“…Such methods have only been proposed for SDR images. For example, [17] and [18] propose to exploit the cover media's color histogram to embed the watermark in the spatial domain with HVSimperceptibility. The method in [19], on the other hand, uses a JND criterion for embedding in the spatial domain, the DCT to share extraction parameters, and a binarization function for extraction.…”
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. INDEX TERMS HDR, invisible watermarking, visible watermarking, LVT curve, HVS-imperceptibility.
“…Such methods have only been proposed for SDR images. For example, [17] and [18] propose to exploit the cover media's color histogram to embed the watermark in the spatial domain with HVSimperceptibility. The method in [19], on the other hand, uses a JND criterion for embedding in the spatial domain, the DCT to share extraction parameters, and a binarization function for extraction.…”
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. INDEX TERMS HDR, invisible watermarking, visible watermarking, LVT curve, HVS-imperceptibility.
“…Human visual system in watermarking technique helps to control the opacity of watermark depending on sensitivity of the watermarking positions. Lin et al [13] proposed the imperceptible visible watermarking (IVW) mechanism. Variance of the images was used to define suitable watermarking positions.…”
Section: Related Workmentioning
confidence: 99%
“…In all these visible watermarking techniques (except [13,15]), watermark is embedded at predefined portions in images/video-frames. Many times, visible watermark occludes the significant portions in images/video-frames (for example, television broadcasting).…”
Visible watermarking is the process of embedding data (watermark) into a multimedia object (video/image) such that the embedded watermark is perceptible to a human observer. Many times, visible watermarks occlude important portion of multimedia objects. This paper introduces a visible watermarking algorithm to embed a binary logo watermark at N non-overlapping positions in an image such that important portions of the image are not occluded. The important portions are found through visual saliency computation or available human eye fixation density maps. In the proposed visible watermarking, just noticeable distortion is used to adaptively filter the watermark embedding energy based on the image content. A mathematical model in terms of information-content-weightedstructural-similarity-index and visual importance is proposed to find optimal watermark embedding strength. We tested the algorithm on several color images of different sizes and on several binary watermarks of different sizes and found the results to be very promising as per the requirements in visible watermarking. When compared to the state-of-the-art, we also found that the proposed technique does better in not hiding the details of any test image.
“…Transform domain methods are used to improve either the robustness or the imperceptibility by transmuting the raw pixel value into a certain coefficient, inserting the watermark, and then performing inverse transformation to get the embedded pixel value. They are combined with several spatial methods, such as the Chinese remainder theorem [8,9], histogram [10,11], and Singular Value Decomposition [12,13] to get good robustness or imperceptibility at the expense of computational time. Among them, Singular Value Decomposition (SVD) is one of the most popular methods due to its robustness against different types of attacks.…”
One of the most used watermarking algorithms is Singular Value Decomposition (SVD), which has a balanced level of imperceptibility and robustness. However, SVD uses a singular matrix for embedding and two orthogonal matrices for reconstruction, which is inefficient. In this paper, a Hadamard matrix is used to get a singular matrix for the reconstruction process. Moreover, SVD works with a floating-point value, which takes long processing time, while the Hadamard matrix works with an integer range, which is more efficient. Visual measurement showed that SVD and the new method had average NC values of 0.8321 and 0.8293, whereas the average SSIM values resulted in the same value (0.9925). In terms of processing time, the proposed method ran faster than SVD with an embedding and extraction time of 0.6308 and 0.2163 seconds against 0.8419 and 0.2935 seconds. The proposed method successfully reduced the running time while maintaining imperceptibility and robustness.
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