Abstract This paper explores the behavior of an algorithm called Audio Dynamics Automatic Equalization (ADAE). This algorithm has been inspired by research carried out in the context of image restoration: it is the adaptation of a contrast and color unsupervised equalizer for images, called Automatic Color Equalization (ACE), into the audio domain. Beside testing if the domain shift from image to audio processing can bring some interesting result, this work also investigates if ADAE behaves like already-known technologies for audio manipulation and restoration. To this end, after a description of the original and the derived algorithms, quantitative test are carried out using typical analyses from the Sound and Music Computing literature, such as frequency response, transfer function, and harmonic distortion. Finally, the paper discusses how the algorithm introduces dynamic range adjustments and non-linear distortions, thus behaving like a dynamics processor, a harmonic exciter, and a waveshaper.

The image contrast is a feature capturing the variation of the image signal across the space. Such a feature is very useful to describe the local image structure at different scales and thus it is relevant to many computer vision applications, like image/texture retrieval and object recognition. In this work, we present MiRCo, a novel measure of image contrast derived from the Retinex theory. MiRCo is robust against in-plane rotations and light changes at multiple scales. Thanks to these properties, MiRCo enables an accurate and robust description of the local image structure. Here we describe and discuss the mathematical insights of MiRCo also in comparison with other popular contrast measures.

The Retinex image filtering algorithms have been inspired by experimental findings on the behavior of the Human Vision System. They are known to locally adjust image color and contrast by preserving edges and attenuating gradients. In a reference formulation of the algorithm by Land and McCann, edge preservation and gradient attenuation are granted by two ad-hoc mechanisms: called respectively reset (the distinctive feature of all the Retinex algorithms) and thresholding. A somehow unanticipated finding is that gradient attenuation is also observed with algorithm variants that do not include the latter mechanism, which was explicitly devised to implement gradient attenuation. In this work, we provide an analytic demonstration of the capability of Retinex models to attenuate gradients using only the "reset" mechanism, combined with the local character of the mutual pixel influences. We show that this capability is an emergent property of all the reset-based Retinex models.

BACKGROUND: The protein ki67 (pki67) is a marker of tumor aggressiveness, and its expression has been proven to be useful in the prognostic and predictive evaluation of several types of tumors. To numerically quantify the pki67 presence in cancerous tissue areas, pathologists generally analyze histochemical images to count the number of tumor nuclei marked for pki67. This allows estimating the ki67-index, that is the percentage of tumor nuclei positive for pki67 over all the tumor nuclei. Given the high image resolution and dimensions, its estimation by expert clinicians is particularly laborious and time consuming. Though automatic cell counting techniques have been presented so far, the problem is still open. RESULTS: In this paper we present a novel automatic approach for the estimations of the ki67-index. The method starts by exploiting the STRESS algorithm to produce a color enhanced image where all pixels belonging to nuclei are easily identified by thresholding, and then separated into positive (i.e. pixels belonging to nuclei marked for pki67) and negative by a binary classification tree. Next, positive and negative nuclei pixels are processed separately by two multiscale procedures identifying isolated nuclei and separating adjoining nuclei. The multiscale procedures exploit two Bayesian classification trees to recognize positive and negative nuclei-shaped regions. CONCLUSIONS: The evaluation of the computed results, both through experts' visual assessments and through the comparison of the computed indexes with those of experts, proved that the prototype is promising, so that experts believe in its potential as a tool to be exploited in the clinical practice as a valid aid for clinicians estimating the ki67-index. The MATLAB source code is open source for research purposes.

Emerging display technologies are proposing monitors for medical imaging with an extended dynamic range of luminance. Those devices offer the opportunity to extend the range of visual information displayed, but the limits of the human visual system (HVS) in perceiving such information can cancel the advantages. To investigate this problem, we present a set of experiments, to assess the visual response of the HVS to controlled high dynamic range (HDR) content. They analyse the effects of glare. Using a typical HDR display, tailored for medical imaging applications, they first study the dependence of the visible dynamic range from the inter‐ocular glare, induced by different backgrounds, then the effect of glare on the detection of test elements on medical radiographic images. Finally, they assess the influence of luminance‐equivalent backgrounds with different structure in the detection of test patches. The results of the experiments confirm the glare as a major player in influencing visual information detection. Glare has a significant impact in limiting the amount of visual information actually perceived, consequently limiting analysis capabilities of such images. This confirms the importance of investigating and considering the characteristics of human vision in the design and test of HDR imaging systems.

Milano‐Retinex is a family of Retinex‐inspired spatial colour algorithms mainly developed for colour image enhancement. According to the Retinex theory, a Milano‐Retinex algorithm takes as input an RGB image and processes the colour intensities of each pixel (i.e. the target) based on the spatial distribution of the colour intensities sampled in a surrounding region. The output is an RGB image, with locally adjusted colours and contrast. In Milano‐Retinex family, different ways of spatial sampling are implemented. This study reviews and compares these sampling characteristics within a group of Milano‐Retinex algorithms developed in the last decade, from Random Spray Retinex (2007) to the gradient‐based colour sampling schemes GREAT and GRASS (2017). Instead of exploring the target neighbourhood by random paths as the original Retinex algorithm does, these methods consider sets of pixels, randomly or deterministically defined, including all the image pixels or a part of them, such as random sprays or image edges. They replace the ratio‐reset‐threshold‐product‐average mechanism of the original Retinex with equations re‐working maximal intensities over the sampled sets. The performance of these approaches is compared with more than 200 images of indoor and outdoor scenes, captured by commercial cameras under several different conditions.

Retinex is an early and famous theory attempting to estimate the human color sensation derived from an observed scene. When applied to a digital image, the original implementation of retinex estimates the color sensation by modifying the pixels channel intensities with respect to a local reference white, selected from a set of random paths. The spatial search of the local reference white influences the final estimation. The recent algorithm energy-driven termite retinex (ETR), as well as its predecessor termite retinex, has introduced a new path-based image aware sampling scheme, where the paths depend on local visual properties of the input image. Precisely, the ETR paths transit over pixels with high gradient magnitude that have been proved to be important for the formation of color sensation. Such a sampling method enables the visit of image portions effectively relevant to the estimation of the color sensation, while it reduces the analysis of pixels with less essential and/or redundant data, i.e., the flat image regions. While the ETR sampling scheme is very efficacious in detecting image pixels salient for the color sensation, its computational complexity can be a limit. In this paper, we present a novel Gradient-based RAndom Sampling Scheme that inherits from ETR the image aware sampling principles, but has a lower computational complexity, while similar performance. Moreover, the new sampling scheme can be interpreted both as a path-based scanning and a 2D sampling.

Automatic or semi-automatic stereoscopic image quality assessment has arisen due to the recent diffusion of a new generation of stereoscopic technologies and content demand. Thereby, there is a growth in asking for algorithms of Stereoscopic Image Quality Metrics (SIQA). In this paper, we present a method for assessing the stereoscopic image quality, QUALITAS. QUALITAS is grounded on some human visual system features such as contrast sensitivity, effect of disparate image quality in left and right images, and distance perception, which do not depend on the images being tested. QUALITAS is defined in five stages. Instead of averaging individual qualities of the stereo-pair, QUALITAS introduces Contrast Band-Pass Filtering on a wavelet domain at both views, namely our algorithm perceptually weights left and right images depending on certain viewing conditions. This paper includes the comparison of 27 Metrics SIQA proposed by 16 authors, which summarizes the work made in this field in the recent five years, on image database LIVE 3D. Some algorithms can be combined with any 2D/Normal Image Quality Assessments (NIQA), giving as a result that QUALITAS was compared against 221 Metrics. QUALITAS obtained the best results in terms of overall performance of correlation coefficients. We conclude all metrics in SIQA-SET are simple modifications of NIQA, which take into account some extra characteristics from the disparity map (usually depth variances). Instead QUALITAS incorporates disparity masking in addition to divide 3D scenario in two parts: background and foreground planes. Moreover QUALITAS employs a contrast band-pass filtering, so dynamic parameters are considered as observational distance. It includes loss of correlation, luminance and contrast distortion. It takes into account the visual differences between left and right images, employing a penalization depending on their wavelet energy. Thus, the novelty of QUALITAS lies in combining some the best features of stereoscopic image quality assessments.

The experience of immersive virtual reality (VR) can be considered as a communication process between human beings, mediated by computer systems, which uses visualisation and other sensory stimulation. In this paper, we analyse how VR characteristics can be explored using semiotic theory and, with methods of generative semiotics, we explore aspects of narrative and interaction in VR. We propose a semiotic analysis of VR communication focusing on syntax, semantics and pragmatics and considering also some principles of generative semiotics. The syntactic level is analysed as determined by the characteristics of the visual communication adopted. The semantic of VR is related to the functional model chosen to realise the virtual system. The pragmatic of VR is based on the human–computer interaction that changes the user's role. We explore how these aspects can be characterised in the context of VR communication design and what principles can be adopted for a VR application, and we present an analysis and a classification of the iconic signs that are being used in VR. Moreover, we present a framework that can be used to classify and describe different kinds of virtual reality systems and to better understand communication in VR, and we use it to classify eight popular systems for e-learning and collaboration.

In this paper, we present an efficient approach to digital color restoration, based on the idea of recovering the appearance of color rather than the original color signal, since, in most of the cases, for old films the original reference is missing, and new films or digital coding can be subject to severe gamut transformations. This approach is based on the application of algorithms inspired by the capabilities of the Human Vision System of automatically adjust to the variation of color and lightness in the scene. The proposed method allows more unsupervised restoration. We present an overview of the approach, characteristics of this family of algorithms, and restoration examples.

Automatic or semi-automatic stereoscopic image quality assessment has arisen due to the recent diffusion of a new generation of stereoscopic technologies and content demand. Thereby, there is a growth in asking for algorithms of Stereoscopic Image Quality Metrics (SIQA). In this paper, we present a method for assessing the stereoscopic image quality, QUALITAS. QUALITAS is grounded on some human visual system features such as contrast sensitivity, effect of disparate image quality in left and right images, and distance perception, which do not depend on the images being tested. QUALITAS is defined in five stages. Instead of averaging individual qualities of the stereo-pair, QUALITAS introduces Contrast Band-Pass Filtering on a wavelet domain at both views, namely our algorithm perceptually weights left and right images depending on certain viewing conditions. This paper includes the comparison of 27 Metrics SIQA proposed by 16 authors, which summarizes the work made in this field in the recent five years, on image database LIVE 3D. Some algorithms can be combined with any 2D/Normal Image Quality Assessments (NIQA), giving as a result that QUALITAS was compared against 221 Metrics. QUALITAS obtained the best results in terms of overall performance of correlation coefficients. We conclude all metrics in SIQA-? are simple modifications of NIQA, which take into account some extra characteristics from the disparity map (usually depth variances). Instead QUALITAS incorporates disparity masking in addition to divide 3D scenario in two parts: background and foreground planes. Moreover QUALITAS employs a contrast band-pass filtering, so dynamic parameters are considered as observational distance. It includes loss of correlation, luminance and contrast distortion. It takes into account the visual differences between left and right images, employing a penalization depending on their wavelet energy. Thus, the novelty of QUALITAS lies in combining some the best features of stereoscopic image quality assessments.

Starting from the revolutionary Retinex by Land and McCann, several further perceptually inspired color correction models have been developed with different aims, e.g. reproduction of color sensation, robust features recognition, enhancement of color images. Such models have a differential, spatially-variant and non-linear nature and they can coarsely be distinguished between white-patch (WP) and gray-world (GW) algorithms. In this paper we show that the combination of a pure WP algorithm (Random Spray Retinex (RSR) )and an essentially GW one (Automatic Color Equalization (ACE)) leads to a more robust and better performing model (RACE). The choice of RSR and ACE follows from the recent identification of a unified spatially-variant approach for both algorithms. Mathematically, the originally distinct non-linear and differential mechanisms of RSR and ACE have been fused using the spray technique and local average operations. The investigation of RACE allowed us to put in evidence a common drawback of differential models: corruption of uniform image areas. To overcome this intrinsic defect, we devised a local and global contrast-based and image-driven regulation mechanism that has a general applicability to perceptually inspired color correction algorithms. Tests, comparisons and discussions are presented.

In this paper, we present a discussion about perceptual-based color correction of digital images in the framework of variational techniques. We propose a novel image functional whose minimization produces a perceptually inspired color enhanced version of the original. The variational formulation permits a more flexible local control of contrast adjustment and attachment to data. We show that a numerical implementation of the gradient descent technique applied to this energy functional coincides with the equation of automatic color enhancement (ACE), a particular perceptual-based model of color enhancement. Moreover, we prove that a numerical approximation of the Euler-Lagrange equation reduces the computational complexity of ACE from theta(N2) to theta(N log N), where N is the total number of pixels in the image.

Image reproduction devices, such as displays or printers, can reproduce only a limited set of colors, denoted the color gamut. The gamut depends on both theoretical and technical limitations. Reproduction device gamuts are significantly different from acquisition device gamuts. These facts raise the problem of reproducing similar color images across different devices. This is well known as the gamut mapping problem. Gamut mapping algorithms have been developed mainly using colorimetric pixel-wise principles, without considering the spatial properties of the image. The recently proposed multilevel gamut mapping approach takes spatial properties into account and has been demonstrated to outperform spatially invariant approaches. However, they have some important drawbacks. To analyze these drawbacks, we build a common framework that encompasses at least two important previous multilevel gamut mapping algorithms. Then, when the causes of the drawbacks are understood, we solve the typical problem of possible hue shifts. Next, we design appropriate operators and functions to strongly reduce both haloing and possible undesired over compression. We use challenging synthetic images, as well as real photographs, to practically show that the improvements give the expected results.

In order to investigate the local filtering behavior of the Retinex model, we propose a new implementation in which paths are replaced by 2-D pixel sprays, hence the name "random spray Retinex." A peculiar feature of this implementation is the way its parameters can be controlled to perform spatial investigation. The parameters' tuning is accomplished by an unsupervised method based on quantitative measures. This procedure has been validated via user panel tests. Furthermore, the spray approach has faster performances than the path-wise one. Tests and results are presented and discussed.

Abstract In this paper we propose a speed‐up technique for a local automatic colour equalization operator derived from a model of the human vision system. This method is characterized by local and global filtering effects, that simultaneously achieve different equalization tasks e.g. performing colour and lightness constancy, realizing dynamic image data driven stretching, controlling the contrast. We describe a way to quickly create a filtering mapping function to perform the global component of the mapping. This method is based on singular value decomposition (SVD) applied to sampled and filtered points in the input image. Then, the local information is added computing the basic algorithm on a neighbourhood of each input pixel. A slight quality loss is the price that we have to pay for a speed‐up of more than two orders of magnitude of the basic algorithm. We present the results on several images and discuss the efficiency and the drawbacks of the speed‐up technique.

In this paper we present tests and results of an automatic color fading restoration process for digitized movies. The proposed color correction method is based on the ACE model, an unsupervised color equalization algorithm based on a perceptual approach and inspired by some mechanisms of the human visual system. This perceptual approach is local, robust and does not need any user region selection or any other user supervision. However the model has a small number of parameters that has to be set once before the filtering. The tests presented in this paper aim to study these parameters and find their effect on the final result.

This paper analyzes the effects of the application of visual adaptation mechanisms on snapshot-based guidance methods. The guidance principle of the visual homing is proven to be a visual potential function with an equilibrium point located at the goal position. The presence of a potential function means that classical control theory principles based on the Lyapunov functions can be applied to assess the robustness of the navigation strategy. The proposed adaptation mechanism is the Retinex algorithm, a blind chromatic adaptation pre-filtering that performs color constancy with no a priori information about the illuminant. It increases the visual information similarity under changes in the illuminant, increasing the robustness of the visual guidance. Tests and comparisons are presented.