Topic
Tone mapping
About: Tone mapping is a research topic. Over the lifetime, 1713 publications have been published within this topic receiving 48490 citations.
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TL;DR: A generalized LIP (GLIP) model is developed that not only provides new insights into the LIP model but also defines new image representations and operations for solving general image processing problems that are not necessarily related to the GVS.
Abstract: The logarithmic image processing (LIP) model is a mathematical theory providing generalized linear operations for image processing. The gigavision sensor (GVS) is a new imaging device that can be described by a statistical model. In this paper, by studying these two seemingly unrelated models, we develop a generalized LIP (GLIP) model. With the LIP model being its special case, the GLIP model not only provides new insights into the LIP model but also defines new image representations and operations for solving general image processing problems that are not necessarily related to the GVS. A new parametric LIP model is also developed. To illustrate the application of the new scalar multiplication operation, we propose an energy-preserving algorithm for tone mapping, which is a necessary step in image dehazing. By comparing with results using two state-of-the-art algorithms, we show that the new scalar multiplication operation is an effective tool for tone mapping.
23 citations
TL;DR: The experimental results show that the HDR images predicted by the proposed iTM-Net have higher-quality than the HDR ones predicted by conventional inverse tone mapping methods, including the state of the art, in terms of both HDR-VDP-2.2 and PU encoding + MS-SSIM.
Abstract: In this paper, we propose a novel inverse tone mapping network, called “iTM-Net.” For training iTM-Net, we also propose a novel loss function that considers the non-linear relation between low dynamic range (LDR) and high dynamic range (HDR) images. For inverse tone mapping with convolutional neural networks (CNNs), we first point out that training CNNs with a standard loss function causes a problem due to the non-linear relation between the LDR and HDR images. To overcome the problem, the novel loss function non-linearly tone-maps target HDR images into LDR ones on the basis of a tone mapping operator, and the distance between the tone-mapped images and predicted ones are then calculated. The proposed loss function enables us not only to normalize the HDR images but also to reduce the non-linear relation between LDR and HDR ones. The experimental results show that the HDR images predicted by the proposed iTM-Net have higher-quality than the HDR ones predicted by conventional inverse tone mapping methods, including the state of the art, in terms of both HDR-VDP-2.2 and PU encoding + MS-SSIM. In addition, compared with loss functions that do not consider the non-linear relation, the proposed loss function is shown to improve the performance of CNNs.
22 citations
TL;DR: The proposed method simply applies a low-pass filter to the generated tone curves for video frames to avoid flickering during the adaptation of the method to the video, and develops a generic methodology to compromise the trade-off between HDR and LDR image qualities for coding.
Abstract: Backward compatibility for high dynamic range image and video compression forms one of the essential requirements in the transition phase from low dynamic range (LDR) displays to high dynamic range (HDR) displays. In a recent work [1], the problems of tone mapping and HDR video coding are originally fused together in the same mathematical framework, and an optimized solution for tone mapping is achieved in terms of the mean square error (MSE) of the logarithm of luminance values. In this paper, we improve this pioneer study in three aspects by considering its three shortcomings. First, the proposed method [1] works over the logarithms of luminance values which are not uniform with respect to Human Visual System (HVS) sensitivity. We propose to use the perceptually uniform luminance values as an alternative for the optimization of tone mapping curve. Second, the proposed method [1] does not take the quality of the resulting tone mapped images into account during the formulation in contrary to the main goal of tone mapping research. We include the LDR image quality as a constraint to the optimization problem and develop a generic methodology to compromise the trade-off between HDR and LDR image qualities for coding. Third, the proposed method [1] simply applies a low-pass filter to the generated tone curves for video frames to avoid flickering during the adaptation of the method to the video. We instead include an HVS based flickering constraint to the optimization and derive a methodology to compromise the trade-off between the rate-distortion performance and flickering distortion. The superiority of the proposed methodologies is verified with experiments on HDR images and video sequences.
22 citations
TL;DR: A subjective experiment attempting to determine users’ preference with respect to these two types of content in two different viewing scenarios—with and without the HDR reference shows that the absence of the reference can significantly influence the subjects' preferences for the natural images, while no significant impact has been found in the case of the synthetic images.
Abstract: The popularity of high dynamic range (HDR) imaging has grown in both academic and private research sectors. Since the native visualization of HDR content still has its limitations, the importance of dynamic range compression (i.e., tone-mapping) is very high. This paper evaluates observers’ preference of experience in context of image tone-mapping. Given the different nature of natural and computer-generated content, the way observers perceive the quality of tone-mapped images can be fundamentally different. In this paper, we describe a subjective experiment attempting to determine users’ preference with respect to these two types of content in two different viewing scenarios—with and without the HDR reference. The results show that the absence of the reference can significantly influence the subjects’ preferences for the natural images, while no significant impact has been found in the case of the synthetic images. Moreover, we introduce a benchmarking framework and compare the performance of selected objective metrics. The resulting dataset and framework are made publicly available to provide a common test bed and methodology for evaluating metrics in the considered scenario.
22 citations
TL;DR: An integrated gamut- and tone-management framework for color-accurate reproduction of high dynamic range images can prevent hue and luminance shifts while taking gamut boundaries into consideration.
Abstract: Few tone mapping operators (TMOs) take color management into consideration, limiting compression to luminance values only. This could lead to changes in image chroma and hues, which are typically managed with a post-processing step. However, current post-processing techniques for tone reproduction do not explicitly consider the target display gamut. Gamut mapping, on the other hand, deals with mapping images from one color gamut to another, usually smaller, gamut but has traditionally focused on smaller scale, chromatic changes. The authors present a combined gamut- and tone-management framework for color-accurate reproduction of high dynamic range images that can prevent hue and luminance shifts while taking gamut boundaries into consideration. Their approach is conceptually and computationally simple, parameter-free, and compatible with existing TMOs.
22 citations