Lossy Compression of Remote Sensing Images with Controllable Distortions

In this chapter, approaches to provide a desired quality of remote sensing images compressed in a lossy manner are considered. It is shown that, under certain conditions, this can be done automatically and quickly using prediction of coder performance parameters. The main parameters (metrics) are mean square error (MSE) or peak signal-to-noise ratio (PSNR) of introduced losses (distortions) although prediction of other important metrics is also possible. Having such a prediction, it becomes possible to set a quantization step of a coder in a proper manner to provide distortions of a desired level or less without compression/decompression iterations for single-channel image. It is shown that this approach can be also exploited in three-dimensional (3D) compression of multichannel images to produce a larger compression ratio (CR) for the same or less introduced distortions as for component-wise compression of multichannel data. The proposed methods are verified for test and real life images

Processing of Multichannel Remote-Sensing Images with Prediction of Performance Parameters

In processing of multichannel remote sensing data, there is a need in automation of basic operations as filtering and compression. Automation presumes undertaking a decision on expedience of image filtering. Automation also deals with obtaining of information based on which certain decisions can be undertaken or parameters of processing algorithms can be chosen. For the considered operations of denoising and lossy compression, it is shown that their basic performance characteristics can be quite easily predicted based on easily calculated local statistics in discrete cosine transform (DCT) domain. The described methodology of prediction is shown to be general and applicable to different types of noise under condition that its basic characteristics are known in advance or pre-estimated accurately

Automatic Adaptive Lossy Compression of Multichannel Remote Sensing Images

In this chapter, we consider lossy compression of multichannel images acquired by remote sensing systems. Two main features of such data are taken into account. First, images contain inherent noise that can be of different intensity and type. Second, there can be essential correlation between component images. These features can be exploited in 3D compression that is demonstrated to be more efficient than component-wise compression. The benefits are in considerably higher compression ratio attained for the same or even less distortions introduced. It is shown that important performance parameters of lossy compression can be rather easily and accurately predicted

Lossy Compression of Hyperspectral Images Based on JPEG2000

International audienceLossy compression of images is used in different applications including remote sensing. A problem is how to carry out such lossy compression automatically with providing appropriate quality. This paper deals with considering peculiarities of using JPEG2000-based coders for compressing hyperspectral data. Two approaches component-wise and 3D are analyzed. It is demonstrated that 3D compression provides better results but both approaches are characterized by the same drawback it is problematic to provide distortions less or comparable with noise in hyperspectral images

Prediction of compression ratio for ADCT coder

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Lossy compression of noisy remote sensing images with prediction of optimal operation point existence and parameters

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Compression Ratio Prediction in Lossy Compression of Noisy Images

International audienceOur paper addresses a question of prediction compression ratio in lossy compression of remote sensing images by coders based on discrete cosine transform (DCT) taking into account noise present in these images. Quantization step is set fixed and proportional to noise standard deviation to provide compression in optimal operation point if it exists. Simple statistics of DCT coefficients is used for predicting compression ratio. Prediction dependences are obtained off-line (in advance) and they occur to be quite simple and accurate. The influence of DCT statistics on prediction efficiency is analyzed. Accuracy of prediction is studied for real-life hyperspectral data compressed component-wis

Prediction of Compression Ratio for DCT-Based Coders With Application to Remote Sensing Images

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VST-based Lossy Compression of Hyperspectral Data for New Generation Sensors

International audienceThis paper addresses lossy compression of hyperspectral images acquired by sensors of new generation for which signal-dependent component of the noise is prevailing compared to the noise-independent component. First, for sub-band (component-wise) compression, it is shown that there can exist an optimal operation point (OOP) for which MSE between compressed and noise-free image is minimal, i.e., maximal noise filtering effect is observed. This OOP can be observed for two approaches to lossy compression where the first one presumes direct application of a coder to original data and the second approach deals with applying direct and inverse variance stabilizing transform (VST). Second, it is demonstrated that the second approach is preferable since it usually provides slightly smaller MSE and slightly larger compression ratio (CR) in OOP. One more advantage of the second approach is that the coder parameter that controls CR can be set fixed for all sub-band images. Moreover, CR can be considerably (approximately twice) increased if sub-band images after VST are grouped and lossy compression is applied to a first sub-band image in a group and to "difference" images obtained for this group. The proposed approach is tested for Hyperion hyperspectral images and shown to provide CR about 15 for data compression in the neighborhood of OOP