Visual quality is important for remote sensing data presented as grayscale, color or pseudo-color images. Although several visual quality metrics (VQMs) have been used to characterize such data, only a limited analysis of their applicability in remote sensing applications has been done so far. In this paper, we study correlation factors for a wide set of VQMs for color images with distortion types typical for remote sensing. It is demonstrated that there are many metrics that have very high Spearman rank order correlation, e.g. PSNR-based and SSIM-based metrics. Meanwhile, there are also metrics that are practically uncorrelated with others. A detailed analysis of VQMs that have the largest SROCC values and belong to different groups is presented in this paper.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Washingtonpost.Newsweek Interactive, LLC is collaborating with JSTOR to digitize, preserve and extend access to Foreign Policy. Russia is justly known as a country of paradoxes, which, in the words of poet Fyodor Tyutchev, cannot be "measured by a common rule." That is true even for such seemingly universal notions as the time and space in which our country exists. By time, I simply mean the level of historical development a country has attained; by space, I mean its geopolitical position. For most countries, those notions are clearly defined, having been well established by history.But Russia is different. It simultaneously straddles two continents and several historical epochs. Some elements of Russian society have clearly advanced to the post-industrial era, while others are stuck in the industrialism of the first five-year plans, or even in the backwardness of the Middle Ages. That is why it is so difficult to place Russia at any single level of development: Should it be seen as a developed, a developing, or simply a backward country?As a result of such historical heterogeneity, Russia faces many problems whose solutions complicate, or even contradict, yet other problems. That slows the overall movement toward an economically healthy liberal democracy. For instance, in the economic sphere we have to speed up industrial development even though we are already choked by the problems of postindustrialism and especially by those of Sovietstyle industrialization. Its legacy is a superabundance of nuclear weapons, a disastrous ecological situation, and a highly unsafe nuclear energy industry.In the social sphere, we are faced with the VLADIMIR P. LUKIN, ambassador of the Russian Feder-

Noise parameters estimation is required in various stages of digital image processing. Many efficient algorithms of noise estimation were proposed during last two decades. However, most of these algorithms are efficient only for a specific type of noise for which they are designed. For example, methods of variance estimation of additive white Gaussian noise (AWGN) will not work in the case of additive colored Gaussian noise (ACGN) or, in general, in the case of a noise with non AWGN distribution. In this paper, a totally blind method of noise level estimation is proposed. For a given image, a distorted image with a discarded portion of pixels (around 10%) is generated. Then an inpainting (or impulse noise removal) method is applied to recover those discarded pixels values. The difference between the true and recovered pixel values is used to robustly estimate image noise level. The algorithm is applied for different image scales to estimate a noise spectrum. In this paper, we propose a convolutional neural network called PIXPNet for effective prediction of values of missing pixels. A comparative analysis confirms that the proposed PIXPNet provides smallest error of recovered pixel values among all existing methods. A good efficiency of application of the proposed method in both AWGN and spatially correlated noise suppression is demonstrated.

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