Editable View Optimized Tone Mapping For Viewing High Dynamic Range Panoramas On Head Mounted Display

Head mounted displays are characterized by relatively low resolution and low dynamic range. These limitations significantly reduce the visual quality of photo-realistic captures on such displays. This thesis presents an interactive view optimized tone mapping technique for viewing large sized high dynamic range panoramas up to 16384 by 8192 on head mounted displays. This technique generates a separate file storing pre-computed view-adjusted mapping function parameters. We define this technique as ToneTexture. The use of a view adjusted tone mapping allows for expansion of the perceived color space available to the end user. This yields an improved visual appearance of both high dynamic range panoramas and low dynamic range panoramas on such displays. Moreover, by providing proper interface to manipulate on ToneTexture, users are allowed to adjust the mapping function as to changing color emphasis. The authors present comparisons of the results produced by ToneTexture technique against widely-used Reinhard tone mapping operator and Filmic tone mapping operator both objectively via a mathematical quality assessment metrics and subjectively through user study. Demonstration systems are available for desktop and head mounted displays such as Oculus Rift and GearVR

Benchmarking of objective quality metrics for HDR image quality assessment

Recent advances in high dynamic range (HDR) capture and display technologies have attracted a lot of interest from scientific, professional, and artistic communities. As any technology, the evaluation of HDR systems in terms of quality of experience is essential. Subjective evaluations are time consuming and expensive, and thus objective quality assessment tools are needed as well. In this paper, we report and analyze the results of an extensive benchmarking of objective quality metrics for HDR image quality assessment. In total, 35 objective metrics were benchmarked on a database of 20 HDR contents encoded with 3 compression algorithms at 4 bit rates, leading to a total of 240 compressed HDR images, using subjective quality scores as ground truth. Performance indexes were computed to assess the accuracy, monotonicity, and consistency of the metrics estimation of subjective scores. Statistical analysis was performed on the performance indexes to discriminate small differences between two metrics. Results demonstrated that HDR-VDP-2 is the most reliable predictor of perceived quality. Finally, our findings suggested that the performance of most full-reference metrics can be improved by considering non-linearities of the human visual system, while further efforts are necessary to improve performance of no-reference quality metrics for HDR content

High-fidelity colour reproduction for high-dynamic-range imaging

The aim of this thesis is to develop a colour reproduction system for high-dynamic-range (HDR) imaging. Classical colour reproduction systems fail to reproduce HDR images because current characterisation methods and colour appearance models fail to cover the dynamic range of luminance present in HDR images. HDR tone-mapping algorithms have been developed to reproduce HDR images on low-dynamic-range media such as LCD displays. However, most of these models have only considered luminance compression from a photographic point of view and have not explicitly taken into account colour appearance. Motivated by the idea to bridge the gap between crossmedia colour reproduction and HDR imaging, this thesis investigates the fundamentals and the infrastructure of cross-media colour reproduction. It restructures cross-media colour reproduction with respect to HDR imaging, and develops a novel cross-media colour reproduction system for HDR imaging. First, our HDR characterisation method enables us to measure HDR radiance values to a high accuracy that rivals spectroradiometers. Second, our colour appearance model enables us to predict human colour perception under high luminance levels. We first built a high-luminance display in order to establish a controllable high-luminance viewing environment. We conducted a psychophysical experiment on this display device to measure perceptual colour attributes. A novel numerical model for colour appearance was derived from our experimental data, which covers the full working range of the human visual system. Our appearance model predicts colour and luminance attributes under high luminance levels. In particular, our model predicts perceived lightness and colourfulness to a significantly higher accuracy than other appearance models. Finally, a complete colour reproduction pipeline is proposed using our novel HDR characterisation and colour appearance models. Results indicate that our reproduction system outperforms other reproduction methods with statistical significance. Our colour reproduction system provides high-fidelity colour reproduction for HDR imaging, and successfully bridges the gap between cross-media colour reproduction and HDR imaging

Quality of Experience in Immersive Video Technologies

Over the last decades, several technological revolutions have impacted the television industry, such as the shifts from black & white to color and from standard to high-definition. Nevertheless, further considerable improvements can still be achieved to provide a better multimedia experience, for example with ultra-high-definition, high dynamic range & wide color gamut, or 3D. These so-called immersive technologies aim at providing better, more realistic, and emotionally stronger experiences. To measure quality of experience (QoE), subjective evaluation is the ultimate means since it relies on a pool of human subjects. However, reliable and meaningful results can only be obtained if experiments are properly designed and conducted following a strict methodology. In this thesis, we build a rigorous framework for subjective evaluation of new types of image and video content. We propose different procedures and analysis tools for measuring QoE in immersive technologies. As immersive technologies capture more information than conventional technologies, they have the ability to provide more details, enhanced depth perception, as well as better color, contrast, and brightness. To measure the impact of immersive technologies on the viewersâ QoE, we apply the proposed framework for designing experiments and analyzing collected subjectsâ ratings. We also analyze eye movements to study human visual attention during immersive content playback. Since immersive content carries more information than conventional content, efficient compression algorithms are needed for storage and transmission using existing infrastructures. To determine the required bandwidth for high-quality transmission of immersive content, we use the proposed framework to conduct meticulous evaluations of recent image and video codecs in the context of immersive technologies. Subjective evaluation is time consuming, expensive, and is not always feasible. Consequently, researchers have developed objective metrics to automatically predict quality. To measure the performance of objective metrics in assessing immersive content quality, we perform several in-depth benchmarks of state-of-the-art and commonly used objective metrics. For this aim, we use ground truth quality scores, which are collected under our subjective evaluation framework. To improve QoE, we propose different systems for stereoscopic and autostereoscopic 3D displays in particular. The proposed systems can help reducing the artifacts generated at the visualization stage, which impact picture quality, depth quality, and visual comfort. To demonstrate the effectiveness of these systems, we use the proposed framework to measure viewersâ preference between these systems and standard 2D & 3D modes. In summary, this thesis tackles the problems of measuring, predicting, and improving QoE in immersive technologies. To address these problems, we build a rigorous framework and we apply it through several in-depth investigations. We put essential concepts of multimedia QoE under this framework. These concepts not only are of fundamental nature, but also have shown their impact in very practical applications. In particular, the JPEG, MPEG, and VCEG standardization bodies have adopted these concepts to select technologies that were proposed for standardization and to validate the resulting standards in terms of compression efficiency

Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark

We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark. Recent advances in inverse rendering have enabled a wide range of real-world applications in 3D content generation, moving rapidly from research and commercial use cases to consumer devices. While the results continue to improve, there is no real-world benchmark that can quantitatively assess and compare the performance of various inverse rendering methods. Existing real-world datasets typically only consist of the shape and multi-view images of objects, which are not sufficient for evaluating the quality of material recovery and object relighting. Methods capable of recovering material and lighting often resort to synthetic data for quantitative evaluation, which on the other hand does not guarantee generalization to complex real-world environments. We introduce a new dataset of real-world objects captured under a variety of natural scenes with ground-truth 3D scans, multi-view images, and environment lighting. Using this dataset, we establish the first comprehensive real-world evaluation benchmark for object inverse rendering tasks from in-the-wild scenes, and compare the performance of various existing methods.Comment: NeurIPS 2023 Datasets and Benchmarks Track. The first two authors contributed equally to this work. Project page: https://stanfordorb.github.io

Contrast limited histogram equalisation revisited

Histogram based tone adjustment algorithms have been used in a number of different computer vision applications in the recent years. One of the primary benefits of using the image histogram to derive the tone curve to enhance an image, is that it ensures the scene contents drives the enhancement i.e., each image has a unique tone curve. Perhaps the most well known image enhancement algorithm, Histogram Equalisation (HE), is a contrast adjustment algorithm that uses the image histogram, directly, to define a tone curve that brings out image details. However, HE often makes tone curves with large slopes that generate unpleasing reproductions. Contrast Limited Histogram Equalisation (CLHE) builds naturally upon HE and constrains the slopes of the tone curve such that the reproductions look better. Indeed, in almost all cases CLHE is preferred to HE. In this thesis we explore the CLHE algorithm in detail and highlight the shortcomings of the algorithm. We explore and discuss several approaches aimed at overcoming the limitations of CLHE, while also considering modern histogram based tone adjustment algorithms. The work in this thesis is motivated by the fact that CLHE is very popular in the modern literature. CLHE also - due to it’s inclusion in the Apical Iridix tone mapper - ships in many thousands of cameras

Image-based Material Editing

Photo editing software allows digital images to be blurred, warped or re-colored at the touch of a button. However, it is not currently possible to change the material appearance of an object except by painstakingly painting over the appropriate pixels. Here we present a set of methods for automatically replacing one material with another, completely different material, starting with only a single high dynamic range image, and an alpha matte specifying the object. Our approach exploits the fact that human vision is surprisingly tolerant of certain (sometimes enormous) physical inaccuracies. Thus, it may be possible to produce a visually compelling illusion of material transformations, without fully reconstructing the lighting or geometry. We employ a range of algorithms depending on the target material. First, an approximate depth map is derived from the image intensities using bilateral filters. The resulting surface normals are then used to map data onto the surface of the object to specify its material appearance. To create transparent or translucent materials, the mapped data are derived from the object\u27s background. To create textured materials, the mapped data are a texture map. The surface normals can also be used to apply arbitrary bidirectional reflectance distribution functions to the surface, allowing us to simulate a wide range of materials. To facilitate the process of material editing, we generate the HDR image with a novel algorithm, that is robust against noise in individual exposures. This ensures that any noise, which would possibly have affected the shape recovery of the objects adversely, will be removed. We also present an algorithm to automatically generate alpha mattes. This algorithm requires as input two images--one where the object is in focus, and one where the background is in focus--and then automatically produces an approximate matte, indicating which pixels belong to the object. The result is then improved by a second algorithm to generate an accurate alpha matte, which can be given as input to our material editing techniques

Perceptual video quality assessment: the journey continues!

Perceptual Video Quality Assessment (VQA) is one of the most fundamental and challenging problems in the field of Video Engineering. Along with video compression, it has become one of two dominant theoretical and algorithmic technologies in television streaming and social media. Over the last 2 decades, the volume of video traffic over the internet has grown exponentially, powered by rapid advancements in cloud services, faster video compression technologies, and increased access to high-speed, low-latency wireless internet connectivity. This has given rise to issues related to delivering extraordinary volumes of picture and video data to an increasingly sophisticated and demanding global audience. Consequently, developing algorithms to measure the quality of pictures and videos as perceived by humans has become increasingly critical since these algorithms can be used to perceptually optimize trade-offs between quality and bandwidth consumption. VQA models have evolved from algorithms developed for generic 2D videos to specialized algorithms explicitly designed for on-demand video streaming, user-generated content (UGC), virtual and augmented reality (VR and AR), cloud gaming, high dynamic range (HDR), and high frame rate (HFR) scenarios. Along the way, we also describe the advancement in algorithm design, beginning with traditional hand-crafted feature-based methods and finishing with current deep-learning models powering accurate VQA algorithms. We also discuss the evolution of Subjective Video Quality databases containing videos and human-annotated quality scores, which are the necessary tools to create, test, compare, and benchmark VQA algorithms. To finish, we discuss emerging trends in VQA algorithm design and general perspectives on the evolution of Video Quality Assessment in the foreseeable future