Recent progress in digital photography and storage availability has drastically changed our approach to photo creation. While in the era of film cameras, careful forethought would usually precede the capture of a photo; nowadays, a large number of pictures can be taken with little effort. One of the consequences is the creation of numerous photos depicting the same moment in slightly different ways, which makes the process of organizing photos laborious for the photographer. Nevertheless, photo collection organization is important both for exploring photo albums and for simplifying the ultimate task of selecting the best photos. In this work, we conduct a user study to explore how users tend to organize or cluster similar photos in albums, to what extent different users agree in their clustering decisions, and to investigate how the clustering-defined photo context affects the subsequent photo-selection process. We also propose an automatic hierarchical clustering solution for modeling user clustering decisions. To demonstrate the usefulness of our approach, we apply it to the task of automatic photo evaluation within photo albums and propose a clustering-based context adaptation.

The selection of the best photos in personal albums is a task that is often faced by photographers. This task can become laborious when the photo collection is large and it contains multiple similar photos. Recent advances on image aesthetics and photo importance evaluation has led to the creation of different metrics for automatically assessing a given image. However, these metrics are intended for the independent assessment of an image, without considering the possible context implicitly present within photo albums. In this work, we perform a user study for assessing how users select photos when provided with a complete photo album---a task that better reflects how users may review their personal photos and collections. Using the data provided by our study, we evaluate how existing state-of-the-art photo assessment methods perform relative to user selection, focusing in particular on deep learning based approaches. Finally, we explore a recent framework for adapting independent image scores to collections and evaluate in which scenarios such an adaptation can prove beneficial.

The human visual system (HVS) non-linearly processes light from the real world, allowing us to perceive detail over a wide range of illumination. Although models that describe this non-linearity are constructed based on psycho-visual experiments, they generally apply to a limited range of illumination and therefore may not fully explain the behavior of the HVS under more extreme illumination conditions. We propose a modified experimental protocol for measuring visual responses to emissive stimuli that do not require participant training, nor requiring the exclusion of non-expert participants. Furthermore, the protocol can be applied to stimuli covering an extended luminance range. Based on the outcome of our experiment, we propose a new model describing lightness response over an extended luminance range. The model can be integrated with existing color appearance models or perceptual color spaces. To demonstrate the effectiveness of our model in high dynamic range applications, we evaluate its suitability for dynamic range expansion relative to existing solutions.

Abstract The objective of colour mapping or colour transfer methods is to recolour a given image or video by deriving a mapping between that image and another image serving as a reference. These methods have received considerable attention in recent years, both in academic literature and industrial applications. Methods for recolouring images have often appeared under the labels of colour correction, colour transfer or colour balancing, to name a few, but their goal is always the same: mapping the colours of one image to another. In this paper, we present a comprehensive overview of these methods and offer a classification of current solutions depending not only on their algorithmic formulation but also their range of applications. We also provide a new dataset and a novel evaluation technique called ‘evaluation by colour mapping roundtrip’. We discuss the relative merit of each class of techniques through examples and show how colour mapping solutions can have been applied to a diverse range of problems.

Abstract Mobile phones and tablets are rapidly gaining significance as omnipresent image and video capture devices. In this context we present an algorithm that allows such devices to capture high dynamic range (HDR) video. The design of the algorithm was informed by a perceptual study that assesses the relative importance of motion and dynamic range. We found that ghosting artefacts are more visually disturbing than a reduction in dynamic range, even if a comparable number of pixels is affected by each. We incorporated these findings into a real‐time, adaptive metering algorithm that seamlessly adjusts its settings to take exposures that will lead to minimal visual artefacts after recombination into an HDR sequence. It is uniquely suitable for real‐time selection of exposure settings. Finally, we present an off‐line HDR reconstruction algorithm that is matched to the adaptive nature of our real‐time metering approach.

With the increasing availability of high-dynamic-range (HDR) displays comes the need to remaster existing content in a way that takes advantage of the extended range of luminance and contrast that such displays offer. At the same time, it is crucial that the creative intent of the director is preserved through such changes as much as possible. In this article, we compare several approaches for dynamic range extension to assess their ability to correctly reproduce the color appearance of standard dynamic range (SDR) images on HDR displays. A number of state-of-the-art inverse tone mapping operators (ITMOs) combined with a standard chromatic adaptation transform (CAT) as well as some HDR color appearance models have been evaluated through a psychophysical study, making use of an HDR display as well as HDR ground-truth data. We found that global ITMOs lead to the most reliable performance when combined with a standard CAT, while more complex methods were found to be more scene dependent, and often less preferred than the unprocessed SDR image. HDR color appearance models, albeit being the most complete solutions for accurate color reproduction, were found to not be well suited to the problem of dynamic range expansion, suggesting that further research may be necessary to provide accurate color management in the context of inverse tone mapping.

Abstract Image calibration requires both linearization of pixel values and scaling so that values in the image correspond to real‐world luminances. In this paper we focus on the latter and rather than rely on camera characterization, we calibrate images by analysing their content and metadata, obviating the need for expensive measuring devices or modeling of lens and camera combinations. Our analysis correlates sky pixel values to luminances that would be expected based on geographical metadata. Combined with high dynamic range (HDR) imaging, which gives us linear pixel data, our algorithm allows us to find absolute luminance values for each pixel—effectively turning digital cameras into absolute light meters. To validate our algorithm we have collected and annotated a calibrated set of HDR images and compared our estimation with several other approaches, showing that our approach is able to more accurately recover absolute luminance. We discuss various applications and demonstrate the utility of our method in the context of calibrated color appearance reproduction and lighting design.

Mobile projectors are gaining momentum, with many pocket sized products reaching the market and projector phones being close to production. Although the usefulness of such devices for entertainment, collaboration as well as many other tasks is obvious, it is not yet clear how their limitations in terms of image quality, brightness and jitter due to hand motion might affect performance. To answer these questions, we conduct two experiments based on traditional psychophysical methods. Using a visual search and a text reading study we compare task performance on a mobile phone display and a mobile projector. In addition, we examine whether performance is affected when devices are handheld rather than placed on a stable surface. We find that the perceived task difficulty is worse on the mobile projector cases for both tasks, while only in the visual search task leads to quantitatively worse performance. In contrast, we find that the stability of the projection plays no role in task performance but tasks performed on a handheld device may be perceived as harder to complete.

Managing the appearance of images across different display environments is a difficult problem, exacerbated by the proliferation of high dynamic range imaging technologies. Tone reproduction is often limited to luminance adjustment and is rarely calibrated against psychophysical data, while color appearance modeling addresses color reproduction in a calibrated manner, albeit over a limited luminance range. Only a few image appearance models bridge the gap, borrowing ideas from both areas. Our take on scene reproduction reduces computational complexity with respect to the state-of-the-art, and adds a spatially varying model of lightness perception. The predictive capabilities of the model are validated against all psychophysical data known to us, and visual comparisons show accurate and robust reproduction for challenging high dynamic range scenes.

Abstract The statistics of natural images have attracted the attention of researchers in a variety of fields and have been used as a means to better understand the human visual system and its processes. A number of algorithms in computer graphics, vision and image processing take advantage of such statistical findings to create visually more plausible results. With this report we aim to review the state of the art in image statistics and discuss existing and potential applications within computer graphics and related areas.