which enables the predictor to adapt reasonably well from smooth regions to edge areas. Recognizing that LS-based adaptation improves the prediction mainly aro...

Most of the image compression techniques currently available were designed mainly with the aim of compressing continuous-tone natural images. However, if this assumption is not verified, such as in the case of histogram sparseness, a degradation in compression performance may occur. In this paper, we analyze the impact of histogram sparseness in three state-of-the-art lossless image compression...

Existing prediction based lossless image compression schemes perform prediction of an image data using their spatial neighborhood technique which can’t predict high-frequency image structure components, such as edges, patterns, and textures very well which will limit the image compression efficiency. To exploit these structure components, adaptive super-spatial prediction approach is developed....