The increased interest in consumer-grade high dynamic range (HDR) images and videos in recent years has caused a proliferation of HDR deghosting algorithms. Despite numerous proposals, a fast, memory-efficient, and robust algorithm has been difficult to achieve. This paper addresses this problem by leveraging the power of attention and U-Net-based neural representations and using a conservative deghosting strategy. Given two bracketed exposures of a scene, we produce an HDR image that maximally resembles the high exposure where it is well-exposed and fuses aligned information from both exposures otherwise. We evaluate the performance of our algorithm under several different challenging scenarios, using both visual and quantitative results, and show that it matches the state-of-the-art algorithms despite using only two exposures and has significantly lower computational complexity. Furthermore, the parameters of our algorithm greatly simplify deploying its different versions for devices with a variety of computational constraints, including mobile devices.

An important problem in autonomous driving is to perceive objects even under challenging illumination conditions. Despite this problem, existing solutions use low-dynamic range (LDR) images for object detection for autonomous driving. In this paper, we provide a novel analysis on whether high-dynamic range (HDR) images can provide better performance for object detection for autonomous driving. To this end, we choose a seminal deep object detector and systematically evaluate its performance when trained with (i) LDR images, (ii) HDR images, and (iii) tone-mapped LDR images for scenes with different illuminations. We show that a detector with HDR images pre-processed with normalization and gamma correction can only marginally perform better than a detector with LDR or tone-mapped LDR images. Our analysis of this unexpected finding reveals that a detector with HDR images requires significantly more samples as the space of HDR images is significantly larger than that of LDR images.

With the progress of autonomous vehicles, the sensing of the environment in more detail with higher dynamic ranges has become more important to classify surrounding objects and obstacles. While stereo HDR images for this purpose can provide advantages compared to the conventional LDR images, they suffer from limited dynamic ranges and spike-like noises due to the inaccuracies in disparity estimation. In this paper, we formulate the HDR image construction problem from trifocal multi-exposure images and develop a method which improves disparity estimation for better HDR image construction. Given the symmetric geometry of the trifocal setup, the proposed method uses the equivalence of disparities from middle to left and middle to right images to determine the reliable regions. The HDR radiance for the pixels in these reliable regions are estimated by using the weighted average of the warped images in different exposures and the middle image, whereas the radiance values outside the reliable regions are estimated by using only the middle image. The experiments with different exposure combinations for left, middle and right images reveal better performances of the proposed method compared to the stereo HDR imaging. It is also observed that the improvements are more apparent for larger disparities between the cameras.

Abstract High dynamic range (HDR) technology allows more of the lighting in a specific scene to be captured at a set point in time, and thus is capable of delivering an overall view of the scene that more closely correlates with our visual experience in the real world, compared to standard, or low dynamic range (LDR) technology. Although HDR capabilities of single exposure capture systems are improving, the traditional method for creating HDR images still includes combing a number of different exposures, captured with an LDR system, into a single HDR image. Several use cases requiring absolute calibration of the resulting HDR luminance map have been undertaken, but none of these have provided a detailed analysis of the optical effects of glare on the results. We develop a calibrated HDR radiance map, including methodical linearization of captured image data, while characterizing the limitations due to the effects of optical glare. A purposely designed controlled test scene is used to challenge the calibrated reconstruction efforts, including low luminance levels, spatial inclusion of lens vignette over the full imaged area, and optical glare. Results demonstrate that even with careful processing and recombination of the LDR data, radiometric accuracy is limited as a result of glare. The proposed approach performs better than calibration methods in commercially available HDR recombination software.

Recently, there has been a significant progress in the research and development of the high dynamic range (HDR) video technology and the state-of-the-art video pipelines are able to offer a higher bit depth support to capture, store, encode, and display HDR video content. In this paper, we introduce a novel HDR video compression algorithm, which uses a perceptually uniform color opponent space, a novel perceptual transfer function to encode the dynamic range of the scene, and a novel error minimization scheme for accurate chroma reproduction. The proposed algorithm was objectively and subjectively evaluated against four state-of-the-art algorithms. The objective evaluation was conducted across a set of 39 HDR video sequences, using the latest x265 10-bit video codec along with several perceptual and structural quality assessment metrics at 11 different quality levels. Furthermore, a rating-based subjective evaluation (n=40) was conducted with six sequences at two different output bitrates. Results suggest that the proposed algorithm exhibits the lowest coding error amongst the five algorithms evaluated. Additionally, the rate-distortion characteristics suggest that the proposed algorithm outperforms the existing state-of-the-art at bitrates ≥ 0.4 bits/pixel.

Virtual Environments (VEs) provide the opportunity to simulate a wide range of applications, from training to entertainment, in a safe and controlled manner. For applications which require realistic representations of real world environments, the VEs need to provide multiple, physically accurate sensory stimuli. However, simulating all the senses that comprise the human sensory system (HSS) is a task that requires significant computational resources. Since it is intractable to deliver all senses at the highest quality, we propose a resource distribution scheme in order to achieve an optimal perceptual experience within the given computational budgets. This paper investigates resource balancing for multi-modal scenarios composed of aural, visual and olfactory stimuli. Three experimental studies were conducted. The first experiment identified perceptual boundaries for olfactory computation. In the second experiment, participants ( N=25) were asked, across a fixed number of budgets ( M=5), to identify what they perceived to be the best visual, acoustic and olfactory stimulus quality for a given computational budget. Results demonstrate that participants tend to prioritize visual quality compared to other sensory stimuli. However, as the budget size is increased, users prefer a balanced distribution of resources with an increased preference for having smell impulses in the VE. Based on the collected data, a quality prediction model is proposed and its accuracy is validated against previously unused budgets and an untested scenario in a third and final experiment.

Abstract Fidelity is of key importance if virtual environments are to be used as authentic representations of real environments. However, simulating the multitude of senses that comprise the human sensory system is computationally challenging. With limited computational resources, it is essential to distribute these carefully in order to simulate the most ideal perceptual experience. This paper investigates this balance of resources across multiple scenarios where combined audiovisual stimulation is delivered to the user. A subjective experiment was undertaken where participants (N=35) allocated five fixed resource budgets across graphics and acoustic stimuli. In the experiment, increasing the quality of one of the stimuli decreased the quality of the other. Findings demonstrate that participants allocate more resources to graphics; however, as the computational budget is increased, an approximately balanced distribution of resources is preferred between graphics and acoustics. Based on the results, an audiovisual quality prediction model is proposed and successfully validated against previously untested budgets and an untested scenario.

Abstract Accurate simulation of all the senses in virtual environments is a computationally expensive task. Visual saliency models have been used to improve computational performance for rendered content, but this is insufficient for multi‐modal environments. This paper considers cross‐modal perception and, in particular, if and how olfaction affects visual attention. Two experiments are presented in this paper. Firstly, eye tracking is gathered from a number of participants to gain an impression about where and how they view virtual objects when smell is introduced compared to an odourless condition. Based on the results of this experiment a new type of saliency map in a selective‐rendering pipeline is presented. A second experiment validates this approach, and demonstrates that participants rank images as better quality, when compared to a reference, for the same rendering budget.

Virtual environments (VEs) grant the ability to experience real-world scenarios, such as driving, in a virtual, safe, and reproducible context. However, in order to achieve their full potential, the fidelity of the VE must provide confidence that it replicates the perception of the real-world experience. The computational cost of simulating real-world visuals accurately means that compromises to the fidelity of the visuals must be made. In this paper, a subjective evaluation of driving in a VE at different quality settings is presented. Participants (n = 44) were driven around in the real world and in a purposely built representative VE and the fidelity of the graphics and overall experience at low-, medium-, and high-visual settings were analyzed. Low quality corresponds to the illumination in many current traditional simulators, medium to a higher quality using accurate shadows and reflections, and high to the quality experienced in modern movies and simulations that require hours of computation. Results demonstrate that graphics quality affects the perceived fidelity of the visuals and the overall experience. When judging the overall experience, participants could tell the difference between the lower quality graphics and the rest but did not significantly discriminate between the medium and higher graphical settings. This indicates that future driving simulators should improve the quality, but once the equivalent of the presented medium quality is reached, they may not need to do so significantly.

High dynamic range (HDR) imaging enables the full range of light in a scene to be captured, transmitted and displayed. However, uncompressed 32-bit HDR is four times larger than traditional low dynamic range (LDR) imagery. If HDR is to fulfil its potential for use in live broadcasts and interactive remote gaming, fast, efficient compression is necessary for HDR video to be manageable on existing communications infrastructure. A number of methods have been put forward for HDR video compression. However, these can be relatively complex and frequently require the use of multiple video streams. In this paper, we propose the use of a straightforward Power Transfer Function (PTF) as a practical, computationally fast, HDR video compression solution. The use of PTF is presented and evaluated against four other HDR video compression methods. An objective evaluation shows that PTF exhibits improved quality at a range of bit-rates and, due to its straightforward nature, is highly suited for real-time HDR video applications.

Abstract A major challenge in generating high‐fidelity virtual environments (VEs) is to be able to provide realism at interactive rates. The high‐fidelity simulation of light and sound is still unachievable in real time as such physical accuracy is very computationally demanding. Only recently has visual perception been used in high‐fidelity rendering to improve performance by a series of novel exploitations; to render parts of the scene that are not currently being attended to by the viewer at a much lower quality without the difference being perceived. This paper investigates the effect spatialized directional sound has on the visual attention of a user towards rendered images. These perceptual artefacts are utilized in selective rendering pipelines via the use of multi‐modal maps. The multi‐modal maps are tested through psychophysical experiments to examine their applicability to selective rendering algorithms, with a series of fixed cost rendering functions, and are found to perform significantly better than only using image saliency maps that are naively applied to multi‐modal VEs.

This paper presents the results of a user study which quantifies the relative and absolute quality of example-based texture synthesis algorithms. In order to allow such evaluation, a list of texture properties is compiled, and a minimal representative set of textures is selected to cover these. Six texture synthesis methods are compared against each other and a reference on a selection of twelve textures by non-expert participants (N = 67). Results demonstrate certain algorithms successfully solve the problem of texture synthesis for certain textures, but there are no satisfactory results for other types of texture properties. The presented textures and results make it possible for future work to be subjectively compared, thus facilitating the development of future texture synthesis methods.

High dynamic range (HDR) video has emerged from research labs around the world and entered the realm of consumer electronics. The dynamic range that a human can see in a scene with minimal eye adaption (approximately 1,000,000:1) is vastly greater than traditional imaging technology which can only capture about 8 f -stops (256:1). HDR technology, on the other hand, has the potential to capture the full range of light in a scene; even more than a human eye can see. This paper examines the field of HDR video from capture to display: past, present and future. In particular the paper looks beyond the current marketing hype around HDR to show how HDR video in the future can and, indeed, should bring about a step change in imaging, analogous to the change from black and white to colour.

Serious games are accepted as an effective approach to deliver augmented feedback in motor (re-)learning processes. The multi-modal nature of the conventional computer games (e.g. audiovisual representation) plus the ability to interact via haptic-enabled inputs provides a more immersive experience. Thus, particular disciplines such as medical education in which frequent hands on rehearsals play a key role in learning core motor skills (e.g. physical palpations) may benefit from this technique. Challenges such as the impracticality of verbalising palpation experience by tutors and ethical considerations may prevent the medical students from correctly learning core palpation skills. This work presents a new data glove, built from off-the-shelf components which captures pressure sensitivity designed to provide feedback for palpation tasks. In this work the data glove is used to control a serious game adapted from the infinite runner genre to improve motor skill acquisition. A comparative evaluation on usability and effectiveness of the method using multimodal visualisations, as part of a larger study to enhance pressure sensitivity, is presented. Thirty participants divided into a game-playing group ( $$n=15$$ n = 15 ) and a control group ( $$n=15$$ n = 15 ) were invited to perform a simple palpation task. The game-playing group significantly outperformed the control group in which abstract visualisation of force was provided to the users in a blind-folded transfer test. The game-based training approach was positively described by the game-playing group as enjoyable and engaging.

The increased interest in high dynamic range (HDR) video over existing low dynamic range (LDR) video during the past decade or so was primarily due to its inherent capability to capture, store and display the full range of real-world lighting visible to the human eye with increased precision. This has led to an inherent assumption that HDR video would be preferable by the end-user over LDR video due to the more immersive and realistic visual experience provided by HDR. This assumption has led to a considerable body of research into efficient capture, processing, storage and display of HDR video. Although this is beneficial for scientific research and industrial purposes, very little research has been conducted to test the veracity of this assumption. In this paper, we conduct two subjective studies by means of a ranking and a rating-based experiment where 60 participants in total, 30 in each experiment, were tasked to rank and rate several reference HDR video scenes along with three mapped LDR versions of each scene on an HDR display, in order of their viewing preference. Results suggest that given the option, end-users prefer the HDR representation of the scene over its LDR counterpart.

Abstract Since high dynamic range (HDR) displays are not yet widely available, there is still a need to perform a dynamic range reduction of HDR content to reproduce it properly on standard dynamic range (SDR) displays. The most common techniques for performing this reduction are termed tone‐mapping operators (TMOs). Although mobile devices are becoming widespread, methods for displaying HDR content on these SDR screens are still very much in their infancy. While several studies have been conducted to evaluate TMOs, few have been done with a goal of testing small screen displays (SSDs), common on mobile devices. This paper presents an evaluation of six state‐of‐the‐art HDR video TMOs. The experiments considered three different levels of ambient luminance under which 180 participants were asked to rank the TMOs for seven tone‐mapped HDR video sequences. A comparison was conducted between tone‐mapped HDR video footage shown on an SSD and on a large screen SDR display using an HDR display as reference. The results show that there are differences between the performance of the TMOs under different ambient lighting levels and the TMOs that perform well on traditional large screen displays also perform well on SSDs at the same given luminance level.

High dynamic range (HDR) imaging has become one of the foremost imaging methods capable of capturing and displaying the full range of lighting perceived by the human visual system in the real world. A number of HDR compression methods for both images and video have been developed to handle HDR data, but none of them has yet been adopted as the method of choice. In particular, the backwards-compatible methods that always maintain a stream/image that allow part of the content to be viewed on conventional displays make use of tone mapping operators which were developed to view HDR images on traditional displays. There are a large number of tone mappers, none of which is considered the best as the images produced could be deemed subjective. This work presents an alternative to tone mapping-based HDR content compression by identifying a single exposure that can reproduce the most information from the original HDR image. This single exposure can be adapted to fit within the bit depth of any traditional encoder. Any additional information that may be lost is stored as a residual. Results demonstrate quality is maintained as well, and better, than other traditional methods. Furthermore, the presented method is backwards-compatible, straightforward to implement, fast and does not require choosing tone mappers or settings.

Visual perception is becoming increasingly important in computer graphics. Research on human visual perception has led to the development of perception-driven computer graphics techniques, where knowledge of the human visual system (HVS) and, in particular, its weaknesses are exploited when rendering and displaying 3D graphics. Findings on limitations of the HVS have been used to maintain high perceived quality but reduce the computed quality of some of the image without this quality difference being perceived. This article investigates the amount of time for which (if at all) such limitations could be exploited in the presence of smell. The results show that for our experiment, adaptation to smell does indeed affect participants’ ability to determine quality difference in the animations. Having been exposed to a smell before undertaking the experiment, participants were able to determine the quality in a similar fashion to the “no smell” condition, whereas without adaptation, participants were not able to distinguish the quality difference.

In this paper we present an efficient method for supporting image based lighting (IBL) for bidirectional methods. This improves both sampling of the environment, and the detection and sampling of important regions of the scene, such as windows and doors. These parts of the scene often have a small area proportional to that of the entire scene, so paths which pass through them are generated with a low probability. The method proposed in this paper improves sampling efficiency, by taking into account view importance, and modifies the lighting distribution to use light transport information from the camera. This method automatically constructs a sampling distribution in locations which are relevant to the camera position, thereby improving sampling of light paths. This approach can be applied to several bidirectional rendering methods, and results are shown for bidirectional path tracing, metropolis light transport and progressive photon mapping. When compared to other methods, efficiency results demonstrate speed ups of orders of magnitude.

When preparing high dynamic range images (HDR) for display on standard monitors, it is often necessary to make a choice between global and local tone mapping. While the former is simple and efficient, it may fail to reproduce details in high contrast image regions. Although, the latter can better reproduce details in such regions, it often comes at the cost of increased complexity and computational time. In this paper, we present an algorithm that combines the best of both approaches. We perform local tone mapping only in high frequency image regions where the visibility of details can be an issue. In low frequency regions, we employ global tone mapping to save computational resources without degrading quality. Our algorithm is most suitable for tone mapping operators (TMOs) that utilize the concept of local adaptation luminances.

A number of novel imaging technologies have been gaining popularity over the past few years. Foremost among these are stereoscopy and high dynamic range (HDR) Imaging. While a large body of research has looked into each of these imaging technologies independently, very little work has attempted to combine them. This is mostly due to the current limitations in capture and display. In this article, we mitigate problems of capturing Stereoscopic HDR (SHDR) that would potentially require two HDR cameras, by capturing an HDR and LDR pair and using it to generate 3D stereoscopic HDR content. We ran a detailed user study to compare four different methods of generating SHDR content. The methods investigated were the following: two based on expanding the luminance of the LDR image, and two utilizing stereo correspondence methods, which were adapted for our purposes. Results demonstrate that one of the stereo correspondence methods may be considered perceptually indistinguishable from the ground truth (image pair captured using two HDR cameras), while the other methods are all significantly distinct from the ground truth.

In this paper we present a new technique for the display of High Dynamic Range (HDR) images on Low Dynamic Range (LDR) displays. 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 reference, that establishes the advantages of our hybrid three-stage approach over a traditional individual TMO.

Abstract Rendering using physically based methods requires substantial computational resources. Most methods that are physically based use straightforward techniques that may excessively compute certain types of light transport, while ignoring more important ones. Importance sampling is an effective and commonly used technique to reduce variance in such methods. Most current approaches for physically based rendering based on Monte Carlo methods sample the BRDF and cosine term, but are unable to sample the indirect illumination as this is the term that is being computed. Knowledge of the incoming illumination can be especially useful in the case of hard to find light paths, such as caustics or scenes which rely primarily on indirect illumination. To facilitate the determination of such paths, we propose a caching scheme which stores important directions, and is analytically sampled to calculate important paths. Results show an improvement over BRDF sampling and similar illumination importance sampling.

Abstract High‐fidelity rendering can be used to investigate Cultural Heritage (CH) sites in a scientifically rigorous manner. However, a high degree of realism in the reconstruction of a CH site can be misleading insofar as it can be seen to imply a high degree of certainty about the displayed scene—which is frequently not the case, especially when investigating the past. So far, little effort has gone into adapting and formulating a Predictive Rendering pipeline for CH research applications. In this paper, we first discuss the goals and the workflow of CH reconstructions in general, as well as those of traditional Predictive Rendering. Based on this, we then propose a research framework for CH research, which we refer to as ‘Cultural Heritage Predictive Rendering’ (CHPR). This is an extension to Predictive Rendering that introduces a temporal component and addresses uncertainty that is important for the scene’s historical interpretation. To demonstrate these concepts, two example case studies are detailed.

Abstract In recent years research in the three‐dimensional sound generation field has been primarily focussed upon new applications of spatialized sound. In the computer graphics community the use of such techniques is most commonly found being applied to virtual, immersive environments. However, the field is more varied and diverse than this and other research tackles the problem in a more complete, and computationally expensive manner. Furthermore, the simulation of light and sound wave propagation is still unachievable at a physically accurate spatio‐temporal quality in real time. Although the Human Visual System (HVS) and the Human Auditory System (HAS) are exceptionally sophisticated, they also contain certain perceptional and attentional limitations. Researchers, in fields such as psychology, have been investigating these limitations for several years and have come up with findings which may be exploited in other fields. This paper provides a comprehensive overview of the major techniques for generating spatialized sound and, in addition, discusses perceptual and cross‐modal influences to consider. We also describe current limitations and provide an in‐depth look at the emerging topics in the field.

Abstract In the last decade, an increasing number of techniques have been developed to reproduce high dynamic range imagery on traditional displays. These techniques, known as Tone Mapping Operators (TMOs), have been compared and ranked in different ways according to several image characteristics. However, none of these algorithms has been developed specifically for small screen devices (SSD). In this paper, we present an evaluation of currently used TMOs to show that SSDs with limited size, resolution and colour depth require specific research to find or create an appropriate solution. The research described in this paper is based on psychophysical experiments; using three different types of displays (CRT, LCD and SSD). The obtained results show that rankings obtained are similar for the LCD and CRT but are significantly different for the SSD. Furthermore, these rankings show additionally that some characteristics of TMOs need to be emphasized to obtain better high‐fidelity mapped images for SSDs.

A major obstacle for real-time rendering of high-fidelity graphics is computational complexity. A key point to consider in the pursuit of “realism in real time” in computer graphics is that the Human Visual System (HVS) is a fundamental part of the rendering pipeline. The human eye is only capable of sensing image detail in a 2 ˆ foveal region, relying on rapid eye movements, or saccades, to jump between points of interest. These points of interest are prioritized based on the saliency of the objects in the scene or the task the user is performing. Such “glimpses” of a scene are then assembled by the HVS into a coherent, but inevitably imperfect, visual perception of the environment. In this process, much detail, that the HVS deems unimportant, may literally go unnoticed. Visual science research has identified that movement in the background of a scene may substantially influence how subjects perceive foreground objects. Furthermore, recent computer graphics work has shown that both fixed viewpoint and dynamic scenes can be selectively rendered without any perceptual loss of quality, in a significantly reduced time, by exploiting knowledge of any high-saliency movement that may be present. A high-saliency movement can be generated in a scene if an otherwise static objects starts moving. In this article, we investigate, through psychophysical experiments, including eye-tracking, the perception of rendering quality in dynamic complex scenes based on the introduction of a moving object in a scene. Two types of object movement are investigated: (i) rotation in place and (ii) rotation combined with translation. These were chosen as the simplest movement types. Future studies may include movement with varied acceleration. The object's geometry and location in the scene are not salient. We then use this information to guide our high-fidelity selective renderer to produce perceptually high-quality images at significantly reduced computation times. We also show how these results can have important implications for virtual environment and computer games applications.

Abstract In the last few years, researchers in the field of High Dynamic Range (HDR) Imaging have focused on providing tools for expanding Low Dynamic Range (LDR) content for the generation of HDR images due to the growing popularity of HDR in applications, such as photography and rendering via Image‐Based Lighting, and the imminent arrival of HDR displays to the consumer market. LDR content expansion is required due to the lack of fast and reliable consumer level HDR capture for still images and videos. Furthermore, LDR content expansion, will allow the re‐use of legacy LDR stills, videos and LDR applications created, over the last century and more, to be widely available. The use of certain LDR expansion methods, those that are based on the inversion of Tone Mapping Operators (TMOs), has made it possible to create novel compression algorithms that tackle the problem of the size of HDR content storage, which remains one of the major obstacles to be overcome for the adoption of HDR. These methods are used in conjunction with traditional LDR compression methods and can evolve accordingly. The goal of this report is to provide a comprehensive overview on HDR Imaging, and an in depth review on these emerging topics.

Abstract In recent years inverse tone mapping techniques have been proposed for enhancing low‐dynamic range (LDR) content for a high‐dynamic range (HDR) experience on HDR displays, and for image based lighting. In this paper, we present a psychophysical study to evaluate the performance of inverse (reverse) tone mapping algorithms. Some of these techniques are computationally expensive because they need to resolve quantization problems that can occur when expanding an LDR image. Even if they can be implemented efficiently on hardware, the computational cost can still be high. An alternative is to utilize less complex operators; although these may suffer in terms of accuracy. Our study investigates, firstly, if a high level of complexity is needed for inverse tone mapping and, secondly, if a correlation exists between image content and quality. Two main applications have been considered: visualization on an HDR monitor and image‐based lighting.

Abstract The ability to interactively render dynamic scenes with global illumination is one of the main challenges in computer graphics. The improvement in performance of interactive ray tracing brought about by significant advances in hardware and careful exploitation of coherence has rendered the potential of interactive global illumination a reality. However, the simulation of complex light transport phenomena, such as diffuse interreflections, is still quite costly to compute in real time. In this paper we present a caching scheme, termed Instant Caching, based on a combination of irradiance caching and instant radiosity. By reutilising calculations from neighbouring computations this results in a speedup over previous instant radiosity‐based approaches. Additionally, temporal coherence is exploited by identifying which computations have been invalidated due to geometric transformations and updating only those paths. The exploitation of spatial and temporal coherence allows us to achieve superior frame rates for interactive global illumination within dynamic scenes, without any precomputation or quality loss when compared to previous methods; handling of lighting and material changes are also demonstrated.

Abstract Recent advances have made interactive ray tracing (IRT) possible on consumer desktop machines. These advances have brought about the potential for interactive global illumination (IGI) with enhanced realism through physically based lighting. IGI, unlike IRT, has a much higher computational complexity. Furthermore, since non‐primary rays constitute the majority of the computation, the rays are predominantly incoherent, making impractical many of the methods that have made IRT possible. Two methods that have already shown promise in decreasing the computational time of the GI solution are interleaved sampling and adaptive rendering. Interleaved sampling is a generalized sampling scheme that smoothly blends between regular and irregular sampling while maintaining coherence. Adaptive rendering algorithms adjust rendering quality, non‐uniformally, using a guidance scheme. While adaptive rendering has shown to provide speed‐up when used for off‐line rendering it has not been utilized in IRT due to its naturally incoherent nature. In this paper, we combine adaptive rendering and interleaved sampling within a component‐based solution into a new approach we term adaptive interleaved sampling. This allows us to tailor new adaptive heuristics for interleaved sampling of the individual components of the GI solution significantly improving overall performance. We present a novel component‐based IGI framework for which we achieve interactive frame rates for a range of effects such as indirect diffuse lighting, soft shadows and single scatter homogeneous participating media.

Mobile devices, also known as small-form-factor (SFF) devices such as mobile phones, PDAs and ultra mobile PCs have continued to grow in popularity. Improvements in SFF hardware has enabled a range of suitable applications such as gaming, interactive visualisation and mobile mapping. Although high-fidelity graphic systems typically have significant computational requirements, the time taken may be largely resolution dependent. The limited resolution of SFFs indicates such platforms are prime candidates for running high-fidelity graphics.

High-fidelity image synthesis is the process of computing images that are perceptually indistinguishable from the real world they are attempting to portray. Such a level of fidelity requires that the physical processes of materials and the behavior of light are accurately simulated. Most computer graphics algorithms assume that light passes freely between surfaces within an environment. However, in many applications, we also need to take into account how the light interacts with media, such as dust, smoke, fog, etc., between the surfaces. The computational requirements for calculating the interaction of light with such participating media are substantial. This process can take many hours and rendering effort is often spent on computing parts of the scene that may not be perceived by the viewer. In this paper, we present a novel perceptual strategy for physically based rendering of participating media. By using a combination of a saliency map with our new extinction map (X map), we can significantly reduce rendering times for inhomogeneous media. The visual quality of the resulting images is validated using two objective difference metrics and a subjective psychophysical experiment. Although the average pixel errors of these metric are all less than 1%, the subjective validation indicates that the degradation in quality still is noticeable for certain scenes. We thus introduce and validate a novel light map (L map) that accounts for salient features caused by multiple light scattering around light sources.

A map can be a major advantage when exploring unfamiliar environments. With the proliferation of mobile devices, such as PDAs and even mobile phones, the tourist industry is currently exploring the potential of new presentation strategies that will maximize the promotional appeal of tourism in their region. Mobile devices are capable of guiding a tourist when he/she is exploring a city. These mobile devices offer the potential for providing relevant 3D information to enable tourists to locate themselves within the city, rapidly navigate around the unfamiliar environment and explore it interactively. However, the computational resources of current mobile technology prevents the display of full complex 3D content in real-time, and thus selective rendering techniques must be adopted to ensure the viewer is provided with the perceptually most important information at interactive rates. This paper presents a series of experiments which help to identify key features of a scene for users to orientate themselves in that environment. Knowledge of these salient key features enable them to be provided to a user at a high quality while the remainder of the scene can be rendered in a much lower quality, saving significant bandwidth and computing power, without the user being perceptually aware of this difference in quality within the image.

Many applications require that an image will appear the same regardless of where or how it is displayed. However, the conditions in which an image is displayed can adversely affect its appearance. Computer monitor screens not only emit light, but can also reflect extraneous light present in the viewing environment. This can cause images displayed on a monitor to appear faded by reducing their perceived contrast. Current approaches to this problem involve measuring this ambient illumination with specialized hardware and then altering the display device or changing the viewing conditions. This is not only impractical, but also costly and time consuming. For a user who does not have the equipment, expertise, or budget to control these facets, a practical alternative is sought. This paper presents a method whereby the display device itself can be used to determine the effect of ambient light on perceived contrast, thus enabling the viewers themselves to perform visual calibration. This method is grounded in established psychophysical experimentation and we present both an extensive procedure and an equivalent rapid procedure. Our work is extended by providing a novel method of contrast correction so that the contrast of an image viewed in bright conditions can be corrected to appear the same as an image viewed in a darkened room. This is verified through formal validation. These methods are easy to apply in practical settings, while accurate enough to be useful.

The perception that we have of our world depends on the task we are currently performing in the environment, so if we are driving a car we will pay attention to the objects that are visually important to the task we are performing such as, the road, road signs, other vehicles, etc. The same is true when we explore virtual environments. The creation of high-fidelity 3D maps on mobile devices to aid navigation in urban environments is computationally very expensive, precluding achieving this quality at interactive rates. In this paper we present a case study to show how the human visual system may be exploited, when viewers are undertaking a task, to reduce the overall quality of the displayed image, without the users being aware of this reduction in quality. The displayed images are selectively rendered with the key features used to identify location and orientation in a 3D urban environment produced in high quality and the remainder of the image in low quality.

Tone mapping operators are designed to reproduce visibility and the overall impression of brightness, contrast and color of the real world onto limited dynamic range displays and printers. Although many tone mapping operators have been published in recent years, no thorough psychophysical experiments have yet been undertaken to compare such operators against the real scenes they are purporting to depict. In this paper, we present the results of a series of psychophysical experiments to validate six frequently used tone mapping operators against linearly mapped High Dynamic Range (HDR) scenes displayed on a novel HDR device. Individual operators address the tone mapping issue using a variety of approaches and the goals of these techniques are often quite different from one another. Therefore, the purpose of this investigation was not simply to determine which is the "best" algorithm, but more generally to propose an experimental methodology to validate such operators and to determine the participants' impressions of the images produced compared to what is visible on a high contrast ratio display.

Finding one's way around an unfamiliar city can be a major challenge. While maps can provide a very good abstract representation of our world, and a simple and efficient way to navigate within that world, they are of little use when, for example, the absence of road signs prevents us from locating where we are on the map. Mobile devices offer the potential for providing relevant 3D information to enable us to locate ourselves, rapidly navigate around an unfamiliar environment and explore it interactively. However, mobile devices are constrained by resources such as bandwidth, storage and small displays. In this paper we investigate which is the most important visual information for position location within an unfamiliar urban environment and show how we can use this knowledge to provide a perceptually high quality 3D virtual environment on existing mobile devices.

Abstract This paper surveys a number of the implementations of Linda that are available in Java. It provides some discussion of their strengths and weaknesses, and presents the results from benchmarking experiments using a network of commodity workstations. Some extensions to the original Linda programming model are also presented and discussed, together with examples of their application to parallel processing problems. Copyright © 2004 John Wiley & Sons, Ltd.

Accurate light simulation is of highest importance in archaeological reconstructions when you want to investigate how a site might have looked like in the past. Failure to use the highest fidelity means there is a very real danger of misrepresenting the past. This sketch describes the high fidelity reconstruction and realistic lighting simulation of an ancient Egyptian temple.

There are two major influences on human visual attention: bottom-up and top-down processing. Bottom-up processing is the automatic direction of gaze to lively or colourful objects as determined by low-level vision. In contrast, top-down processing is consciously directed attention in the pursuit of predetermined goals or tasks. Previous work in perception-based rendering has exploited bottom-up visual attention to control detail (and therefore time) spent on rendering parts of a scene. In this paper, we demonstrate the principle of Inattentional Blindness, a major side effect of top-down processing, where portions of the scene unrelated to the specific task go unnoticed. In our experiment, we showed a pair of animations rendered at different quality levels to 160 subjects, and then asked if they noticed a change. We instructed half the subjects to simply watch our animation, while the other half performed a specific task during the animation.When parts of the scene, outside the focus of this task, were rendered at lower quality, almost none of the task-directed subjects noticed, whereas the difference was clearly visible to the control group. Our results clearly show that top-down visual processing can be exploited to reduce rendering times substantially without compromising perceived visual quality in interactive tasks.

Abstract Ray tracing is a well known technique for producing realistic computer images. The computational requirements of this method are such that optimisation techniques, for example space subdivision, must be used if complex scenes are to be rendered in reasonable times. Constructive solid geometry (CSG) is a method for describing the geometry of complex scenes by applying set operations to primitive objects. The status tree approach has been used successfully within ray tracing to evaluate CSG structures. This paper proposes a combination of the status tree and space subdivision techniques as a means to improve further the efficiency of ray tracing.