Illuminance Analysis from High Dynamic Range Images

Feature Point (FP) detection is a fundamental step in computer vision tasks. Although FP detectors are mostly designed to support Low Dynamic Range (LDR) images as input, interest in High Dynamic Range (HDR) images has increased recently due to their higher precision to register overexposed and underexposed areas in an image. As the detection of FPs is strongly dependent on the illumination of the scene, HDR images have the potential to be more robust than LDR images during FP detection. Known FP detectors, however, do not use the full potential of HDR images. In addition, few works have evaluated the performance of HDR images in this context. In this paper, we propose a modification of FP detectors aiming to improve FP detection on HDR images. To this end, we design a local mask based on the Coefficient of Variation (CV) of sets of pixels, creating thus a new step in FP detection. We compare our approach with popular FP detection methods using a standard evaluation metric, Repeatability Rate (RR) of FPs, and also Uniformity as a proposed new criterion. A dataset of images from scenes affected by camera transformations and substantial illumination changes was used as input. Experimental results show that our proposed algorithms give better Uniformity and RR in most HDR images from the dataset when compared to standard FP detectors. Moreover, they indicate that HDR images present a great potential to be explored in applications that rely on FP detection.

High dynamic range (HDR) images are becoming pervasive due to capturing or rendering of a wider range of luminance, but their special display equipment is difficult to be popularized because of high cost and technological problem. Thus, HDR images must be adapted to the conventional display devices by applying tone mapping (TM) operation, which puts forward higher requirements for intellectual property protection of HDR images. As the robustness presents regional diversity in the low dynamic range (LDR) watermarked image after TM, which is different from the traditional watermarking technologies, a concept of watermarking activity is defined and used to distinguish the essential distinction of watermarking between LDR image and HDR image in this paper. Then, a novel robust HDR image watermarking algorithm is proposed against TM operations. Firstly, based on the hybrid processing of redundant discrete wavelet transform and singular value decomposition, the watermark is embedded by modifying the structure information of the HDR image. Distinguished from LDR image watermarking, the high embedding strength can cause more obvious distortion in the high brightness regions of HDR image than the low brightness regions. Thus, a perceptual brightness mask with low complexity is designed to improve the imperceptibility further. Experimental results show that the proposed algorithm is robust to the existing TM operations, with taking into account the imperceptibility and embedded capacity, which is superior to the current state-of-art HDR image watermarking algorithms.

Recently, visual representations using high dynamic range (HDR) images become increasingly popular, with advancement of technologies for increasing the dynamic range of image. HDR image is expected to be used in wide-ranging applications such as digital cinema, digital photography and next generation broadcast, because of its high quality and its powerful expression ability. HDR imaging technologies will spread its sphere of influence in imaging industry. In this paper, we review the state-of-the-art studies and the trends of the HDR imaging, in terms of the following three points: (1) HDR imaging sensor and HDR image generation techniques as image acquisition technologies, (2) encode method of HDR images for efficient transmission and storage, (3) human visual system issues associated with reproduction of HDR image.

Typical digital cameras exhibit a limitation regarding the dynamic range of the scene radiance they can capture. High Dynamic Range (HDR) imaging refers to methods and systems that aim to generate images that exhibit higher dynamic range between the lightest and the darkest parts of the an image. A typical approach for generating HDR images is exposure bracketing where multiple frames, each one with a different exposure setting, are captured and combined to a HDR image of the scene. The large number of images that exposure bracketing requires often leads to motion artefacts that limit the visual quality of the resulting HDR image. In this work, we propose a novel approach in HDR imaging that significantly reduces the necessary number of images. In our proposed system, we employ the notion of random exposure where each pixel of a single frame collects light for a random amount of time. By collecting a small number of such images, the full sequence of low dynamic range images can be reconstructed and subsequently used for HDR generation. The problem is solved by casting the reconstruction of the sequence as a nuclear norm minimization problem following the premises of the recently proposed theory of Matrix Completion. Experimental results suggest that the proposed method is able to reconstruct the sequence from as low as 20% of the images that traditional techniques require with minimal reduction in image quality.

Tone-mapping-operators (TMOs) provide a useful means for converting high dynamic range (HDR) images to low dynamic range (LDR) images so that they can be viewed on standard displays, but this may influence the visual quality of experience (QoE) of the end-user. There is a need to understand the impact of TMOs to inform the choice of TMO algorithms for different displays, especially for small-screen-devices (SSDs) such as those used in mobile phones. This is important, as mobile devices are becoming the primary means of consuming multimedia contents. However, few studies have been undertaken to assess the impact of TMOs and viewing devices (especially SSDs) on the visual QoE of the user when using. In this chapter, we evaluate subjectively and objectively, the commonly used TMOs in different displays and resolutions for colour and grey-scale HDR images. Our results show that viewing devices have an influence on the TMOs performance, suggesting the need for a careful choice of TMO to enhance the viewing-QoE of the end-user. As expected, the higher resolution, the better HDR-image quality. Surprisingly, there was no significant difference between the Mean of Opinion Score (MOS) scores for colour and grey-scale images in SSDs. The device and TMOs affect QoE for colour and grey HDR-image equally. We found Shannon entropy (SE) to be a good objective measure of quality for colour and grey HDR images, suggesting that entropy may find use in automated HDR quality control assessment schemes, while; HDR-VDP-2 is a good objective measure for colour HDR image only.

High Dynamic Range (HDR) images capture the full range of luminance present in real world scenes, and unlike Low Dynamic Range (LDR) images, can simultaneously contain detailed information in the deepest of shadows and the brightest of light sources. In order to render HDR image on LDR displayers, it is often necessary to create LDR depictions of HDR images at the cost of contrast information loss. To reduce the loss, this paper enables to render HDRI (High Dynamic Range Image) with multiple low-bit images periodically. From the viewpoint of a human, the pixel value is fractural. It does not adjust the tones but can reconstruct HDR images.

High dynamic range (HDR) images are highly desirable and widely applied in our daily life. However, most conventional digital cameras are only able to capture low dynamic range (LDR) images. To produce HDR images, a popular and effective method is to merge multiple LDR images with different exposure settings. To achieve high quality HDR images, determining the exposure settings when capturing the multiple LDR images is of great importance. Though many researchers have paid attention to the exposure determination issue for HDR imaging, most of them have focused on DSLR cameras and surveillance video cameras. Meanwhile, mobile devices nowadays have increasing use but few studies in literature focus on them. Since cameras of mobile devices have some features different from DSLR cameras and surveillance video cameras, many existing exposure determination methods may not be directly applied on them. To fill this research gap, this paper proposes a dynamic exposure determination method for HDR photography on mobile devices, in particular on iOS mobile devices. In the proposed method, three LDR images are captured on iOS devices, which are then merged to generate an HDR result image that can cover more details of the target scene. Considering the time and memory limits of mobile devices, the proposed method analyzes the histogram of the target scene thumbnail to determine the three exposure settings with suitable exposure gaps. Based on our proposed method, we have developed an iOS HDR Camera App. The experimental results demonstrate that the HDR result images of this App are capable to contain more details in both bright area and dark area than those of Apple's default HDR Camera App on iPhone 5s for real-world target scenes.

Physical or virtual models are commonly employed to visualize the conceptual ideas of architects, lighting designers and daylighting researchers. The models are also used to assess the daylighting performance of their buildings, particularly when Complex Fenestration Systems (CFS) are considered. Recent studies have revealed a general tendency of physical models to over- estimate the performance, usually expressed through work plane illuminance and daylight factor profiles, when compared to that of the real building. These discrepancies can be attributed to several experimental errors. To analyze the main sources of error, a set of comparisons between a real building, a virtual model and a physical model was undertaken. The real building in our case consisted of a full scale test module with a south-facing windows designed for experimentation on daylighting systems. A virtual model was a computed model created in Radiance program while the physical model was a scale model (1:10) of the real case. The fenestration systems considered in this study were a simple window (double glazing) and two CFS (Laser-cut panel and Prismatic film). The physical model was placed in outdoor conditions similar to that of the real building as well as under a scanning sky simulator (for both real sky luminance distribution and CIE standard sky); the virtual model simulations were carried out with the program Radiance using the GenSky function (for CIE standard sky) and the Partial Daylight Factor (PDF) method, the later using the real sky luminance distribution acquired by a digital sky scanner at the same time as the real building's daylight performance was assessed. The daylighting performances of the building, daylight factor (DF) for overcast sky and illuminance ratio (IR) for clear sky, were monitored using illuminance meters: a set of sensors for exterior illuminance and another set of equally spaced 7 sensors placed at 1m intervals starting from the window plane for the interior space were used for that purpose. The interior surface luminance of both real building and physical model was measured using a luminance meter and a High Dynamic Range (HDR) imaging technique (within the Photosphere program). The Radiance program was used to determine the interior surface luminance within the virtual model. The measured performance of the real case, physical models and virtual models were compared, the causes of discrepancies between the real building and models were analyzed. The causes of errors that were evaluated were modeling of building details and dimensions, CFS modeling, mocking-up of the photometric properties (surface reflectance and window transmittance), model location as well as photometer features. To study the impact of these error sources on daylighting performance assessment, virtual models created using the Radiance program were used to achieve a sensitivity analysis of modeling errors. The significant factors were considered, leading to a set of modeling guidelines. The experimental study shows that large discrepancies can occur in daylighting performance figures. For example if glazings are omitted from the model's window, a relative divergence of 25% to 40% can be found at different points in the room, suggesting more light entering than actually measured in the real building. Inaccuracy in window transmittance inaccuracy is a major cause of errors commonly found in daylight modeling. In addition, significant discrepancies can be caused by even slight error in surface reflectance values. Only 10% overestimation of surface reflectance modeling leads up to 80% relative errors in work plane illuminance for a simple window and up to 90% for the assessment of CFS. Continuous sky distribution presented more accurate results than 145 sky sectors simulation, particularly when CFS were evaluated. These discrepancies can be reduced by making an effort to mock up the geometric and photometric features including the daylight simulation of the models carefully. A checklist presented in this thesis can be used as a guideline to help the daylight designers to estimate and avoid errors when assessing daylighting performance.

A well-known technique in high dynamic range (HDR) imaging is to take multiple photographs, each one with a different exposure time, and then combine them to produce an HDR image. Unless the scene is static and the camera position is fixed, this process creates the so-called artifacts. In order to handle non-static scenes or moving camera, images have to be spatially registered. This is a challenging problem because most optical flow estimation algorithm depends on the constant brightness assumption, which is obviously not the case in HDR imaging. In this paper, we present an algorithm to estimate the dense motion field in image sequences with photometric variations. In an alternating optimization scheme, the algorithm estimates both the dense motion field and the photometric mapping. As a latent information, the occluded regions are extracted and excluded from the photometric mapping estimation. We include experiments with both synthetic and real imagery to demonstrate the efficacy of the proposed algorithm. We show that the ghosting artifacts are reduced significantly in HDR imaging of non-static scenes.

In this paper, we propose a bit-depth scalable lossless coding method for high dynamic range (HDR) images based on a reversible logarithmic mapping. HDR images are generally expressed as floating-point data, such as in the OpenEXR or RGBE formats. Our bit-depth scalable coding approach outputs base layer data and enhancement layer data. It can reconstruct the low dynamic range (LDR) image from the base layer data and reconstructs the HDR image by adding the enhancement layer data. Most previous two-layer methods have focused on the lossy coding of HDR images. Unfortunately, the extension of previous lossy methods to lossless coding does not significantly compress the enhancement layer data. This is because the bit depth becomes very large, especially for HDR images in floating-point data format. To tackle this problem, we apply a reversible logarithmic mapping to the input HDR data. Moreover, we introduce a format conversion to avoid any degradation in the quality of the reconstructed LDR image. The proposed method is effective for both OpenEXR and RGBE formats. Through a series of experiments, we confirm that the proposed method decreases the volume of compressed data while maintaining the visual quality of the reconstructed LDR images.

<p>The objective of this research was to determine the adequacy of Android devices capturing High Dynamic Range (HDR) photography, and using it as a tool for daylight analysis in New Zealand’s commercial building stock. This study was conducted with an Android Smartphone and later an Android Tablet, employing the use of a US$50 magnetic fisheye lens. The overall aim of this research was to evaluate whether an inexpensive programmable data acquisition system could provide meaningful and useful luminance data. To complete this research, the adequacy of computer simulation using HDR photography of the real horizontal and vertical skies was explored. Using the method documented in this research, the luminance distribution of the building interiors could then be mapped accurately in daylight simulations. The BRANZ Building Energy End-Use Study (BEES) team currently have one internal lighting measurement point, which records light levels in each of more than 100 commercial buildings randomly selected to be representative of commercial buildings in New Zealand. The HOBO U12 data logger typically records the environmental data on a desktop within the main area of the monitored premises. The HOBO data loggers only provide the environmental measurement of that specific location and do not provide the researcher the daylight distribution of the whole space. Using the data collected by BEES, a thesis was developed to explore the utility of HDR imaging as a supplement to the use of a single internal light measurement in the analysis of daylight potential in New Zealand’s commercial building stock. Three buildings were randomly selected from the BEES targeted strata five database to be monitored over a one day period. Within each building, at least three rooms were studied, all facing different orientations. The pilot study and the first two buildings monitored employed the use of a Motorola Defy Smartphone to capture the low dynamic range (LDR) photographs of each scene using both the HDR Camera application available from the Android Google Play Application Store, and the built-in camera application that came with the Smartphone. The vertical (by pressing the Smartphone hard up against the window) and horizontal (from the ground) skies were also captured simultaneously as only one device was available at each monitored building and to ensure consistency in each building. These photographs were fused using an HDR software called Photosphere, into a single HDR image. However, before the HDR images could be generated to contain accurate luminance data within the images, a camera response curve is required to be generated. A camera response curve is unique to each device and only needs to be generated once and can be generated using Photosphere. Unfortunately, a camera response curve could not be generated for the Motorola Defy Smartphone and through various experimentations and tests in both the lighting laboratory and in-field, it was discovered that this had nothing to do with the EXIF data contained within the photographs captured as originally thought, but the JPEG image format itself. This resulted in a generic camera response curve, from Photosphere, being used for the pilot study and the first two monitored buildings. For the final building that was monitored, a Galaxy Note Tablet was used. A camera response curve for this device could be easily generated using Photosphere. The pilot study and three monitored buildings were geometrically simulated using Google SketchUp 8 and were then exported in to Radiance Lighting Simulation and Rendering System using the su2rad plug-in. The files were then edited in Ecotect™ Radiance Control Panel, after which the real and simulated images were compared using HDRShop and RadDisplay. The four comparison methods were used to compare the real and simulated data were pixel to pixel comparison; section to section pixel comparison; surface to surface comparison and visual field comparison. Of the four methods used the first two were visual based comparisons, whereas the latter two were numerical, which employ the use of a calculation method to calculate the relative error percentages. The biggest problem that arose from the visual comparisons was the geometrical misalignment due to the use of a fisheye lens and only provided the luminance difference ranging from a scale of 0 cd/m2 to 50 cd/m2. The numerical comparison methods provided a 60% correlation between real and simulated data. It was concluded that, depending on the Android device used, HDR photographs are able to provide reliable images that contain accurate luminance data when a camera response curve for the device could be generated.</p>

The present paper proposes a blind multi-bit watermarking method for High Dynamic Range (HDR) images. The proposed approach is designed in order to guarantee the watermark imperceptibility both in the HDR marked image and in its Low Dynamic Range (LDR) counterpart, being thus robust against significant non-linear distortions such as those performed by tone-mapping operators (TMOs). In order to do so, the wavelet transform of the Just Noticeable Difference (JND)- scaled space of the original HDR image is employed as embedding domain. Moreover, a visual mask taking into account specific aspects of the Human Visual System (HVS) is exploited to improve the quality of the resulting watermarked image. Specifically, bilateral filtering is used to locate information on the detail part of the HDR image, where the watermark should be preferably embedded. A contrast sensitivity function is also employed to modulate the watermark intensity in each wavelet decomposition subband according to its scale and orientation. An extensive set of experimental results testifies the effectiveness of the proposed scheme in embedding multi-bit watermarks into HDR images without affecting the visual quality of the original image, while being robust against TMOs.

Ghost detection and removal for high dynamic range images: Recent advances

This paper addresses the conversion problem of High Dynamic Range (HDR) images into Low Dynamic Range (LDR) images. The aim of this conversion is to ensure a good visual rendering of the displayed Tone Mapped (TM) HDR images in accordance with the observers' assessment. To do so, the proposed algorithm adjusts a piecewise linear function to the logarithm luminance distribution of the HDR image to adapt this transformation to the perceptual quantizer in accordance with the Human Visual System (HVS). The computation of the slope value of the piecewise linear transformation is simplified. Indeed, it is given by the ratio between the probability and the logarithm luminance distance in the sampling bin. This leads to histograms which tend to be flat, thus inducing a contrast enhancement of the tone mapped HDR images in both under-exposed and overexposed areas. Simulation results provide good results, both in terms of visual quality and TMQI metric, compared to existing competitive TM approaches.

In this work, a reversible data hiding (RDH) algorithm is proposed for high dynamic range (HDR) images containing an additional luminance channel. Since prediction accuracy is the key of RDH, we propose to embed data into HDR images by exploiting the edge information among the luminance channel and color channels to achieve accurate prediction and high embedding capacity. Besides, a new edge-directed order is presented to reduce the visual loss of the stego image. Various experimental results demonstrate that the proposed algorithm can hide more data into HDR images with less distortion than directly applying traditional RDH methods designed for low dynamic range (LDR) images. Compared to the current HDR hiding algorithms, the proposed method is not only reversible, but also achieves a tradeoff between embedding capacity and distortion.