LDR Image to HDR Image Mapping with Overexposure Preprocessing

High dynamic range (HDR) image generation had been studied for years. Due to the expense and rareness of HDR cameras, many works try to generate HDR images using several low dynamic range (LDR) images with different exposure conditions. To ensure high-quality HDR image generation, details of the scene should be retained in different LDR images and the exposure parameters of LDR image should be chosen carefully. In this paper, we propose a technique to dynamically determine the suitable exposure parameter for LDR images according to the property of each scene to be captured. Experimental results reveal that better HDR images are always generated with the LDR images captured by the determined exposure, compared to those created by the method with fixed exposures.

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.

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.

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.

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.

A fixed-point tone mapping operation (TMO) instead of a floating-point TMO is proposed in this paper. A TMO generates a low dynamic range (LDR) image from a high dynamic range (HDR) image. Since pixel values of an HDR image are generally expressed in a floating-point data format, e.g. RGBE and OpenEXR, a TMO is also implemented in floating-point arithmetic in conventional approaches. However, it requires a huge computational cost, even though a resulting LDR image is expressed in simple integers. The proposed TMO is implemented with only fixed-point arithmetic. As a result, the proposed method reduces a computational cost. Experimental results shows the PSNR of LDR images in the proposed method are comparable to those of the conventional methods.

New generations of display technologies provide signicantly improved dynamic range over conventional display devices. Current inverse tone mapping schemes require multiple exposure low dynamic range (LDR) images to generate high dynamic range (HDR) images. In this work, we propose an exposure dependent S curve to convert one optimized LDR image to multiple images with different brightness, which are then fused into a virtual real scene HDR image with wide dynamic range. According to our implementation results, the dynamic range can reach about 105. The synthesizer is robust and temporally coherent, and does not require image specific parameter adjustment. This paper also presents the image quality assessment with HDR visual difference predictor (HDR-VDP) and relative entropy contrast, and our work has better performance than other inverse tone mapping operators (iTMOs) for the both image quality assessments.

In modern digital photographs, most images have low dynamic range (LDR) formats, which means that the range of light intensities from the darkest to the brightest is much lower than the range that can be perceived by the human eye. Therefore, to visualize images as naturally as possible on devices that display them in high dynamic range (HDR) format, the LDR images need to be converted into HDR images. The aim of this study was to develop an adaptive inverse tone mapping operator (iTMO) that can convert a single LDR image into a realistic HDR image based on artificial neural networks. In contrast to conventional iTMO algorithms, our technique was developed by learning the complicated relationship between various LDR-HDR pair images, which enabled nearly ground-truth HDR images to be generated from various types of LDR images. The novel learning technique is called cumulative histogram-based learning and color difference learning. The superior performance of our technique over conventional methods was assessed through objective evaluations of various types of LDR and HDR images.

This paper introduces region of interest (ROI) as a functionality to local tone mappings of high dynamic range (HDR) radiance maps. In the proposed scheme, a new quadratic regularization method is provided to decompose the luminance of an HDR radiance map into a base layer with large gradients and a detail layer with small gradients. An ROI based global tone mapping is designed to compress the base layer. Since the dynamic range of a display device is limited, more dynamic ranges are assigned to the base layer in the ROI such that the visual quality of the ROI is preserved better. The detail layer can be amplified to enhance local contrasts such that the output LDR images look sharper. Both automatic and interactive modes are provided. With our automatic mode, the generated low dynamic range (LDR) images look natural and local details are also preserved very well. In addition, a user friendly interactive mode is provided which is also able to produce dynamic LDR images in real time.

Recently, the standardization of backward compatible JPEG image coding for high dynamic range (HDR) image has been undertaken to establish an international standard called “JPEG XT.” The JPEG XT consists of two layers: the base layer and the residual layer. The base layer contains tone mapped low dynamic range (LDR) image data and the residual layer contains the error signal used to reconstruct the HDR image. This paper gives the result of a study to evaluate the overall performance of tone mapping operators (TMOs) for this standard. The evaluation is performed using five HDR image datasets and six TMOs for profiles A, B, and C of the proposed JPEG XT standard. The Tone Mapped image Quality Index (TMQI) and no reference image quality assessment (NR IQA) are used for measuring the LDR image quality. The peak signal to noise ratio (PSNR) is used to evaluate the overall compression performance of JPEG XT profiles A, B, and C. In TMQI and NR IQA measurements, TMOs using display adaptive tone mapping and adaptive logarithmic mapping each gave good results. A TMO using adaptive logarithmic mapping gave good PSNRs.

Recently, high dynamic range (HDR) imaging has attracted much attention as a technology to reflect human visual characteristics owing to the development of the display and camera technology. This paper proposes a novel deep neural network model that reconstructs an HDR image from a single low dynamic range (LDR) image. The proposed model is based on a convolutional neural network composed of dilated convolutional layers and infers LDR images with various exposures and illumination from a single LDR image of the same scene. Then, the final HDR image can be formed by merging these inference results. It is relatively simple for the proposed method to find the mapping between the LDR and an HDR with a different bit depth because of the chaining structure inferring the relationship between the LDR images with brighter (or darker) exposures from a given LDR image. The method not only extends the range but also has the advantage of restoring the light information of the actual physical world. The proposed method is an end-to-end reconstruction process, and it has the advantage of being able to easily combine a network to extend an additional range. In the experimental results, the proposed method shows quantitative and qualitative improvement in performance, compared with the conventional algorithms.

This paper proposes a High Dynamic Range (HDR) image Tone Mapping (TM) approach converting HDR images into Low Dynamic Range (LDR) images so as the tone mapped HDR images ensure good visual quality on LDR display devices in accordance with the observers’ assessment. The proposed algorithm adjusts the logarithm luminance distribution of the HDR image according to a perceptual quantizer with respect to the Human Visual System (HVS) using a piecewise linear function. The parameters’s approximation are induced by L M -norms (l 1 , l 2 , l ∞ ) and the performance of the proposed algorithm is discussed. Simulation results provide good results, both in terms of visual quality and TMQI metric, compared to existing competitive TM approaches.

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 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.

We present a high dynamic range (HDR) imaging algorithm that utilizes a modern rank minimization framework. Linear dependency exists among low dynamic range (LDR) images. However, global or local misalignment by camera motion and moving objects breaks down the low-rank structure of LDR images. The proposed algorithm simultaneously estimates global geometric transforms to align LDR images and detects moving objects and under-/over-exposed regions using a rank minimization approach. In the HDR composition step, structural consistency weighting is proposed to generate an artifact-free HDR image from an user-selected reference image. We demonstrate the robustness and effectiveness of the proposed method with real datasets.