Appearance-based image splitting for HDR display systems

High dynamic range displays that incorporate two optically-coupled image planes have recently been developed. This dual image plane design requires that a given HDR input image be split into two complementary standard dynamic range components that drive the coupled systems, therefore there existing image splitting issue. In this research, two types of HDR display systems (hardcopy and softcopy HDR display) are constructed to facilitate the study of HDR image splitting algorithm for building HDR displays. A new HDR image splitting algorithm which incorporates iCAM06 image appearance model is proposed, seeking to create displayed HDR images that can provide better image quality. The new algorithm has potential to improve image details perception, colorfulness and better gamut utilization. Finally, the performance of the new iCAM06-based HDR image splitting algorithm is evaluated and compared with widely spread luminance square root algorithm through psychophysical studies

High dynamic range display systems

High contrast ratio (CR) enables a display system to faithfully reproduce the real objects. However, achieving high contrast, especially high ambient contrast (ACR), is a challenging task. In this dissertation, two display systems with high CR are discussed: high ACR augmented reality (AR) display and high dynamic range (HDR) display. For an AR display, we improved its ACR by incorporating a tunable transmittance liquid crystal (LC) film. The film has high tunable transmittance range, fast response time, and is fail-safe. To reduce the weight and size of a display system, we proposed a functional reflective polarizer, which can also help people with color vision deficiency. As for the HDR display, we improved all three aspects of the hardware requirements: contrast ratio, color gamut and bit-depth. By stacking two liquid crystal display (LCD) panels together, we have achieved CR over one million to one, 14-bit depth with 5V operation voltage, and pixel-by-pixel local dimming. To widen color gamut, both photoluminescent and electroluminescent quantum dots (QDs) have been investigated. Our analysis shows that with QD approach, it is possible to achieve over 90% of the Rec. 2020 color gamut for a HDR display. Another goal of an HDR display is to achieve the 12-bit perceptual quantizer (PQ) curve covering from 0 to 10,000 nits. Our experimental results indicate that this is difficult with a single LCD panel because of the sluggish response time. To overcome this challenge, we proposed a method to drive the light emitting diode (LED) backlight and the LCD panel simultaneously. Besides relatively fast response time, this approach can also mitigate the imaging noise. Finally yet importantly, we improved the display pipeline by using a HDR gamut mapping approach to display HDR contents adaptively based on display specifications. A psychophysical experiment was conducted to determine the display requirements

High-dynamic-range Foveated Near-eye Display System

Wearable near-eye display has found widespread applications in education, gaming, entertainment, engineering, military training, and healthcare, just to name a few. However, the visual experience provided by current near-eye displays still falls short to what we can perceive in the real world. Three major challenges remain to be overcome: 1) limited dynamic range in display brightness and contrast, 2) inadequate angular resolution, and 3) vergence-accommodation conflict (VAC) issue. This dissertation is devoted to addressing these three critical issues from both display panel development and optical system design viewpoints. A high-dynamic-range (HDR) display requires both high peak brightness and excellent dark state. In the second and third chapters, two mainstream display technologies, namely liquid crystal display (LCD) and organic light emitting diode (OLED), are investigated to extend their dynamic range. On one hand, LCD can easily boost its peak brightness to over 1000 nits, but it is challenging to lower the dark state to \u3c 0.01 nits. To achieve HDR, we propose to use a mini-LED local dimming backlight. Based on our simulations and subjective experiments, we establish practical guidelines to correlate the device contrast ratio, viewing distance, and required local dimming zone number. On the other hand, self-emissive OLED display exhibits a true dark state, but boosting its peak brightness would unavoidably cause compromised lifetime. We propose a systematic approach to enhance OLED\u27s optical efficiency while keeping indistinguishable angular color shift. These findings will shed new light to guide future HDR display designs. In Chapter four, in order to improve angular resolution, we demonstrate a multi-resolution foveated display system with two display panels and an optical combiner. The first display panel provides wide field of view for peripheral vision, while the second panel offers ultra-high resolution for the central fovea. By an optical minifying system, both 4x and 5x enhanced resolutions are demonstrated. In addition, a Pancharatnam-Berry phase deflector is applied to actively shift the high-resolution region, in order to enable eye-tracking function. The proposed design effectively reduces the pixelation and screen-door effect in near-eye displays. The VAC issue in stereoscopic displays is believed to be the main cause of visual discomfort and fatigue when wearing VR headsets. In Chapter five, we propose a novel polarization-multiplexing approach to achieve multiplane display. A polarization-sensitive Pancharatnam-Berry phase lens and a spatial polarization modulator are employed to simultaneously create two independent focal planes. This method enables generation of two image planes without the need of temporal multiplexing. Therefore, it can effectively reduce the frame rate by one-half. In Chapter six, we briefly summarize our major accomplishments

Adaptive image synthesis for compressive displays

Recent years have seen proposals for exciting new computational display technologies that are compressive in the sense that they generate high resolution images or light fields with relatively few display parameters. Image synthesis for these types of displays involves two major tasks: sampling and rendering high-dimensional target imagery, such as light fields or time-varying light fields, as well as optimizing the display parameters to provide a good approximation of the target content. In this paper, we introduce an adaptive optimization framework for compressive displays that generates high quality images and light fields using only a fraction of the total plenoptic samples. We demonstrate the framework for a large set of display technologies, including several types of auto-stereoscopic displays, high dynamic range displays, and high-resolution displays. We achieve significant performance gains, and in some cases are able to process data that would be infeasible with existing methods.University of British Columbia (UBC Four Year Doctoral Fellowship)Natural Sciences and Engineering Research Council of Canada (Postdoctoral Fellowship)United States. Defense Advanced Research Projects Agency (DARPA SCENICC program)Alfred P. Sloan Foundation (Sloan Research Fellowship)United States. Defense Advanced Research Projects Agency (DARPA Young Faculty Award)University of British Columbia (Dolby Research Chair at UBC

Real-Time Under-Display Cameras Image Restoration and HDR on Mobile Devices

The new trend of full-screen devices implies positioning the camera behind the screen to bring a larger display-to-body ratio, enhance eye contact, and provide a notch-free viewing experience on smartphones, TV or tablets. On the other hand, the images captured by under-display cameras (UDCs) are degraded by the screen in front of them. Deep learning methods for image restoration can significantly reduce the degradation of captured images, providing satisfying results for the human eyes. However, most proposed solutions are unreliable or efficient enough to be used in real-time on mobile devices. In this paper, we aim to solve this image restoration problem using efficient deep learning methods capable of processing FHD images in real-time on commercial smartphones while providing high-quality results. We propose a lightweight model for blind UDC Image Restoration and HDR, and we also provide a benchmark comparing the performance and runtime of different methods on smartphones. Our models are competitive on UDC benchmarks while using x4 less operations than others. To the best of our knowledge, we are the first work to approach and analyze this real-world single image restoration problem from the efficiency and production point of view.Comment: ECCV 2022 AIM Worksho

High dynamic range images: processing, display and perceptual quality assessment

2007/2008The intensity of natural light can span over 10 orders of magnitude from starlight to direct sunlight. Even in a single scene, the luminance of the bright areas can be thousands or millions of times greater than the luminance in the dark areas; the ratio between the maximum and the minimum luminance values is commonly known as dynamic range or contrast. The human visual system is able to operate in an extremely wide range of luminance conditions without saturation and at the same time it can perceive fine details which involve small luminance differences. Our eyes achieve this ability by modulating their response as a function of the local mean luminance with a process known as local adaptation. In particular, the visual sensation is not linked to the absolute luminance, but rather to its spatial and temporal variation. One consequence of the local adaptation capability of the eye is that the objects in a scene maintain their appearance even if the light source illuminating the scene changes significantly. On the other hand, the technologies used for the acquisition and reproduction of digital images are able to handle correctly a significantly smaller luminance range of 2 to 3 orders of magnitude at most. Therefore, a high dynamic range (HDR) image poses several challenges and requires the use of appropriate techniques. These elementary observations define the context in which the entire research work described in this Thesis has been performed. As indicated below, different fields have been considered; they range from the acquisition of HDR images to their display, from visual quality evaluation to medical applications, and include some developments on a recently proposed class of display equipment. An HDR image can be captured by taking multiple photographs with different exposure times or by using high dynamic range sensors; moreover, synthetic HDR images can be generated with computer graphics by means of physically-based algorithms which often involve advanced lighting simulations. An HDR image, although acquired correctly, can not be displayed on a conventional monitor. The white level of most devices is limited to a few hundred cd/m² by technological constraints, primarily linked to the power consumption and heat dissipation; the black level also has a non negligible luminance, in particular for devices based on the liquid crystal technology. However, thanks to the aforementioned properties of the human visual system, an exact reproduction of the luminance in the original scene is not strictly necessary in order to produce a similar sensation in the observer. For this purpose, dynamic range reduction algorithms have been developed which attenuate the large luminance variations in an image while preserving as far as possible the fine details. The most simple dynamic range reduction algorithms map each pixel individually with the same nonlinear function commonly known as tone mapping curve. One operator we propose, based on a modified logarithmic function, has a low computational cost and contains one single user-adjustable parameter. However, the methods belonging to this category can reduce the visibility of the details in some portions of the image. More advanced methods also take into account the pixel neighborhood. This approach can achieve a better preservation of the details, but the loss of one-to-one mapping from input luminances to display values can lead to the formation of gradient reversal effects, which typically appear as halos around the object boundaries. Different solutions to this problem have been attempted. One method we introduce is able to avoid the formation of halos and intrinsically prevents any clipping of the output display values. The method is formulated as a constrained optimization problem, which is solved efficiently by means of appropriate numerical methods. In specific applications, such as the medical one, the use of dynamic range reduction algorithms is discouraged because any artifacts introduced by the processing can lead to an incorrect diagnosis. In particular, a one-to-one mapping from the physical data (for instance, a tissue density in radiographic techniques) to the display value is often an essential requirement. For this purpose, high dynamic range displays, capable of reproducing images with a wide luminance range and possibly a higher bit depth, are under active development. Dual layer LCD displays, for instance, use two liquid crystal panels stacked one on top of the other over an enhanced backlight unit in order to achieve a dynamic range of 4 ÷ 5 orders of magnitude. The grayscale reproduction accuracy is also increased, although a “bit depth” can not be defined unambiguously because the luminance levels obtained by the combination of the two panels are partially overlapped and unevenly spaced. A dual layer LCD display, however, requires the use of complex splitting algorithms in order to generate the two images which drive the two liquid crystal panels. A splitting algorithm should compensate multiple sources of error, including the parallax introduced by the viewing angle, the gray-level clipping introduced by the limited dynamic range of the panels, the visibility of the reconstruction error, and glare effects introduced by an unwanted light scattering between the two panels. For these reasons, complex constrained optimization techniques are necessary. We propose an objective function which incorporates all the desired constraints and requirements and can be minimized efficiently by means of appropriate techniques based on multigrid methods. The quality assessment of high dynamic range images requires the development of appropriate techniques. By their own nature, dynamic range reduction algorithms change the luminance values of an image significantly and make most image fidelity metrics inapplicable. Some particular aspects of the methods can be quantified by means of appropriate operators; for instance, we introduce an expression which describes the detail attenuation introduced by a tone mapping curve. In general, a subjective quality assessment is preferably performed by means of appropriate psychophysical experiments. We conducted a set of experiments, targeted specifically at measuring the level of agreement between different users when adjusting the parameter of the modified logarithmic mapping method we propose. The experimental results show a strong correlation between the user-adjusted parameter and the image statistics, and suggest a simple technique for the automatic adjustment of this parameter. On the other hand, the quality assessment in the medical field is preferably performed by means of objective methods. In particular, task-based quality measures evaluate by means of appropriate observer studies the clinical validity of the image used to perform a specific diagnostic task. We conducted a set of observer studies following this approach, targeted specifically at measuring the clinical benefit introduced by a high dynamic range display based on the dual layer LCD technology over a conventional display with a low dynamic range and 8-bit quantization. Observer studies are often time consuming and difficult to organize; in order to increase the number of tests, the human observers can be partially replaced by appropriate software applications, known as model observers or computational observers, which simulate the diagnostic task by means of statistical classification techniques. This thesis is structured as follows. Chapter 1 contains a brief background of concepts related to the physiology of human vision and to the electronic reproduction of images. The description we make is by no means complete and is only intended to introduce some concepts which will be extensively used in the following. Chapter 2 describes the technique of high dynamic range image acquisition by means of multiple exposures. In Chapter 3 we introduce the dynamic range reduction algorithms, providing an overview of the state of the art and proposing some improvements and novel techniques. In Chapter 4 we address the topic of quality assessment in dynamic range reduction algorithms; in particular, we introduce an operator which describes the detail attenuation introduced by tone mapping curves and describe a set of psychophysical experiments we conducted for the adjustment of the parameter in the modified logarithmic mapping method we propose. In Chapter 5 we move to the topic of medical images and describe the techniques used to map the density data of radiographic images to display luminances. We point out some limitations of the current technical recommendation and propose an improvement. In Chapter 6 we describe in detail the dual layer LCD prototype and propose different splitting algorithms for the generation of the two images which drive the two liquid crystal panels. In Chapter 7 we propose one possible technique for the estimation of the equivalent bit depth of a dual layer LCD display, based on a statistical analysis of the quantization noise. Finally, in Chapter 8 we address the topic of objective quality assessment in medical images and describe a set of observer studies we conducted in order to quantify the clinical benefit introduced by a high dynamic range display. No general conclusions are offered; the breadth of the subjects has suggested to draw more focused comments at the end of the individual chapters.XXI Ciclo198

COMPRESSIVE IMAGING AND DUAL MOIRE´ LASER INTERFEROMETER AS METROLOGY TOOLS

Metrology is the science of measurement and deals with measuring different physical aspects of objects. In this research the focus has been on two basic problems that metrologists encounter. The first problem is the trade-off between the range of measurement and the corresponding resolution; measurement of physical parameters of a large object or scene accompanies by losing detailed information about small regions of the object. Indeed, instruments and techniques that perform coarse measurements are different from those that make fine measurements. This problem persists in the field of surface metrology, which deals with accurate measurement and detailed analysis of surfaces. For example, laser interferometry is used for fine measurement (in nanometer scale) while to measure the form of in object, which lies in the field of coarse measurement, a different technique like moire technique is used. We introduced a new technique to combine measurement from instruments with better resolution and smaller measurement range with those with coarser resolution and larger measurement range. We first measure the form of the object with coarse measurement techniques and then make some fine measurement for features in regions of interest. The second problem is the measurement conditions that lead to difficulties in measurement. These conditions include low light condition, large range of intensity variation, hyperspectral measurement, etc. Under low light condition there is not enough light for detector to detect light from object, which results in poor measurements. Large range of intensity variation results in a measurement with some saturated regions on the camera as well as some dark regions. We use compressive sampling based imaging systems to address these problems. Single pixel compressive imaging uses a single detector instead of array of detectors and reconstructs a complete image after several measurements. In this research we examined compressive imaging for different applications including low light imaging, high dynamic range imaging and hyperspectral imaging

Rendering non-pictorial (Scientific) high dynamic range images

In recent years, the graphics community is seeing an increasing demand for the capture and usage of high-dynamic-range (HDR) images. Since the production of HDR imagery is not solely the domain of the visualization of real life or computer generated scenes, novel techniques are also required for imagery captured from non-visual sources such as remote sensing, medical imaging, astronomical imaging, etc. This research proposes to integrate the techniques used for the display of high-dynamic-range pictorial imagery for the practical visualization of non-pictorial (scientific) imagery for data mining and interpretation. Nine algorithms were utilized to overcome the problem associated with rendering the high-dynamic-range image data to low-dynamic-range display devices, and the results were evaluated using a psychophysical experiment. Two paired-comparison experiments and a target detection experiment were performed. Paired-comparison results indicate that the Zone System performs the best on average and the Local Color Correction method performs the worst. The results show that the performance of different encoding schemes depend on the type of data being visualized. The correlation between the preference and scientific usefulness judgments (R2 = 0.31) demonstrates that observers tend to use different criteria when judging the scientific usefulness versus image preference. The experiment was conducted using observers with expertise (Radiologists) for the Medical image to further elucidate the success of HDR rendering on these data. The results indicated that both Radiologists and Non-radiologists tend to use similar criteria regardless of their experience and expertise when judging the usefulness of rendered images. A target detection experiment was conducted to measure the detectability of an embedded noise target in the Medical image to demonstrate the effect of the tone mapping operators on target detection. The result of the target detection experiment illustrated that the detectability of targets the image is greatly influenced by the rendering algorithm due to the inherent differences in tone mapping among the algorithms