Benoit Vozel

Image database TID2013

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. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted images are presented. Mean opinion scores (MOS) for the new database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted images and specific image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created image database and the collected MOS values are freely available for downloading and utilization for scientific purposes. We have created a new large database.This database contains larger number of distorted images and distortion types.MOS values for all images are obtained and provided.Analysis of correlation between MOS and a wide set of existing metrics is carried out.Methodology for determining drawbacks of existing visual quality metrics is described.

Identification of the nature of noise and estimation of its statistical parameters by analysis of local histograms

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, we focus our attention on three classes of degraded noise images, the first one being degraded by an additive noise, the second one by a multiplicative noise and the latter by an impulsive noise. First, in order to identify the nature of the noise, we propose a new approach consisting of characterizing each class by a parameter obtained from histograms computed on several homogeneous regions of the observed image. The homogeneous regions are obtained by segmenting images. Then, the estimation of the standard deviation is achieved from the analysis of an histogram of local standard deviations computed on each of the homogeneous regions.

Image informative maps for component- wise estimating parameters of signal- dependent noise

Abstract. 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 motion (fBm) model is used for locally describing image texture. A polynomial model is assumed for the purpose of describing the signal-dependent noise variance dependence on image intensity. Using the maximum likelihood approach, estimates of both fBm-model and noise parameters are obtained. It is demonstrated that Fisher information (FI) on noise parameters contained in an image is distributed nonuniformly over intensity coordinates (an image intensity range). It is also shown how to find the most informative intensities and the corresponding image areas for a given noisy image. The proposed estimator benefits from these detected areas to improve the estimation accuracy of signal-dependent noise parameters. Finally, the potential estimation accuracy (Cramér-Rao Lower Bound, or CRLB) of noise parameters is derived, providing confidence intervals of these estimates for a given image. In the experiment, the proposed and existing state-of-the-art noise variance estimators are compared for a large image database using CRLB-based statistical efficiency criteria.

A New Color Image Database TID2013: Innovations and Results

A new database of distorted color images called TID2013 is designed and described. In opposite to its predecessor, TID2008, this database contains images with five levels of distortions instead of four used earlier and a larger number of distortion types (24 instead of 17). The need for these modifications is motivated and new types of distortions are briefly considered. Information on experiments already carried out in five countries with the purpose of obtaining mean opinion score (MOS) is presented. Preliminary results of these experiments are given and discussed. Several popular metrics are considered and Spearman rank order correlation coefficients between these metrics and MOS are presented and discussed. Analysis of the obtained results is performed and distortion types difficult for assessment by existing metrics are noted.

Segmentation-based method for blind evaluation of noise variance in images

Noise is one of the basic factors that degrade remote sensing (RS) data and prevent accurate and reliable retrieval of useful information. Availability of a priori information on noise type and properties allows applying more effective methods for image processing, namely, filtering, edge detection, feature extraction, etc. However, noise statistics are often unknown and are to be estimated for an image at hand. Thus one needs blind methods for the evaluation of the noise variance, especially if the number of images or sub-band images of multichannel RS data is large enough. In this paper, we consider several approaches to blind evaluation of noise variance. An important item is that we consider both i.i.d. and spatially correlated noise. It is demonstrated that some techniques that perform well enough for i.i.d. noise fail if the image is corrupted by spatially correlated noise. We show how segmentation-based methods for blind evaluation of noise variance that operate in the spatial domain can be modified in order to provide better accuracy for wide ranges of noise variance and spatial correlation parameters. Numerical simulation results comparing the performance of several techniques are presented. Real RS data processing examples are also given.

Methods and automatic procedures for processing images based on blind evaluation of noise type and characteristics

In many modern applications, methods and algorithms used for image processing require a priori knowledge or estimates of noise type and its characteristics. Noise type and basic parameters can be sometimes known in advance or determined in an interactive manner. However, it occurs more and more often that they should be estimated in a blind manner. The results of noise-type blind determination can be false, and the estimates of noise parameters are characterized by certain accuracy. Such false decisions and estimation errors have an impact on performance of image-processing techniques that is based on the obtained information. We address some issues of such a negative influence. Possible structures of automatic procedures are presented and discussed for several typical applications of image processing as remote sensing data preprocessing and compression.

Image Informative Maps for Estimating Noise Standard Deviation and Texture Parameters

The problem of automatic detection of image areas appropriate for accurate estimation of additive noise standard deviation (STD) irrespectively to processed image properties is considered in this paper. For accurate estimation of either image texture or noise STD, we distinguish two complementary informative maps: noise- (NI-) and texture- (TI-) informative ones. The NI map is determined and iteratively upgraded based on the Fisher information on noise STD calculated in scanning window (SW) fashion. Fractional Brownian motion (fBm) model for image texture is used to derive the required Fisher information. To extract final noise STD from NI map, fBm- and DCT-based estimators are implemented. The performance of these two estimators is comparatively assessed on large image database for different noise levels. It is also compared with performance of two competitive state-of-the-art estimators recently published. Utilizing NI map along with DCT-based noise STD estimator has proved to be significantly more efficient.

Filtering of radar images based on blind evaluation of noise characteristics

A common assumption concerning noise in radar images is that it is of multiplicative nature and spatially uncorrelated. Meanwhile, recent studies have shown that additive noise component cannot be neglected, especially for images formed by side look aperture radars (SLARs). Moreover, majority of radar image filtering techniques are designed under assumption that noise is i.i.d., i.e. spatially uncorrelated. However, in many practical situations the latter assumption is not true. Besides, spatial correlation properties of noise can be different and they are often a priori unknown. In this paper we demonstrate that complex statistical and spatial correlation characteristics of noise in radar images can and should be taken into consideration at image filtering stage. We design a modification of the denoising algorithm based on discrete cosine transform (DCT) that is able to easily incorporate a priori information or obtained estimates of noise statistical and spatial correlation characteristics. This can be done in automatic (blind) manner due to utilizing a sequence of blind estimation operations. We present simulation results that show appropriate accuracy and robustness of these operations. Finally, real life image filtering examples are given that confirm the effectiveness of the designed techniques.

Maximum likelihood estimation of spatially correlated signal-dependent noise in hyperspectral images

Abstract. A new algorithm is described for estimating the noise model parameters in hyperspectral data when neither noise components variance nor noise spatial/spectral correlation priors are available. A maximum likelihood (ML) technique is introduced for checking the noise spatial correlation hypothesis and estimating the spatial correlation function width alongside with estimating signal-independent and signal-dependent noise components variance. The hyperspectral image is assumed to match a limited set of assumptions. A three-dimensional (3-D) fractional Brownian motion (fBm) model is introduced for describing locally the texture of the 3-D image noisy textural fragment. Nonstationarity of the useful image signal is taken into account by performing the estimation locally on a 3-D block-by-block basis. The accuracy of the proposed algorithm is first illustrated for synthetic images obtained from either pure fBm or almost noise-free AVIRIS hyperspectral images artificially degraded with spatially correlated noise. The results obtained for synthetic images demonstrate appropriate accuracy and robustness of the proposed method. Then results obtained for real life AVIRIS hyperspectral data sets confirm the noise spatial uncorrelation hypothesis for images acquired by the AVIRIS sensor. Conclusions and open problems are outlined.

A comparative study between parametric blur estimation methods

In pattern recognition problems, the effectiveness of the analysis depends heavily on the quality of the image to be processed. This image may be blurred and/or noisy and the goal of digital image restoration is to find an estimate of the original image. A fundamental issue in this process is the blur estimation. When the blur is not readily available, it has to be estimated from the observed image. Two main approaches can be found in the literature. The first one identify the blur parameters before any restoration whereas the second one realizes these two steps jointly. We present a comparative study of several parametric blur estimation methods, based on a parametric ARMA modeling of the image, belonging to the first approach. Our purpose is to evaluate the accuracy of the various methods, on which the restoration procedure relies, and their robustness to modeling assumptions, noise, and size of support.