In Full-Reference Image Quality Assessment (FR-IQA), subjective Mean Opinion Scores (MOS) reflect human retinal perception, whereas objective metrics operate on the displayed image. Bridging these domains requires parametric mappings that are sensitive to viewing distance. This paper introduces Blur-Equivalent Linearized Estimator (BELE), a lightweight and perceptually interpretable FR-IQA model that disentangles the impact of strong edge degradations from that of texture distortions. BELE computes two indices: a blur index derived from a linearized estimator of Positional Fisher Information loss on strong edges, explicitly accounting for viewing distance; and a texture index based on a Complex Peak Signal-to-Noise Ratio (CPSNR) that captures distortions affecting fine spatial details in textured regions. All distortion estimates are combined via low-order polynomial fitting, with a focalization term applied in this final stage to replace the VQEG rectification, thereby eliminating its limitations such as overfitting and lack of interpretability. BELE is entirely training-free, requiring only five interpretable parameters, and achieves very low computational complexity. Extensive experiments on six benchmark datasets demonstrate that BELE attains competitive or superior correlation with MOS compared to both classical and deep learning-based FR-IQA methods, while offering strong generalization, real-time feasibility, and minimal resource demands.

Objective Image Quality Assessment (IQA) methods often lack of linearity of their quality estimates with respect to scores expressed by human subjects and therefore IQA metrics undergo a calibration process based on subjective quality examples. However, example-based training presents a challenge in terms of generalization hampering result comparison across different applications and operative conditions. In this paper, new Full Reference (FR) techniques, providing estimates linearly correlated with human scores without using calibration are introduced. We show that on natural images, application of estimation theory and psychophysical principles to images degraded by Gaussian blur leads to a so-called canonical IQA method, whose estimates are linearly correlated to both the subjective scores and the viewing distance. Then, we show that any mainstream IQA methods can be reconducted to the canonical method by converting its metric based on a unique specimen image. The proposed scheme is extended to wide classes of degraded images, e.g. noisy and compressed images. The resulting calibration-free FR IQA methods allows for comparability and interoperability across different imaging systems and on different viewing distances. A comparison of their statistical performance with respect to state-of-the-art calibration prone methods is finally provided, showing that the presented model is a valid alternative to the final 5-parameter calibration step of IQA methods, and the two parameters of the model have a clear operational meaning and are simply determined in practical applications. The enhanced performance are achieved across multiple viewing distance databases by independently realigning the blur values associated with each distance. • The work provides a novel model for the realistic estimation of the visual discomfort caused by blur of natural scenes, considering the cognitive needs of the subjects and the adaption capability of the visual system. • We i) present the canonical estimator useful for a-priori linearization of the blur metric and ii) show how any classical IQA metric can be mapped into an equivalent blur metric. • We present a 2-parameter model as an effective and fast alternative to the 5-parameter scoring function suggested by VQEG, in the final step of the study of Full Reference IQA methods.

In this paper, a novel full Reference method is proposed for image quality assessment, using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details. To this purpose, the gradient of the impaired image is locally decomposed as a predicted version of the original gradient, plus a gradient residual. It is assumed that the detail attenuation identifies the detail loss, whereas the gradient residuals describe the spurious details. It turns out that the perceptual impact of detail losses is roughly linear with the loss of the positional Fisher information, while the perceptual impact of the spurious details is roughly proportional to a logarithmic measure of the signal to residual ratio. The affine combination of these two metrics forms a new index strongly correlated with the empirical differential mean opinion score (DMOS) for a significant class of image impairments, as verified for three independent popular databases. The method allowed alignment and merging of DMOS data coming from these different databases to a common DMOS scale by affine transformations. Unexpectedly, the DMOS scale setting is possible by the analysis of a single image affected by additive noise.

Management of air quality is an important task in many human activities. It is carried out mainly by installing ventilation and filtering facilities. In order to ensure efficiency, these systems must be designed after the knowledge of key environmental parameters, such as size and type of particles and fibres present in the air. In this paper, we propose a new method for the classification of dust particles and fibres based on a minimal set of geometric features extracted from binary images of dust elements, captured by a very cheap imaging system. The proposed technique is discussed and tested. Experimental results obtained by real-measured data are presented, showing satisfactory performance by using several well-known classifiers.

A method for full-reference visual quality assessment based on the 2-D combination of two diverse metrics is described. The first metric is a measure of structural information loss based on the Fisher information about the position of the structures in the observed images. The second metric acts as a categorical indicator of the type of distortion that images underwent. These two metrics constitute the inner state of a virtual cognitive model, viewed as a system whose output is the automatic visual quality estimate. The use of a 2-D metric fills the intrinsic incompleteness of methods based on a single metric while providing consistent response across different image impairment factors and blind distortion classification capability with a modest computational overhead. The high accuracy and robustness of the method are demonstrated through cross-validation experiments.

A novel method for estimating the direction of arrival of ultra-wideband wavefronts impinging on a linear uniform sensor array is proposed. It is based on expanding the space time signals onto a basis of Laguerre-Gauss Circular Harmonic functions, whose peculiar properties lead to a new signal subspace parametric model. This approach allows robust angle of arrival estimation in low signal to noise ratio environments.

The nice relationship existing among the Hermite-Gauss and the Laguerre-Gauss image expansions, whose basis functions span the same signal space, is investigated. As a result, a novel efficient method for Cartesian to polar coordinate inter-conversion, especially suited for dedicated hardware realization, is proposed. Applications to local image rotation, based on the simple steerability of the Laguerre-Gauss expansion, and to local image analysis, based on Gaussian derivatives, are considered in detail. A possible fixed-point realization of the proposed inter-conversion scheme is discussed.

A method for measuring the orientation of linear (1-D) patterns, based on a local expansion with Laguerre-Gauss circular harmonic (LG-CH) functions, is presented. It lies on the property that the polar separable LG-CH functions span the same space as the 2-D Cartesian separable Hermite-Gauss (2-D HG) functions. Exploiting the simple steerability of the LG-CH functions and the peculiar block-linear relationship among the two expansion coefficients sets, maximum likelihood (ML) estimates of orientation and cross section parameters of 1-D patterns are obtained projecting them in a proper subspace of the 2-D HG family. It is shown in this paper that the conditional ML solution, derived by elimination of the cross section parameters, surprisingly yields the same asymptotic accuracy as the ML solution for known cross section parameters. The accuracy of the conditional ML estimator is compared to the one of state of art solutions on a theoretical basis and via simulation trials. A thorough proof of the key relationship between the LG-CH and the 2-D HG expansions is also provided.

In this contribution a procedure of eye detection and localization for biometric applications is presented. It is essentially based on the calculus of two polar moments whose values are employed for detection and localization of pupils. The polar moments are calculated using a bank of mono-dimensional Hermite filters whose outputs can be easily converted to give the local expansion of the analyzed image in the Gauss Laguerre polar separable family of functions.

Advances in digital imaging technologies make increasingly available high quality images characterized by high dynamic range (HDR) of color components. Usual display devices are unable to visualize the information content of HDR images, especially in presence of light. For this reason special compression techniques for image rendering on conventional displays have been developed. In this work, a new technique based on simultaneous compression of low frequency radiance variations and detail enhancement is presented. In this work, a method working in the transform domain of an edge oriented wavelet transform is presented. Low-frequency components and high-resolution wavelet coefficients are separately manipulated before image reconstruction in order to reduce the overall dynamic range. The effectiveness of the technique is shown by means of significant examples.

Large collections of image documents are today accessible via communication networks. Cost effective retrieval of the wanted images needs convenient browsing tools. In this contribution a technique for image selection on the basis of key details is illustrated. The technique is useful for instance for assisted queries to digital artwork collections, tele-sensing images, aerial photographs, astronomic databases, etc. The basic feature of the method is the capability of detecting and locating anyway rotated details with reasonable computational costs. In this work a criterion for proper choice of detail reference points is illustrated.

Usual approaches to localization, i.e., joint estimation of position, orientation and scale of a bidimensional pattern employ suboptimum techniques based on invariant signatures, which allow for position estimation independent of scale and orientation. In this paper a Maximum Likelihood method for pattern localization working in the Gauss-Laguerre Transform (GLT) domain is presented. The GLT is based on an orthogonal family of Circular Harmonic Functions with specific radial profiles, which permits optimum joint estimation of position and scale/rotation parameters looking at the maxima of a "Gauss-Laguerre Likelihood Map." The Fisher information matrix for any given pattern is given and the theoretical asymptotic accuracy of the parameter estimates is calculated through the Cramer Rao Lower Bound. Application of the ML estimation method is discussed and an example is provided.

This contribution describes a fast frequency domain approach for blind channel identification which does not rely on the statistics of the symbols. The proposed approach is based on the so-called "intraspectral relations" of DFTs of PAM fractionally sampled signals. The use of DFTs is allowed under certain conditions commonly encountered in data communication systems. The intraspectral relations are equivalent to analogous relations introduced in the time domain in Xu et al. (1995), Moulines et al. (1995) and Hua (1996). From the intraspectral relations, asymptotically efficient solutions are derived which turn out to be either more accurate or less expensive in terms of complexity w.r.t. the time domain counterparts. Simulation results are provided to assess the validity of the proposed approach in comparison with the Rao Cramer bound and with other approaches from the literature.

We address the problem of the blind joint identification and order estimation of a non-minimum phase FIR communication channel by exploiting the cyclo-stationarity of the received signal sampled at a rate greater than the symbol rate. We show that the identification can be formulated as a "subspace fitting" problem; this allows for using the subspace distance as a test statistic to detect the correct channel length (among different hypotheses). Moreover, mimicking estimation procedures proposed in the framework of DOA estimation, an asymptotically efficient procedure is proposed which obtains the channel estimate in two steps: the channel estimate obtained in the first step determines optimal weights which are then employed in the second step to refine the channel estimate. The accuracy of the identification is comparable with other methods described in the literature (e.g. the subspace method) while the test for order detection performs quite well also in the presence of channel disparity outperforming commonly used tests based on the eigenvalues of the covariance matrix.

A twin stage texture synthesis-by-analysis method is presented. It aims to approximate first- and second-order distributions of the texture, accordingly to the Julesz conjecture. In the first stage, the binary textural behavior of a given prototype is represented by means of a hard-limited Gaussian process. In the second stage, the texture is synthesized by passing the binary hard-limited Gaussian process through a linear filter followed by a zero memory histogram equalizer.

A new technique for unsupervised texture synthesis by analysis is presented. It employs a stochastic model of textured fields based on white Gaussian fields passed through a nonlinear system composed of two linear filters connected by a hard limiter. The identification of the texture models is performed using a Bussgang blind deconvolution algorithm and exploiting of the Van Vleck rule for determining the filters parameters. After a theoretical discussion of the method typical examples are provided.

The normalized autocorrelation function of a Gaussian process may be recovered from second order moments of their polarity, through the arcsin law. By analogy, it is possible to calculate the normalized autocorrelation function of a circularly complex Gaussian process from the knowledge of moments of its instantaneous phase. In the present paper, two estimators of the normalized autocorrelation function based on the phase only are presented. Their theoretical accuracy is evaluated and compared to the accuracy of the direct estimate.>

The extension of higher order moments based spectral analysis to hybrid nonlinear (HNL) moments is theoretically discussed. It is shown that the structure of the covariance matrix of harmonic processes is retained by HNL moments matrices, thus allowing the use of eigenassisted estimation procedures. The Gaussian components insensitivity of slice cumulants is generalized to a subclass of HNL moments, and a covariance subtraction method for blind rejection of colored noise is proposed. Simulation results confirm the applicability of such a procedure for bearings estimation in array processing schemes.>

A new criterion for speech segmentation based on higher order moments is presented. It is shown that simple estimates of third and fourth order cumulants can provide useful cues for speech segmentation and classification. In fact, the analysis of the cumulants behaviour seems well suited for discriminating voiced, unvoiced and transient sounds. Moreover, the estimated quantities give some indications about the information content of segments of speech. This permit the experimentation of a coding scheme for the speech signal, which has yet been experimented in image coding, founded on a perceptually - based conjecture

The problem of recognizing and locating isolated objects from their images is addressed, and a complete procedure is described. The technique is based on a template matching in the log-polar amplitude spectrum domain and in the space domain. This makes it possible to take properly into account both spectral features and spatial details and to measure with accuracy the scale factor, the orientation, and the absolute position of the object. To reduce the computational burden, pairs of tomographic projections are used in both domains. As an example, the double tomographic method is applied to the classical problem of the automatic reading of printed characters, with the additional specification of unknown size and orientation, and shows good results.>

A novel, suboptimal, nonlinear recursive algorithm for the estimation of filtered Markov chains from noisy observations is presented. The expression of the estimator is formally identical to the Kalman filter solution when the error covariance matrix is replaced by the conditional covariance matrix. Starting from the Martingale-difference (MD) representation theorem, a recursive expression for the conditional covariance matrix that can be reasonably approximated by means of the Markov property is derived. Thus the obtained estimator is suboptimal in the sense that the conditional covariance matrix is evaluated only approximately.>

The technique of template matching in the log-polar visibility (magnitude of Fourier transform in the logarithmic radius and azimuth plane) is proposed as a promising tool for many problems of isolated object recognition irrespective of its position, scale, and rotation. The properties of the log-polar visibility suggests the use of partial information to implement some forms of "reduced template matching". These techniques imply substantial reduction in the computational cost.

The problem of frequency and frequency rate estimation from interrupted observations is addressed. The intuitive fact that the accuracy can be greatly improved by using separate observation intervals is quantitatively discussed. Cramer-Rao bounds and ambiguity functions are given and structures of maximum likelihood receivers are considered.

The function e-x2has many well-known mathematical properties. Here it is shown that if a stationary process has a Gaussian-shaped acf, then its PARCOR coefficients decay exponentially.