Designing a Cockpit for Image Quality Evaluation

Image Quality (IQ) as assessed by humans is a concept hard to be defined, since it relies on many different features, including both low level and high level visual characteristics. Image luminance, contrast, color distribution, smoothness, presence of noise or of geometric distortions are some examples of low level cues usually contributing to image quality. Aesthetic canons and trends, displacement of the objects in the scene, significance and message of the imaged visual content are instances of the high level (i.e. semantic) concepts that may be involved in image quality assessment. Despite subjective evaluation of IQ being very popular in many applications (e.g. image restoration, colorization and noise removal), it may be scarcely reliable due to subjectivity issues and biases. Therefore, an objective evaluation, i.e. an image quality assessment based on visual features extracted from the image and mathematically modelled, is highly desirable, since it guarantees the repeatability of the results and it enables the automation of image quality measurements. Here the crucial point lies in the detection of visual elements salient for IQ. Many objective, numerical measures have been proposed in the literature. They differ from one another in the features considered to be relevant to IQ, and in the presence of a reference image, an image of \u201cperfect\u201d quality with which to compare the image to be evaluated. Objective measures are thus broadly classified as full-reference, reduced-reference or no-reference, according to the availability of reference information. Due to the complexity of the IQ assessment process, a single measure may be not robust and accurate enough to capture and numerically summarize all the aspects concurring to IQ. Therefore, we propose to employ multiple objective IQ measures assembled in a cockpit of objective IQ measures. This cockpit should be designed to offer not only an extensive analysis and overview of features relevant to IQ, but also as a tool to automate the selection of machine vision algorithms devoted to image enhancement. In this work we describe a preliminary version of a cockpit, and we employ it to assess a set of images of the same scene acquired under different conditions, with different devices or even processed by computer algorithms

Gradient attenuation as an emergent property of reset-based Retinex models

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

A cockpit of multiple measures for assessing film restoration quality

In machine vision, the idea of expressing the quality of a films by a single value is very popular. Usually this value is computed by processing a set of image features with the aim of resembling as much as pos- sible a kind of human judgment of the film quality. Since human quality assessment is a complex mech- anism involving many different perceptual aspects, we believe that such approach may scarcely provide a comprehensive analysis. Especially in the field of digital movie restoration, a single score can hardly provide reliable information about the effects of the various restoring operations. For this reason we in- troduce an alternative approach, where a set of measures, describing over time basic global and local visual properties of the film frames, is computed in an unsupervised way and delivered to expert evalu- ators for checking the restoration pipeline and results. The proposed framework can be viewed as a car or airplane cockpit , whose parameters (i.e. the computed measures) are necessary to control the machine status and performance. This cockpit, which is publicly available online, would like to support the digital restoration process and its assessment

Cardiac output monitoring during abdominal aortic cross clamping: a comparison between Vigileo/FloTrac system and transoesophageal Doppler

Cardiac output (CO) monitoring is one of the key points in the hemodynamic evaluation of critically ill patients, and can be useful in various settings of high-risk surgery. There is a lack of evidence that the extensive use of invasive devices in the hemodynamic monitoring has a good impact in terms of outcome [1], and less invasive systems have been proposed. Our aim was to compare the CO estimated by Vigileo/FloTrac with the blood flow in thoracic aorta as measured by transoesophageal Doppler in patients undergoing open abdominal aortic aneurysm repair, during the aortic cross-clamping (AoX) phase. We have measured the Augmentation Index (AI), a parameter related to vascular stiffness, using the applanation tonometry method, in order to have a better understanding of the effect of AoX on blood pressure waves. Methods We enrolled 10 consecutive patients (10 men; age 66 \ub1 6 years) undergoing elective open AAA repair (ASA II to III) under general anesthesia. Radial arterial access was used for semi-invasive determination of blood pressures and CO (APCO) with the Vigileo. An esophageal Doppler was positioned after clinical stabilization. Applanation tonometry was measured just before and after the aortic clamping. Results We found a significant (P < 0.05) increase in CO reported by Vigileo/FloTrac system in the post-clamping phase, when compared with the pre-clamping and basal phases, while the blood flow in thoracic aorta resulted decreased, according with the theory of redistribution of fluids in the splanchnic venous vasculature [2]. There was an important contribution of the wave reflection to the aortic pulse pressure wave after the AoX, as expressed by a significant increase in the AI. Conclusions The Vigileo/FloTrac system appears to overestimate CO after AoX when compared with the measure of blood flow in thoracic aorta, and this result could be influenced by the pulse pressure wave reflection occurring after clamping. In high-risk surgical settings, other situations of rapid change of systemic resistance vessels could be similarly misread, thus suggesting the necessity of a more tailored Vigileo algorithm

A Bayesian belief network for local air quality forecasting

This study is focused on the development of a Bayesian network for air quality assessment and aims at offering a pragmatic and scientifically credible approach to modelling complex systems where substantial uncertainties exist. In particular, the main object is the prediction of the occurrence of suitable conditions for the stagnation of pollutants in a given area. The analytical modeling of the network provides a set of independent nodes, represented by the outputs of a forecasting meteorological Limited Area Model, from which descend the conditions for the stagnation of pollutants in different areas of the city (through measurements of the heuristic pollutant from monitoring stations) and finally the global conditions. The urban area of Genoa (Italy) was selected in order to test the actual capability of the model prototype. Network training was performed by means of historical data resulting from significant statistical series of the past years by the air quality-monitoring network. The system used for data assimilation, construction and network learning is completely based on an open source statistical processing software

Engineering Cu2O Nanowire Surfaces for Photoelectrochemical Hydrogen Evolution Reaction

Cu2O is a narrow band gap material serving as an important candidate for photoelectrochemical hydrogen evolution reaction. However, the main challenge that hinders its practical exploitation is its poor photostability, due to its oxidation into CuO by photoexcited holes. Here, we thoroughly minimize the photo-oxidation of Cu2O nanowires by growing a thin layer of the TiO2 protective layer and an amorphous layer of the VOx cocatalyst using magnetron sputtering and atomic layer deposition, respectively. After optimization of the protective and the cocatalyst layers, the photoelectrode exhibits a current density of -2.46 mA/cm2 under simulated sunlight (100 mW/cm2) at 0.3 V versus reversible hydrogen electrode, and its performance is stable for an extended illumination time. The chemical stability and the good performance of the engineered photoelectrode demonstrate the potential of using earth-abundant materials as a light-harvesting device for solar hydrogen production

The regional economic impact of more graduates in the labour market: a “micro-to-macro” analysis for Scotland

This paper explores the system-wide impact of graduates on the regional economy. Graduates enjoy a significant wage premium, often interpreted as reflecting their greater productivity relative to non-graduates. If this is so there is a clear and direct supply-side impact of HEI activities on regional economies. We use an HEI-disaggregated computable general equilibrium model of Scotland to estimate the impact of the growing proportion of graduates in the Scottish labour force that is implied by the current participation rate and demographic change, taking the graduate wage premium in Scotland as an indicator of productivity enhancement. While the detailed results vary with alternative assumptions about the extent to which wage premia reflect productivity, they do suggest that the long-term supply-side impacts of HEIs provide a significant boost to regional GDP. Furthermore, the results suggest that the supply-side impacts of HEIs are likely to be more important than the expenditure impacts that are the focus of most HEI impact studies

Review and Comparison of Random Spray Retinex and of its variants STRESS and QBRIX

In this paper, we review and compare three spatial color algorithms of the Milano Retinex family: Random Spray Retinex (RSR) and its subsequent variants STRESS and QBRIX. These algorithms process the colors of any input image in line with the principles of the Retinex theory, introduced about 50 years ago by Land and McCann to explain how humans see colors. According to this theory, RSR, STRESS and QBRIX re-scale independently the color intensities of each pixel by a quantity, named local reference white, which depends on the spatial arrangement of the colors in the pixel surround. The output is a new color enhanced image that generally has a higher brightness and more visible details than the input one. RSR, STRESS and QBRIX adopt different models of spatial arrangement and implement different equations for the computation of the local reference white, so that they produce different enhanced images. We propose a comparative analysis of their performance based on numerical measures of the image brightness, details and dynamic range. In order to enable result repeatability and further comparisons, we use a set of images publicly available on the net

Using pixel intensity as a self- regulating threshold for deterministic image sampling in Milano Retinex : the T-Rex algorithm

Milano Retinexes are spatial color algorithms, part of the Retinex family, usually employed for image enhancement. They modify the color of each pixel taking into account the surrounding colors and their position, catching in this way the local spatial color distribution relevant to image enhancement. We present T-Rex (from the words threshold and Retinex), an implementation of Milano Retinex, whose main novelty is the use of the pixel intensity as a self-regulating threshold to deterministically sample local color information. The experiments, carried out on real-world pictures, show that T-Rex image enhancement performance are in line with those of the Milano Retinex family: T-Rex increases the brightness, the contrast, and the flatness of the channel distributions of the input image, making more intelligible the content of pictures acquired under difficult light conditions