Mixing tone mapping operators on the GPU by differential zone mapping based on psychophysical experiments

© 2016 In this paper, we present a new technique for displaying High Dynamic Range (HDR) images on Low Dynamic Range (LDR) displays in an efficient way on the GPU. The described process has three stages. First, the input image is segmented into luminance zones. Second, the tone mapping operator (TMO) that performs better in each zone is automatically selected. Finally, the resulting tone mapping (TM) outputs for each zone are merged, generating the final LDR output image. To establish the TMO that performs better in each luminance zone we conducted a preliminary psychophysical experiment using a set of HDR images and six different TMOs. We validated our composite technique on several (new) HDR images and conducted a further psychophysical experiment, using an HDR display as the reference that establishes the advantages of our hybrid three-stage approach over a traditional individual TMO. Finally, we present a GPU version, which is perceptually equal to the standard version but with much improved computational performance

A local model of eye adaptation for high dynamic range images

In the real world, the human eye is confronted with a wide range of luminances from bright sunshine to low night light. Our eyes cope with this vast range of intensities by adaptation; changing their sensitivity to be responsive at di erent illumination levels. This adaptation is highly localized, allowing us to see both dark and bright regions of a high dynamic range environment. In this paper we present a new model of eye adaptation based on physiological data. The model, which can be easily integrated into existing renderers, can function either as a static local tone mapping operator for single high dynamic range image, or as a temporal adaptation model taking into account time elapsed and intensity of preadaptation for a dynamic sequence. We nally validate our technique with a high dynamic range display and a psychophysical study.(undefined

Reverse tone mapping for suboptimal exposure conditions

La mayor parte de las imágenes y videos existentes son de bajo rango dinámico (generalmente denominado LDR por las siglas del término en inglés, low dynamic range). Se denominan así porque, al utilizar sólo 8 bits por canal (R,G,B) para almacenarlas, sólo son capaces de reproducir dos órdenes de magnitud en luminancia (mientras que el sistema visual humano puede percibir hasta cinco órdenes de magnitud simultáneamente). En los últimos años hemos asistido al nacimiento y expansión de las tecnologías de alto rango dinámico (HDR por sus siglas en inglés), que utilizan hasta 32 bits/canal, permitiendo representar más fielmente el mundo que nos rodea. Paulatinamente el HDR se va haciendo más presente en los pipelines de adquisición, procesamiento y visualización de imágenes, y como con el advenimiento de cualquier nueva tecnología que sustituye a una anterior, surgen ciertos problemas de compatibilidad. En particular, el presente trabajo se centra en el problema denominado reverse tone mapping: dado un monitor de alto rango dinámico, cuál es la forma óptima de visualizar en él todo el material ya existente en bajo rango dinámico (imágenes, vídeos...). Lo que hace un operador de reverse tone mapping (rTMO) es tomar la imagen LDR como entrada y ajustar el contraste de forma inteligente para dar una imagen de salida que reproduzca lo más fielmente posible la escena original. Dado que hay información de la escena original que se ha perdido irreversiblemente al tomar la fotografía en LDR, el problema es intrínsecamente ill-posed o mal condicionado. En este trabajo, en primer lugar, se ha realizado una serie de experimentos psicofísicos utilizando un monitor HDR Brightside para evaluar el funcionamiento de los operadores de reverse tone mapping existentes. Los resultados obtenidos muestran que los actuales operadores fallan -o no ofrecen resultados convincentes- cuando las imágenes de entrada no están expuestas correctamente. Los rTMO existentes funcionan bien con imágenes bien expuestas o subexpuestas, pero la calidad percibida se degrada sustancialmente con la sobreexposición, hasta el punto de que en algunos casos los sujetos prefieren las imágenes originales en LDR a imágenes que han sido procesadas con rTMOs. Teniendo esto en cuenta, el segundo paso ha sido diseñar un rTMO para esos casos en los que los algoritmos existentes fallan. Para imágenes de entrada sobreexpuestas, proponemos un rTMO simple basado en una expansión gamma que evita los errores introducidos por otros métodos, así como un método para fijar automáticamente un valor de gamma para cada imagen basado en el key de la imagen y en datos empíricos. En tercer lugar se ha hecho la validación de los resultados, tanto mediante experimentos psicofísicos como utilizando una métrica objetiva de reciente publicación. Por otro lado, se ha realizado también otra serie de experimentos con el monitor HDR que sugieren que los artefactos espaciales introducidos por los operadores de reverse tone mapping son más determinantes de cara a la calidad final percibida por los sujetos que imprecisiones en las intensidades expandidas. Adicionalmente, como subproyecto menor, se ha explorado la posibilidad de abordar el problema desde un enfoque de más alto nivel, incluyendo información semántica y de saliencia. La mayor parte de este trabajo ha sido publicada en un artículo publicado en la revista Transactions on Graphics (índice JCR 2009 2/93 en la categoría de Computer Science, Software Engineering, con un índice de impacto a 5 años de 5.012, el más alto de su categoría). Además, el Transactions on Graphics está considerado como la mejor revista en el campo de informática gráfica. Otra publicación que cubre parte de este trabajo ha sido aceptada en el Congreso Español de Informática Gráfica 2010. Como medida adicional de la relevancia del trabajo aquí presentado, los dos libros existentes hasta la fecha (hasta donde sabemos) escritos por expertos en el campo de HDR dedican varias páginas a tratar el trabajo aquí expuesto (ver [2, 3]). Esta investigación ha sido realizada en colaboración con Roland Fleming, del Max Planck Institute for Biological Cybernetics, y Olga Sorkine, de New York University

Inverse tone mapping

The introduction of High Dynamic Range Imaging in computer graphics has produced a novelty in Imaging that can be compared to the introduction of colour photography or even more. Light can now be captured, stored, processed, and finally visualised without losing information. Moreover, new applications that can exploit physical values of the light have been introduced such as re-lighting of synthetic/real objects, or enhanced visualisation of scenes. However, these new processing and visualisation techniques cannot be applied to movies and pictures that have been produced by photography and cinematography in more than one hundred years. This thesis introduces a general framework for expanding legacy content into High Dynamic Range content. The expansion is achieved avoiding artefacts, producing images suitable for visualisation and re-lighting of synthetic/real objects. Moreover, it is presented a methodology based on psychophysical experiments and computational metrics to measure performances of expansion algorithms. Finally, a compression scheme, inspired by the framework, for High Dynamic Range Textures, is proposed and evaluated

High-fidelity colour reproduction for high-dynamic-range imaging

The aim of this thesis is to develop a colour reproduction system for high-dynamic-range (HDR) imaging. Classical colour reproduction systems fail to reproduce HDR images because current characterisation methods and colour appearance models fail to cover the dynamic range of luminance present in HDR images. HDR tone-mapping algorithms have been developed to reproduce HDR images on low-dynamic-range media such as LCD displays. However, most of these models have only considered luminance compression from a photographic point of view and have not explicitly taken into account colour appearance. Motivated by the idea to bridge the gap between crossmedia colour reproduction and HDR imaging, this thesis investigates the fundamentals and the infrastructure of cross-media colour reproduction. It restructures cross-media colour reproduction with respect to HDR imaging, and develops a novel cross-media colour reproduction system for HDR imaging. First, our HDR characterisation method enables us to measure HDR radiance values to a high accuracy that rivals spectroradiometers. Second, our colour appearance model enables us to predict human colour perception under high luminance levels. We first built a high-luminance display in order to establish a controllable high-luminance viewing environment. We conducted a psychophysical experiment on this display device to measure perceptual colour attributes. A novel numerical model for colour appearance was derived from our experimental data, which covers the full working range of the human visual system. Our appearance model predicts colour and luminance attributes under high luminance levels. In particular, our model predicts perceived lightness and colourfulness to a significantly higher accuracy than other appearance models. Finally, a complete colour reproduction pipeline is proposed using our novel HDR characterisation and colour appearance models. Results indicate that our reproduction system outperforms other reproduction methods with statistical significance. Our colour reproduction system provides high-fidelity colour reproduction for HDR imaging, and successfully bridges the gap between cross-media colour reproduction and HDR imaging

Perceived dynamic range of HDR images

Although high dynamic range (HDR) imaging has gained great popularity and acceptance in both the scientific and commercial domains, the relationship between perceptually accurate, content-independent dynamic range and objective measures has not been fully explored. In this paper, a new methodology for perceived dynamic range evaluation of complex stimuli in HDR conditions is proposed. A subjective study with 20 participants was conducted and correlations between mean opinion scores (MOS) and three image features were analyzed. Strong Spearman correlations between MOS and objective DR measure and between MOS and image key were found. An exploratory analysis reveals that additional image characteristics should be considered when modeling perceptually-based dynamic range metrics. Finally, one of the outcomes of the study is the perceptually annotated HDR image dataset with MOS values, that can be used for HDR imaging algorithms and metric validation, content selection and analysis of aesthetic image attributes

A Model of Local Adaptation

The visual system constantly adapts to different luminance levels when viewing natural scenes. The state of visual adaptation is the key parameter in many visual models. While the time-course of such adaptation is well understood, there is little known about the spatial pooling that drives the adaptation signal. In this work we propose a new empirical model of local adaptation, that predicts how the adaptation signal is integrated in the retina. The model is based on psychophysical measurements on a high dynamic range (HDR) display. We employ a novel approach to model discovery, in which the experimental stimuli are optimized to find the most predictive model. The model can be used to predict the steady state of adaptation, but also conservative estimates of the visibility(detection) thresholds in complex images.We demonstrate the utility of the model in several applications, such as perceptual error bounds for physically based rendering, determining the backlight resolution for HDR displays, measuring the maximum visible dynamic range in natural scenes, simulation of afterimages, and gaze-dependent tone mapping

Modelling Surround-aware Contrast Sensitivity for HDR Displays

Despite advances in display technology, many existing applications rely on psychophysical datasets of human perception gathered using older, sometimes outdated displays. As a result, there exists the underlying assumption that such measurements can be carried over to the new viewing conditions of more modern technology. We have conducted a series of psychophysical experiments to explore contrast sensitivity using a state-of-the-art HDR display, taking into account not only the spatial frequency and luminance of the stimuli but also their surrounding luminance levels. From our data, we have derived a novel surroundaware contrast sensitivity function (CSF), which predicts human contrast sensitivity more accurately. We additionally provide a practical version that retains the benefits of our full model, while enabling easy backward compatibility and consistently producing good results across many existing applications that make use of CSF models. We show examples of effective HDR video compression using a transfer function derived from our CSF, tone-mapping, and improved accuracy in visual difference prediction

A model of perceived dynamic range for HDR images

For High Dynamic Range (HDR) content, the dynamic range of an image is an important characteristic in algorithm design and validation, analysis of aesthetic attributes and content selection. Traditionally, it has been computed as the ratio between the maximum and minimum pixel luminance, a purely objective measure; however, the human visual system's perception of dynamic range is more complex and has been largely neglected in the literature. In this paper, a new methodology for measuring perceived dynamic range (PDR) of chromatic and achromatic HDR images is proposed. PDR can benefit HDR in a number of ways: for evaluating inverse tone mapping operators and HDR compression methods; aesthetically; or as a parameter for content selection in perceptual studies. A subjective study was conducted on a data set of 36 chromatic and achromatic HDR images. Results showed a strong agreement across participants' allocated scores. In addition, a high correlation between ratings of the chromatic and achromatic stimuli was found. Based on the results from a pilot study, five objective measures (pixel-based dynamic range, image key, area of bright regions, contrast and colorfulness) were selected as candidates for a PDR predictor model; two of which have been found to be significant contributors to the model. Our analyses show that this model performs better than individual metrics for both achromatic and chromatic stimuli