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Visual quality of color images is an important aspect in various applications of digital image processing and multimedia. A large number of visual quality metrics (indices) has been proposed recently. In order to assess their reliability, several databases of color images with various sets of distortions have been exploited. Here we present a new database(More)
This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new database contains a larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 levels for each type of distortion.(More)
This paper deals with the problem of identifying the nature of noise and estimating its standard deviation from the observed image in order to be able to apply the most appropriate processing or analysis algorithm afterwards. In this study, w e focus our attention on three classes of degraded noise images, the rst one being degraded by an additive noise,(More)
We deal with the problem of blind parameter estimation of signal-dependent noise from mono-component image data. Multispectral or color images can be processed in a component-wise manner. The main results obtained rest on the assumption that the image texture and noise parameters estimation problems are interdependent. A two-dimensional fractal Brownian(More)
Methods for blind estimation of signal dependent noise parameters from scatter-plots by polynomial regression are considered. Some new modifications as well as known ones are discussed and their performance is compared for test images with simulated signal dependent noise. Recommendations on method application and parameter setting are given.
A problem of lossy compression of hyperspectral images is considered. A specific aspect is that we assume a signal-dependent model of noise for data acquired by new generation sensors. Moreover, a signal-dependent component of the noise is assumed dominant compared to a signal-independent noise component. Sub-band (component-wise) lossy compression is(More)
A maximum-likelihood method for estimating hyperspectral sensors random noise components, both dependent and independent from the signal, is proposed. A hyperspectral image is locally jointly processed in the spatial and spectral dimensions within a multicomponent scanning window (MSW), as small as 7 × 7 × 7 spatial-spectral pixels. Each MSW(More)