To visualize HDR contents on low dynamic range displays, a fast and efficient TMO is often preferred. One way to achieve this is to use a Global TMO. However, Global TMO often results in poor contrast tone-mapped images and often needs a postprocess that enhances the contrast, such as Unsharp Masking. This work illustrates that such Unsharp Masking can be directly integrated into several global TMOs, resulting in an alternative framework to apply the Unsharp Masking to the HDR visualization pipeline. The proposed framework is fast and delivers images with a proper contrast without the need for the additional image sharpening at the post process.
High Dynamic Range (HDR) and Wide Color Gamut (WCG) displays require adapted color measurements analysis. In this paper, we evaluate the viewing angle dependence of the color gamut and color volume of two HDR/WCG displays, one QLED TV and one OLED TV measured using a Fourier optics viewing angle system. The analysis is made using L*a*b* color space and ICtCp color space recently proposed by Dolby laboratories. The different interests of the ICtCp color space for direct comparison of the displays is discussed.
Computing dynamic range of high dynamic range (HDR) content is an important procedure when selecting the test material, designing and validating algorithms, or analyzing aesthetic attributes of HDR content. It can be computed on a pixel-based level, measured through subjective tests or predicted using a mathematical model. However, all these methods have certain limitations. This paper investigates whether dynamic range of modeled images with no semantic information, but with the same first order statistics as the original, natural content, is perceived the same as for the corresponding natural images. If so, it would be possible to improve the perceived dynamic range (PDR) predictor model by using additional objective metrics, more suitable for such synthetic content. Within the subjective study, three experiments were conducted with 43 participants. The results show significant correlation between the mean opinion scores for the two image groups. Nevertheless, natural images still seem to provide better cues for evaluation of PDR.
Recent work in prediction of overall HDR and WCG display quality has shown that machine learning approaches based on physical measurements performs on par with more advanced perceptually transformed measurements. While combining machine learning with the perceptual transforms did improve over using each technique separately, the improvement was minor. However, that work did not explore how well these models performed when applied to display capabilities outside of the training data set. This new work examines what happens when the machinelearning approaches are used to predict quality outside of the training set, both in terms of extrapolation and interpolation. While doing so, we consider two models – one based on physical display characteristics, and a perceptual model that transforms physical parameters based on human visual system models. We found that the use of the perceptual transforms particularly helps with extrapolation, and without their tempering effects, the machine learning-based models can produce wildly unrealistic quality predictions.
High dynamic range (HDR) imaging has become an important topic in both academic and industrial domains. Nevertheless, the concept of dynamic range (DR), which underpins HDR, and the way it is measured are still not clearly understood. The current approach to measure DR results in a poor correlation with perceptual scores (r ≈ 0.6). In this paper, we analyze the limitations of the existing DR measure, and propose several options to predict more accurately subjective DR judgments. Compared to the traditional DR estimates, the proposed measures show significant improvements in Spearman's and Pearson's correlations with subjective data (up to r ≈ 0.9). Despite their straightforward nature, these improvements are particularly evident in specific cases, where the scores obtained by using the classical measure have the highest error compared to the perceptual mean opinion score.
High Dynamic Range (HDR) imaging has recently been applied to video systems, including the next-generation ultrahigh definition television (UHDTV) format. This format requires a camera with a dynamic range of over 15 f-stops and an S/N ratio that is the same as that of HDTV systems. Current UHDTV cameras cannot satisfy these conditions, as their small pixel size decreases the full-well capacity of UHDTV camera image sensors in comparison with that of HDTV sensors. We propose a four-chip capturing method combining threechip and single-chip systems. A prism divides incident light into two rays with intensities in the ratio m:1. Most of the incident light is directed to the three-chip capturing block; the remainder is directed to a single-chip capturing block, avoiding saturation in high-exposure videos. High quality HDR video can then be obtained by synthesizing the high-quality image obtained from the three-chip system with the low saturation image from the singlechip. Herein, we detail this image synthesis method, discuss the smooth matching method between spectrum characteristics of the two systems, and consider the modulation transfer function (MTF) response differences between the three- and single-chip capturing systems by means of analyzing using human visual models.
Nowadays many cameras embed multi-imaging (MI) technology without always giving the option to the user to explicitly activate or deactivate it. MI means that they capture multiple images, combine them and give a single final image, letting sometimes this procedure being completely transparent to the user. One of the reasons why this technology has become very popular is that natural scenes may have a dynamic range that is larger than the dynamic range of a camera sensor. So as to produce an image without under- or over-exposed areas, several input images are captured and later merged into a single high dynamic range (HDR) result. There is an obvious need for evaluating this new technology. In order to do so, we will present laboratory setups conceived so as to exhibit the characteristics and artifacts that are peculiar to MI, and will propose metrics so as to progress toward an objective quantitative evaluation of those systems. On the first part of this paper we will focus on HDR and more precisely on contrast, texture and color aspects. Secondly, we will focus on artifacts that are directly related to moving objects or moving camera during a multi-exposure acquisition. We will propose an approach to measure ghosting artifacts without accessing individual source images as input, as most of the MI devices most often do not provide them. Thirdly, we will expose an open question arising from MI technology about how the different smartphone makers define the exposure time of the single reconstructed image and will describe our work around a timemeasurement solution. The last part of our study concerns the analysis of the degree of correlation between the objective results computed using the proposed laboratory setup and subjective results on real natural scenes captured using HDR ON and OFF modes of a given device.
The 8K ultra-high-definition television (UHDTV) is a next generation television system with a high realistic sensation. High dynamic range (HDR) is a new standard for television systems, defined in Recommendation ITU-R BT. 2100. Hybrid log-gamma (HLG) is one of the HDR standards, jointly suggested by NHK (Japan Broadcasting Corporation) and BBC (British Broadcasting Corporation), and is highly compatible with the conventional standard dynamic range system. Although a "full-featured" 8K camera with HLG has already been developed, most existing 8K cameras do not comply with the HLG standard. In this paper, we describe a method for adapting existing 8K cameras to HLG, thus enhancing their dynamic range. Based on subjective image quality evaluation results, we propose a guideline to choose the dynamic range setting for each shooting scene, considering the noise performance of the particular used 8K camera.
We describe the implementation of a non-contact motion encoder based on the Near-Sensor Image Processing (NSIP) concept. Rather than computing image displacements between frames we search for "LEP stability" as used successfully in a previously published Time-to-Impact detector. A LEP is a single pixel feature that is tracked during its motion. It is found that this results in a non-complex and fast implementation. As with other NSIP-based solutions, high dynamic range is obtained as the sensor adapts itself to the lighting conditions.