Alessandro Artusi

Technical Section: Evaluation of HDR tone mapping methods using essential perceptual attributes

The problem of reproducing high dynamic range images on media with restricted dynamic range has gained a lot of interest in the computer graphics community. There exist various approaches to this issue, which span several research areas including computer graphics, image processing, color vision, physiological aspects, etc. These approaches assume a thorough knowledge of both the objective and subjective attributes of an image. However, no comprehensive overview and analysis of such attributes has been published so far. In this contribution, we present an overview about the effects of basic image attributes in high dynamic range tone mapping. Furthermore, we propose a scheme of relationships between these attributes, leading to the definition of an overall image quality measure. We present results of subjective psychophysical experiments that we have performed to prove the proposed relationship scheme. Moreover, we also present an evaluation of existing tone mapping methods (operators) with regard to these attributes. Finally, the execution of with reference and without a real reference perceptual experiments gave us the opportunity to relate the obtained subjective results. Our effort is not just useful to get into the tone mapping field or when implementing a tone mapping method, but it also sets the stage for well-founded quality comparisons between tone mapping methods. By providing good definitions of the different attributes, user-driven or fully automatic comparisons are made possible.

A Survey of Specularity Removal Methods

The separation of reflection components is an important issue in computer graphics, computer vision and image processing. It provides useful information for the applications that need consistent object surface appearance, such as stereo reconstruction, visual recognition, tracking, objects re‐illumination and dichromatic editing. In this paper we will present a brief survey of recent advances in separation of reflection components, also known as specularity (highlights) removal. Several techniques that try to tackle the problem from different points of view have been proposed so far. In this survey, we will overview these methods and we will present a critical analysis of their benefits and drawbacks.

High Dynamic Range Imaging and Low Dynamic Range Expansion for Generating HDR Content

In the last few years, researchers in the field of High Dynamic Range (HDR) Imaging have focused on providing tools for expanding Low Dynamic Range (LDR) content for the generation of HDR images due to the growing popularity of HDR in applications, such as photography and rendering via Image‐Based Lighting, and the imminent arrival of HDR displays to the consumer market. LDR content expansion is required due to the lack of fast and reliable consumer level HDR capture for still images and videos. Furthermore, LDR content expansion, will allow the re‐use of legacy LDR stills, videos and LDR applications created, over the last century and more, to be widely available. The use of certain LDR expansion methods, those that are based on the inversion of Tone Mapping Operators (TMOs), has made it possible to create novel compression algorithms that tackle the problem of the size of HDR content storage, which remains one of the major obstacles to be overcome for the adoption of HDR. These methods are used in conjunction with traditional LDR compression methods and can evolve accordingly. The goal of this report is to provide a comprehensive overview on HDR Imaging, and an in depth review on these emerging topics.

Illuminating the past: state of the art

Virtual reconstruction and representation of historical environments and objects have been of research interest for nearly two decades. Physically based and historically accurate illumination allows archaeologists and historians to authentically visualise a past environment to deduce new knowledge. This report reviews the current state of illuminating cultural heritage sites and objects using computer graphics for scientific, preservation and research purposes. We present the most noteworthy and up-to-date examples of reconstructions employing appropriate illumination models in object and image space, and in the visual perception domain. Finally, we also discuss the difficulties in rendering, documentation, validation and identify probable research challenges for the future. The report is aimed for researchers new to cultural heritage reconstruction who wish to learn about methods to illuminate the past.

Multidimensional image retargeting

Retargeting refers to the process by which an image or video is adapted from the display device for which it was meant (target display) to another one (retarget display). The retarget display has different features from the target one such as dynamic range, discretization levels, color gamut, multi-view, and refresh rate spatial resolution. This is a very relevant topic in graphics, given the increasing number of display devices from large, high-contrast screens to small cell phones with limited dynamic range; a lot of techniques are being published in different venues, and its hard to keep up. For most cases retargeting can be an ill-posed problem, for example in the process of displaying Low Dynamic Range (LDR) or 8-bit content on High Dynamic Range (HDR) displays. Such a problem requires the retargeting algorithm to generate new content which is missing in the input image/frame. In this course, we will present the latest solutions and techniques for retargeting images along various dimensions such as dynamic range, colors, temporal and spatial resolutions, and for the first time offer a much-needed holistic view of the field. Moreover, we are going to show how to measure and analyze the changes applied to an image or video in terms of quality using both psychophysical experiments (subjective) and computational metrics (objective). The course should be of interest to anyone involved in graphics in a broader sense, given the almost unavoidable need to retarget results to different devices -from developers interested in implementing retargeting techniques, to users that just need an overall perspective. For researchers fully engaged in developing multi-dimensional retargeting techniques, this course will serve as a solid background for future algorithms.

A psychophysical evaluation of inverse tone mapping techniques

In recent years inverse tone mapping techniques have been proposed for enhancing low‐dynamic range (LDR) content for a high‐dynamic range (HDR) experience on HDR displays, and for image based lighting. In this paper, we present a psychophysical study to evaluate the performance of inverse (reverse) tone mapping algorithms. Some of these techniques are computationally expensive because they need to resolve quantization problems that can occur when expanding an LDR image. Even if they can be implemented efficiently on hardware, the computational cost can still be high. An alternative is to utilize less complex operators; although these may suffer in terms of accuracy. Our study investigates, firstly, if a high level of complexity is needed for inverse tone mapping and, secondly, if a correlation exists between image content and quality. Two main applications have been considered: visualization on an HDR monitor and image‐based lighting.

High-dynamic-range video solution

The natural world presents our visual system with a wide, ever-changing range of colors and intensities. Existing video cameras are only capable of capturing a limited part of this wide range with sufficient resolution. High-dynamic-range (HDR) images can represent most of the real worlds luminances, but until now capturing HDR images with a linear-response function has been limited to static scenes. This demonstration showcases a novel complete HDR video solution. The system includes a unique HDR video camera capable of capturing a full HDTV video stream consisting of 20 f-stops dynamic range at a resolution of 1920 x 1080 pixels at 30 frames per second; an encoding method for coping with the huge amount of data generated by the camera (achieving a compression ratio of up to 100:1 and real-time decompression); and a new 22-inch desktop HDR display for directly visualizing the dynamic HDR content.

Delivering interactivity to complex tone mapping operators

The accurate display of high dynamic range images requires the application of complex tone mapping operators. These operators are computationally costly, which prevents their usage in interactive applications. We propose a general framework that delivers interactive performance to an important subclass of tone mapping operators, namely global tone mapping operators. The proposed framework consists of four steps: sampling the input image, applying the tone mapping operator, fitting the point-sampled tone mapping curve, and reconstructing the tone mapping curve for all pixels of the input image. We show how to make use of recent graphics hardware while keeping the advantage of generality by performing tone mapping in software. We demonstrate the capabilities of our method by accelerating several common global tone mapping operators and integrating the operators in a real-time rendering application.

Dynamic range compression by differential zone mapping based on psychophysical experiments

In this paper we present a new technique for the display of High Dynamic Range (HDR) images on Low Dynamic Range (LDR) displays. The described process has three stages. First, the input image is segmented into luminance zones. Second, the tone mapping operator (TMO) that performs better in each zone is automatically selected. Finally, the resulting tone mapping (TM) outputs for each zone are merged, generating the final LDR output image. To establish the TMO that performs better in each luminance zone we conducted a preliminary psychophysical experiment using a set of HDR images and six different TMOs. We validated our composite technique on several (new) HDR images and conducted a further psychophysical experiment, using an HDR display as reference, that establishes the advantages of our hybrid three-stage approach over a traditional individual TMO.

A novel image decomposition approach and its applications

The current state-of-the-art edge-preserving decomposition techniques may not be able to fully separate textures while preserving edges. This may generate artifacts in some applications, e.g., edge detection, texture transfer, etc. To solve this problem, a novel image decomposition approach based on explicit texture separation from large scale components of an image is presented. We first apply a Gaussian structure-texture decomposition, to separate the majority of textures out of the input image. However, residual textures are still visible around the strong edges. To remove these residuals, an asymmetric sampling operator is proposed and followed by a joint bilateral correction to remove an excessive blur effect. We demonstrate that our approach is well suited for the tasks such as texture transfer, edge detection, non-photorealistic rendering, and tone mapping. The results show our approach outperforms existing state-of-the-art image decomposition approaches.