Scene-based imperceptible-visible watermarking for HDR video content

This paper presents the High Dynamic Range - Imperceptible Visible Watermarking for HDR video content (HDR-IVW-V) based on scene detection for robust copyright protection of HDR videos using a visually imperceptible watermarking methodology. HDR-IVW-V employs scene detection to reduce both computational complexity and undesired visual attention to watermarked regions. Visual imperceptibility is achieved by finding the region of a frame with the highest hiding capacities on which the Human Visual System (HVS) cannot recognize the embedded watermark. The embedded watermark remains visually imperceptible as long as the normal color calibration parameters are held. HDR-IVW-V is evaluated on PQ-encoded HDR video content successfully attaining visual imperceptibility, robustness to tone mapping operations and image quality preservation

HDR Image Watermarking

In this Chapter we survey available solutions for HDR image watermarking. First, we briefly discuss watermarking in general terms, with particular emphasis on its requirements that primarily include security, robustness, imperceptibility, capacity and the availability of the original image during recovery. However, with respect to traditional image watermarking, HDR images possess a unique set of features such as an extended range of luminance values to work with and tone-mapping operators against whom it is essential to be robust. These clearly affect the HDR watermarking algorithms proposed in the literature, which we extensively review next, including a thorough analysis of the reported experimental results. As a working example, we also describe the HDR watermarking system that we recently proposed and that focuses on combining imperceptibility, security and robustness to TM operators at the expense of capacity. We conclude the chapter with a critical analysis of the current state and future directions of the watermarking applications in the HDR domain

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

Watermarking of HDR images in the spatial domain with HVS-imperceptibility

This paper presents a watermarking method in the spatial domain with HVS-imperceptibility for High Dynamic Range (HDR) images. The proposed method combines the content readability afforded by invisible watermarking with the visual ownership identification afforded by visible watermarking. The HVS-imperceptibility is guaranteed thanks to a Luma Variation Tolerance (LVT) curve, which is associated with the transfer function (TF) used for HDR encoding and provides the information needed to embed an imperceptible watermark in the spatial domain. The LVT curve is based on the inaccuracies between the non-linear digital representation of the linear luminance acquired by an HDR sensor and the brightness perceived by the Human Visual System (HVS) from the linear luminance displayed on an HDR screen. The embedded watermarks remain imperceptible to the HVS as long as the TF is not altered or the normal calibration and colorimetry conditions of the HDR screen remain unchanged. Extensive qualitative and quantitative evaluations on several HDR images encoded by two widely-used TFs confirm the strong HVSimperceptibility capabilities of the method, as well as the robustness of the embedded watermarks to tone mapping, lossy compression, and common signal processing operations

High-capacity watermarking of high dynamic range images

High dynamic range (HDR) imaging techniques address the need to capture the full range of color and light that the human eyes can perceive in the real world. HDR technology is becoming more and more pervasive. In fact, most of the cameras and smartphones available on the market are capable of capturing HDR images. Among the challenges posed by the spread of this new technology there is the increasing need to design proper techniques to protect the intellectual property of HDR digital media. In this paper, we speculate about the use of watermarking techniques to cope with the peculiarities of HDR media to prevent the misappropriation of HDR images

Dataset and metrics for predicting local visible differences

A large number of imaging and computer graphics applications require localized information on the visibility of image distortions. Existing image quality metrics are not suitable for this task as they provide a single quality value per image. Existing visibility metrics produce visual difference maps, and are specifically designed for detecting just noticeable distortions but their predictions are often inaccurate. In this work, we argue that the key reason for this problem is the lack of large image collections with a good coverage of possible distortions that occur in different applications. To address the problem, we collect an extensive dataset of reference and distorted image pairs together with user markings indicating whether distortions are visible or not. We propose a statistical model that is designed for the meaningful interpretation of such data, which is affected by visual search and imprecision of manual marking. We use our dataset for training existing metrics and we demonstrate that their performance significantly improves. We show that our dataset with the proposed statistical model can be used to train a new CNN-based metric, which outperforms the existing solutions. We demonstrate the utility of such a metric in visually lossless JPEG compression, super-resolution and watermarking.</jats:p