High Dynamic Range Image Watermarking Robust Against Tone-Mapping Operators

High dynamic range (HDR) images represent the future format for digital images since they allow accurate rendering of a wider range of luminance values. However, today special types of preprocessing, collectively known as tone-mapping (TM) operators, are needed to adapt HDR images to currently existing displays. Tone-mapped images, although of reduced dynamic range, have nonetheless high quality and hence retain some commercial value. In this paper, we propose a solution to the problem of HDR image watermarking, e.g., for copyright embedding, that should survive TM. Therefore, the requirements imposed on the watermark encompass imperceptibility, a certain degree of security, and robustness to TM operators. The proposed watermarking system belongs to the blind, detectable category; it is based on the quantization index modulation (QIM) paradigm and employs higher order statistics as a feature. Experimental analysis shows positive results and demonstrates the system effectiveness with current state-of-art TM algorithms

06221 Abstracts Collection -- Computational Aestethics in Graphics, Visualization and Imaging

From 28.05.06 to 02.06.06, the Dagstuhl Seminar 06221 ``Computational Aesthetics in Graphics, Visualization and Imaging\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Learning a self-supervised tone mapping operator via feature contrast masking loss

High Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies. Nevertheless, the dynamic range of common display devices is still limited, therefore tone mapping (TM) remains a key challenge for image visualization. Recent work has demonstrated that neural networks can achieve remarkable performance in this task when compared to traditional methods, however, the quality of the results of these learning-based methods is limited by the training data. Most existing works use as training set a curated selection of best-performing results from existing traditional tone mapping operators (often guided by a quality metric), therefore, the quality of newly generated results is fundamentally limited by the performance of such operators. This quality might be even further limited by the pool of HDR content that is used for training. In this work we propose a learning-based self-supervised tone mapping operator that is trained at test time specifically for each HDR image and does not need any data labeling. The key novelty of our approach is a carefully designed loss function built upon fundamental knowledge on contrast perception that allows for directly comparing the content in the HDR and tone mapped images. We achieve this goal by reformulating classic VGG feature maps into feature contrast maps that normalize local feature differences by their average magnitude in a local neighborhood, allowing our loss to account for contrast masking effects. We perform extensive ablation studies and exploration of parameters and demonstrate that our solution outperforms existing approaches with a single set of fixed parameters, as confirmed by both objective and subjective metrics

Evaluation of the effectiveness of HDR tone-mapping operators for photogrammetric applications

[EN] The ability of High Dynamic Range (HDR) imaging to capture the full range of lighting in a scene has meant that it is being increasingly used for Cultural Heritage (CH) applications. Photogrammetric techniques allow the semi-automatic production of 3D models from a sequence of images. Current photogrammetric methods are not always effective in reconstructing images under harsh lighting conditions, as significant geometric details may not have been captured accurately within under- and over-exposed regions of the image. HDR imaging offers the possibility to overcome this limitation, however the HDR images need to be tone mapped before they can be used within existing photogrammetric algorithms. In this paper we evaluate four different HDR tone-mapping operators (TMOs) that have been used to convert raw HDR images into a format suitable for state-of-the-art algorithms, and in particular keypoint detection techniques. The evaluation criteria used are the number of keypoints, the number of valid matches achieved and the repeatability rate. The comparison considers two local and two global TMOs. HDR data from four CH sites were used: Kaisariani Monastery (Greece), Asinou Church (Cyprus), Château des Baux (France) and Buonconsiglio Castle (Italy).We would like to thank Kurt Debattista, Timothy Bradley, Ratnajit Mukherjee, Diego Bellido Castañeda and TomBashford Rogers for their suggestions, help and encouragement. We would like to thank the hosting institutions: 3D Optical Metrology Group, FBK (Trento, Italy) and UMR 3495 MAP CNRS/MCC (Marseille, France), for their support during the data acquisition campaigns. This project has received funding from the European Union’s 7 th Framework Programme for research, technological development and demonstration under grant agreement No. 608013, titled “ITN-DCH: Initial Training Network for Digital Cultural Heritage: Projecting our Past to the Future”.Suma, R.; Stavropoulou, G.; Stathopoulou, EK.; Van Gool, L.; Georgopoulos, A.; Chalmers, A. (2016). Evaluation of the effectiveness of HDR tone-mapping operators for photogrammetric applications. Virtual Archaeology Review. 7(15):54-66. https://doi.org/10.4995/var.2016.6319SWORD546671

Subjective and objective quality evaluation of synthetic and high dynamic range images

Recent years have seen a huge growth in the acquisition, transmission, and storage of videos. The visual data consists of both natural scenes as well as synthetic scenes, such as animated movies, cartoons and video games. In all these cases, the ultimate goal is to provide the viewers with a satisfactory quality-of-experience. In addition to the traditional 8-bit images, high dynamic range imaging is also becoming popular because of its ability to represent the real world luminances more realistically. Coming up with objective image quality assessment algorithms for these applications is an interesting research problem. In this work, I have developed a synthetic image quality database by introducing varying degrees of different types of distortions and conducted a subjective experiment in order to obtain the ground-truth data. I evaluated the performance of state-of-the-art image quality assessment algorithms (typically meant for natural images) on this database, especially no-reference algorithms that have not been applied to the domain of computer graphics images before. I identified the top-performing algorithms along with analyzing the types of distortions on which the present algorithms show a less impressive performance. For high dynamic range(HDR) images, I have designed two new full-reference image quality assessment algorithms to judge the quality of tonemapped HDR images using statistical features extracted from them. I have also conducted a massive online crowd-sourced subjective test for HDR image artifacts arising from tonemapping, multiple-exposure fusion and post processing. To the best of our knowledge, presently this is the largest HDR image database in the world involving the largest number of source images and most number of human evaluations. Based on the subjective evaluations obtained, I have also proposed machine learning based no-reference image quality assessment algorithms to predict the perceptual quality of HDR images.Electrical and Computer Engineerin