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.

In recent years much interest has been focused on time delay estimation in reverberant environments, for a variety of practical purposes. Algorithms based on generalized cross correlation are commonly used, but show clear limitations even in the presence of low reverberation levels. More sophisticated approaches, like cepstral prefiltering, have been proposed, but they can be computationally expensive and inadequate for real-time applications. In this paper a novel prefiltering approach, based on the common acoustical pole modeling of room transfer functions, is described and compared to existing techniques. Experimental tests show its effectiveness in combating reverberation while maintaining the simplicity requirement needed in many practical situations.

Localization of acoustic sources in reverberant environments by microphone arrays remains a challenging task in audio signal processing. As a matter of fact, most assumptions of commonly adopted models are not met in real applications. Moreover, in practical systems it is not convenient or possible to employ sophisticated and costly architectures, that require precise synchronization and fast data shuffling among sensors. In this paper, a new robust multi-step procedure for speaker localization in reverberant rooms is introduced and described. The new approach is based on a disturbed harmonics model of time delays in the frequency domain and employs the well-known ROOT-MUSIC algorithm, after a preliminary distributed processing of the received signals. Candidate source positions are then estimated by clustering of raw TDOA estimates. Main features of the proposed approach, compared to previous solutions, are the capability of tracking multiple speakers and the high accuracy of the closed form TDOA estimator.

Neural networks add flexibility to the design of equalizers for digital communications. In this work novel decision-feedback (DF) neural equalizers (DFNE) are introduced and compared with classical DF equalizers and Viterbi demodulators. It is shown that the choice of a cost functional based on the Discriminative Learning (DL), coupled with a fast training paradigm, can provide neural equalizers that outperform the standard DF equalizer (DFE) at practical signal to noise ratio (SNR). Resulting architectures are competitive with the Viterbi solution from cost-performance aspects.

Neural networks have been successfully applied to the equalization of digital communication channels. Decision feedback is a common technique to enhance the performance of linear equalizers. The two concepts can be effectively merged, generating a wide set of possible architectures. In this work several decision-feedback (DF) neural equalizers (DFNE) are compared with classical DF equalizers and Viterbi demodulators. In particular, it is shown that the choice of a cost functional based on Discriminative Learning (DL), coupled with a fast training paradigm, can provide neural equalizers that outperform the standard DF equalizer (DFE) at a practical signal to noise ratio (SNR). Resulting architectures are competitive with the Viterbi solution as for cost-performance aspects.

In this paper a general class of fast learning algorithms for feedforward neural networks is introduced and described. The approach exploits the separability of each layer into linear and nonlinear blocks and consists of two steps. The first step is the descent of the error functional in the space of the outputs of the linear blocks (descent in the neuron space), which can be performed using any preferred optimization strategy. In the second step, each linear block is optimized separately by using a least squares (LS) criterion. To demonstrate the effectiveness of the new approach, a detailed treatment of a gradient descent in the neuron space is conducted. The main properties of this approach are the higher speed of convergence with respect to methods that employ an ordinary gradient descent in the weight space backpropagation (BP), better numerical conditioning, and lower computational cost compared to techniques based on the Hessian matrix. The numerical stability is assured by the use of robust LS linear system solvers, operating directly on the input data of each layer. Experimental results obtained in three problems are described, which confirm the effectiveness of the new method.

Classical methods for training feedforward neural networks are characterized by a number of shortcomings, first of all the slow rate of convergence and the occurrence of local minima. In this paper a new learning algorithm is presented as a faster alternative to the backpropagation method. The algorithm is based on the solution of a linearized system for each layer of the network performed by a block total least squares technique. Simulation results are reported showing the high convergence speed of the new algorithm and its high degree of accuracy.

It is known that RNS VLSI processors can parallelize fixed-point addition and multiplication operations by the use of the Chinese remainder theorem (CRT). The required modular operations, however, must use specialized hardware whose design and implementation can create several problems. In this paper a modified residue arithmetic, called pseudo-RNS is introduced in order to alleviate some of the RNS problems when digital signal processing (DSP) structures are implemented. Pseudo-RNS requires only the use of modified binary processors and exhibits a speed performance comparable with other RNS traditional approaches. Some applications of the pseudo-RNS to common DSP architectures, such as multipliers and filters, are also presented in this paper. They are compared in terms of the area-time square product versus other RNS and weighted binary structures. It is proven that existing combinatorial or look-up table approaches for RNS are tailored to small designs or special applications, while the pseudo-RNS approach remains competitive also for complex systems.>

In highly integrated processors, a concurrent fault tolerance capability is particularly important, especially for real-time applications. In fact, in these systems, transient errors are often present, but are difficult to correct online. Error recovery procedures applied for each processing or memory element require large amount of hardware and can reduce throughput. Residue arithmetic has intrinsic fault tolerance capability and requires less complex hardware. A single error correction procedure based on the use of a redundant residue number system (RRNS) and the base extension operation is proposed. The method uses a very small decision table and works in parallel mode; therefore it is suitable for high speed VLSI circuit realization. A parallel systolic architecture which realizes the algorithm is introduced.>

A procedure for assigning optimal weights to the prediction equations which are used to obtain the parameters of an autoregressive (AR) model for spectrum estimation by the least squares (LS) solution is presented. The set of weights is computed, by linear programming techniques, in order to reduce the effects of strong impulsive noise onto the AR parameter estimate. The method is particularly effective when the Gaussian white noise component is much smaller than both spikes and useful signal. In order to demonstrate the capability of the proposed approach, the results of a simple AR parameter estimation experiment are also reported.>

Fast RNS (residue number system) algorithms which use only binary arithmetic are developed. Scaled residues, called pseudoresidues, are introduced by exploiting the cycle properties of each RNS channel and solving a Diophantine equation. Using the pseudoresidues instead of the original residue set to perform the desired computations, an RNS processor can be built with standard binary devices of small wordlength. The effectiveness of the procedure is shown by developing the pseudoresidue implementations of a modular multiplier for odd moduli RNS and of a FIR (finite impulse response) filter. The resulting structures exhibit complete reprogrammability for both moduli and coefficients, a very low number of fast machine cycles, and a square time-area product reduction.>

An important problem in real-time DSP (digital signal-processing) systems with highly integrated components is the capability of automatic error detection and correction. The use of residue number arithmetic allows error detection and correction because of its unweighted nature. A single-error-correction procedure is proposed which is based on the use of redundant residue number systems (RRNS) and the base extension operation. The proposed method uses a small decision table and works in parallel mode; therefore it is suitable for high-speed VLSI circuit realization. A parallel architecture which realizes the method is also introduced.>