A Detail Based Method for Linear Full Reference Image Quality Prediction

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.Comment: 15 pages, 9 figures. Copyright notice: The paper has been accepted for publication on the IEEE Trans. on Image Processing on 19/09/2017 and the copyright has been transferred to the IEE

Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays

Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an essential task in sonar, radar, acoustics, biomedical and multimedia applications. Many state of the art wide-band DOA estimators coherently process frequency binned array outputs by approximate Maximum Likelihood, Weighted Subspace Fitting or focusing techniques. This paper shows that bin signals obtained by filter-bank approaches do not obey the finite rank narrow-band array model, because spectral leakage and the change of the array response with frequency within the bin create \emph{ghost sources} dependent on the particular realization of the source process. Therefore, existing DOA estimators based on binning cannot claim consistency even with the perfect knowledge of the array response. In this work, a more realistic array model with a finite length of the sensor impulse responses is assumed, which still has finite rank under a space-time formulation. It is shown that signal subspaces at arbitrary frequencies can be consistently recovered under mild conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant eigenvectors of the wide-band space-time sensor cross-correlation matrix. A novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order to recover consistency. The number of sources active at each frequency are estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can be fed to any subspace fitting DOA estimator at single or multiple frequencies. Simulations confirm that the new technique clearly outperforms binning approaches at sufficiently high signal to noise ratio, when model mismatches exceed the noise floor.Comment: 15 pages, 10 figures. Accepted in a revised form by the IEEE Trans. on Signal Processing on 12 February 1918. @IEEE201

Predicting blur visual discomfort for natural scenes by the loss of positional information

The perception of blur due to accommodation failures, insufficient optical correction or imperfect image reproduction is a common source of visual discomfort, usually attributed to an anomalous and annoying distribution of the image spectrum in the spatial frequency domain. In the present paper, this discomfort is related to a loss of the localization accuracy of the observed patterns. It is assumed, as a starting perceptual principle, that the visual system is optimally adapted to pattern localization in a natural environment. Thus, since the best possible accuracy of the image patterns localization is indicated by the positional Fisher Information, it is argued that blur discomfort is strictly related to a loss of this information. Following this concept, a receptive field functional model is adopted to predict the visual discomfort. It is a complex-valued operator, orientation-selective both in the space domain and in the spatial frequency domain. Starting from the case of Gaussian blur, the analysis is extended to a generic type of blur by applying a positional Fisher Information equivalence criterion. Out-of-focus blur and astigmatic blur are presented as significant examples. The validity of the proposed model is verified by comparing its predictions with subjective ratings. The model fits linearly with the experiments reported in independent databases, based on different protocols and settings

Predicting the Blur Visual Discomfort for Natural Scenes by the Loss of Positional Information

The perception of the blur due to accommodation failures, insufficient optical correction or imperfect image reproduction is a common source of visual discomfort, usually attributed to an anomalous and annoying distribution of the image spectrum in the spatial frequency domain. In the present paper, this discomfort is attributed to a loss of the localization accuracy of the observed patterns. It is assumed, as a starting perceptual principle, that the visual system is optimally adapted to pattern localization in a natural environment. Thus, since the best possible accuracy of the image patterns localization is indicated by the positional Fisher Information, it is argued that the blur discomfort is strictly related to a loss of this information. Following this concept, a receptive field functional model, tuned to common features of natural scenes, is adopted to predict the visual discomfort. It is a complex-valued operator, orientation-selective both in the space domain and in the spatial frequency domain. Starting from the case of Gaussian blur, the analysis is extended to a generic type of blur by applying a positional Fisher Information equivalence criterion. Out-of-focus blur and astigmatic blur are presented as significant examples. The validity of the proposed model is verified by comparing its predictions with subjective ratings. The model fits linearly with the experiments reported in independent databases, based on different protocols and settings.Comment: 12 pages, 8 figures, article submitted to Vision Research (Elsevier) Journal in July 202

Improved spectral analysis of near periodic signals with long-term prediction

L'estimation du spectre de signaux quasi périodiques peut être améliorée par la sur-détermination du modèle AutoRegressive dans le contexte d'analyse a prédiction linéaire. Dans cet étude on propose l'addition de termes de prédiction à "longue distance", ce qui se traduit en modèles d'ordre élevé avec une modeste extension des systèmes d'équations normaux. Comme dans toutes mesures de type interférometrique, cette procédure comporte la présence d'ambiguïtés qui peuvent être éliminées par l'exploitation d'information a priori. La méthode est illustrée sur deux cas simples de processus AR à bande étroite et de composants harmoniques proches

Multiscale image features analysis with circular harmonic wavelets

In this contribution we introduce a new family of wavelets named Circular Harmonic Wavelets (CHW), suited for multiscale feature-based representations, that constitute a basis for general steerable wavelets. The family is based on Circular Harmonic Functions (CHF) derived by the Fourier expansion of local Radial Tomographic Projections. A multiscale general feature analysis can be performed by linearly combining the outputs of CHW operators of different order. After a survey on the general properties of the CHFs, we investigate the relationship between CHF and the wavelet expansion, stating the basic admissibility and stability conditions with reference to the Hankel transform of the radial profiles and describing some fundamental mathematical properties. Finally some applications are illustrated through examples

Adaptive DCT coding by entropy guided segmentation

Le méthodes traditionnelles de compression d'image parmi transformée cosinus utilisent des règles de quantisation uniforme sur le plan d'image. Dans cet article on expose un critère pour régler la quantisation suivant des éxigences générales d'intéret visuel

Bayesian iterative method for blind deconvolution

Blind deconvolution is a typical solution to unknown LSI system inversion problems. When only the output is available, second order statistics are not sufficient to retrieve the phase of the LSI system, so that some form of higher-order analysis has to be employed. In this work, a general iterative solution based on a Bayesian approach is illustrated, and some cases both for mono and bidimensional applications are discussed. The method implies the use of non second-order statistics (rather than higher-order statistics), tuned to specific a priori statistical models. The Bayesian approach yields specific solutions corresponding to known techniques, such as MED deconvolution employed in seismic processing, and more sophisticated procedures for non-independent identically distributed (for instance Markovian) inputs

Full-Reference Calibration-Free Image Quality Assessment

One major problem of objective Image Quality Assessment (IQA) methods is the lack of linearity of their quality estimates with respect to scores expressed by human subjects. For this reason, usually IQA metrics undergo a calibration process based on subjective quality examples. However, example-based training makes generalization problematic, 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. To reach this objective, these techniques are deeply rooted on principles and theoretical constraints. Restricting the interest on the IQA of the set of natural images, it is first recognized that application of estimation theory and psycho physical principles to images degraded by Gaussian blur leads to a so-called canonical IQA method, whose estimates are not only highly linearly correlated to subjective scores, but are also straightforwardly related to the Viewing Distance (VD). Then, it is shown that mainstream IQA methods can be reconducted to the canonical method applying a preliminary metric conversion based on a unique specimen image. The application of this scheme is then extended to a significant class of degraded images other than Gaussian blur, including noisy and compressed images. The resulting calibration-free FR IQA methods are suited for applications where comparability and interoperability across different imaging systems and on different VDs is a major requirement. A comparison of their statistical performance with respect to some conventional calibration prone methods is finally provided