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

Abstract

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.

Is Texture Denoising Efficiency Predictable?

“…It is di±cult to thoroughly study all these subtasks within the scope of a paper. Earlier studies 33,38,51 have shown the following. Firstly, there exist quite simple statistics of DCT coe±cients which can serve as good input parameters.…”

“…Secondly, it has been already demonstrated that IPSNR for many¯lters can be predicted quite accurately for DCT-based¯lters 1,33,38,51 and some other denoising techniques. 34 Accuracy can be characterized di®erently where the root mean square error (RMSE) of estimates is one of the most adequate and widely used quantitative criteria.…”

“…Several ways to improve the prediction (to increase R 2 and to reduce¯tting RMSE) have been already proposed. They presume¯nding a better input parameter, 33 search and employment of a better approximation function, 51 and usage of two or more input parameters 38 aggregated in one or another way. In the latter case, di®erent statistics of local (calculated in analyzed blocks) probabilities have been employed: mean, median, variance, skewness, kurtosis.…”

“…However, the prediction is considerably less accurate for visual quality metrics. There exist methods to improve this accuracy 33,38,51 that, in particular, deal with a joint use of two or more input parameters. But, these options have not been tested for texture image denoising yet.…”