The signature of hyperspectral image (HSI) pixels and their decomposition into empirical modes (EM) and low-frequency residuals are investigated. On the basis of estimates related to the EM-decomposition method, the possibility of switching from a 2-byte representation of the values of the HIS-signature to a 1-byte one is examined using the example of the Moffett Field from the AVIRIS spectrometer. It is revealed that the localization of the minimum window sizes for the first EM is correlated with the localization of the significant influence of the atmosphere; the first low-frequency residues have a fairly high correlation coefficient with the signature and the first 2 of them and their EM are most interesting for use; 50 of the 224 HIS-channels are noisy and can be excluded from consideration; EM with practically no loss of accuracy can be reduced to a 1-byte representation. The management of the classification capabilities of signatures by changing the threshold value of the correlation coefficient with the sample, as well as the application of the 1st and 2nd low-frequency residues in place of the signature, was studied. Classification capabilities of signatures in a 1byte representation are almost equivalent to a 2-byte one, which makes it possible to put a signature with 1-byte representation as the object of compression. For the wavelet decomposition of the HSI data array, in combination with a 1-byte representation, a nearlossless compression ratio of 6.65 is obtained.
Abstract. The paper deals with the problem of the enhancement of the operating speed of the algorithm of adaptive compression of binary bitmap images (BBI) on the basis of entropy coding using context simulation. The influence of the size of the maximal order context on the compression rate is considered. The enhancement of the speed of the algorithm depending on the number of the threads used is considered.
Keywords: compression, optimization, binary images, context-based modelingCitation: Borusyak A.V. The enhancement of the operating speed of the algorithm of adaptive compression of binary bitmap images.
We consider the problem of compression of RGB and multispectral images by context-based methods. The algorithm' logic allows for its examination by using the example of full-color images as a particular case of multispectral images. The image-forming channels are divided into two groups: main and additional (detecting) channels. A distinguishing feature of the main channels is a significant correlation between neighbors. A number of variants of prediction from the adjacent channel for the main and additional channels for lossless image compression were considered. In the experiment on a series of images of different contents, the proposed algorithm showed a superior compression ratio in comparison with the popular WinRar, 7z, PNG archivers for all prediction variants. The leader among popular compression methods, JPEG-LS, was surpassed in the record configuration 2b on the image from the Landsat series by 40%. We expect to continue research on a wider sample of images and to use this algorithm to compress multispectral images with a greater number of channels.
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