Enhancing Image Watermarking With Adaptive Embedding Parameter and PSNR Guarantee

Huang, Ying; Niu, Baoning; Guan, Haibing; Zhang, Shuwu

doi:10.1109/tmm.2019.2907475

Cited by 75 publications

(27 citation statements)

References 33 publications

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“…The comparison can be accomplished using mathematical equations to rank each methods performance at a given peak signal to noise ratio (PSNR) [21]. The PSNR has been computed using the formula (10).…”

Section: Resultsmentioning

confidence: 99%

Lifting dual tree complex wavelets transform

Naimi

Adamou-Mitiche

Mitiche

2021

IJECE

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We describe the lifting dual tree complex wavelet transform (LDTCWT), a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). We describe a way to estimate the accuracy of this approximation and style appropriate filters to attain this. These benefits are often exploited among applications like denoising, segmentation, image fusion and compression. The results of applications shrinkage denoising demonstrate objective and subjective enhancements over the dual tree complex wavelet transform (DTCWT). The results of the shrinkage denoising example application indicate empirical and subjective enhancements over the DTCWT. The new transform with the DTCWT provide a trade-off between denoising computational competence of performance, and memory necessities. We tend to use the PSNR (peak signal to noise ratio) alongside the structural similarity index measure (SSIM) and the SSIM map to estimate denoised image quality.

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Section: Resultsmentioning

confidence: 99%

Lifting dual tree complex wavelets transform

Naimi

Adamou-Mitiche

Mitiche

2021

IJECE

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“…Watermarking can be executed in a spatial domain [4,13,23,28,29] or a transform domain [3,[5][6][7][8][9][10][11][12]14,15,18,19,21,22,[24][25][26][27][30][31][32][33][34][35]. The spatial domain watermarking has high efficiency by modifying the pixels of the host images or frames directly to embed the watermark.…”

Section: Related Workmentioning

confidence: 99%

“…The spatial domain watermarking has high efficiency by modifying the pixels of the host images or frames directly to embed the watermark. In comparison, the transform domain watermarking modifies the coefficients obtained by transformation, such as the Discrete Cosine Transform (DCT) [8][9][10]18,19,21,22,24,26,30,31], Discrete Wavelet Transform (DWT) [7,11,12,14,[25][26][27]32,33], Singular Value Decomposition (SVD) [6,12,25,26], Discrete Multiwavelet Transformation (DMT) [3], Contourlet Transform [6], Dual-Tree Complex Wavelet Transform (DT CWT) [5,15,34], and so on. They possess better imperceptibility and robustness, but their computational complexity is higher than that of the spatial domain watermarking.…”

Section: Related Workmentioning

confidence: 99%

“…After determining the embedding domain, we should modify or analyze its coefficients to embed and extract the watermark. The quantization methods [7][8][9][10][16][17][18][19][20][21] are typical watermark embedding and extracting methods. It divides the reference axis into multiple index intervals, and each index interval corresponds to one component (0 or 1) of the watermark.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Video Watermarking against Scaling Attacks Based on Quantization Index Modulation

Huang

Guan

et al. 2021

Electronics

Self Cite

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Video watermarking plays a vital role in protecting the video copyright. The quantization-based methods are widely used in the existing watermarking algorithms, owing to their low computational complexity and completely blind extraction. However, most of them work poorly in resisting scaling attacks, by which the quantization value may fall outside the original quantization interval. For addressing this issue, an adaptive quantization index modulation method is proposed. The property that is associated with the ratio of the DC coefficient before and after scaling the video resolution motivates us to select the DC coefficient as the quantization value and set the size of the quantization interval by the video resolution to maintain the synchronization between them before and after scaling. Moreover, a strategy taking advantage of the high decoding reliability of the QRCode is proposed to terminate the extraction in advance, and both the embedding and the extracting process are performed in the spatial domain, which all contribute to further enhance the execution efficiency. The experimental results show that our algorithm outperforms the state-of-the-art method in terms of imperceptibility, robustness, and computational cost.

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“…There are a lot of image processing approaches to enhance quality of images and videos [32,[86][87][88][89][90] but at the first, its metric should be defined. Now, well-known metrics for assessing the quality of images and videos are VQM (Video quality measurement), PVQM (Perceptual video quality measure), MPQM (Moving picture quality metrics), SSIM (Structural Similarity Index), MSSIM (Mean SSIM), FSIM (feature-similarity), PSNR (Maximum signal to noise ratio), and HVS (human visual system) [85,[91][92][93]. Human Visual System (HVS) based techniques for IQ assessments (IQA) take benefit of some known characteristics of Human Visual System in order to incorporate perceptual image quality metrics (IQM).…”

Section: Performance Evaluation Metric For the Proposed Image Transmimentioning

confidence: 99%

Image Transmission over Cognitive Radio Networks for Smart Grid Applications

2019

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Today, Smart Grids (SGs), as the goal of the next-generation power grid system, span extremely wide aspects from power generation to end-user utilities. In smart grids, Energy and Information flows are mutually dependent and performance degradation of one side may have a high impact on the other side. In this work, we introduce our architecture for monitoring of Wind Turbine (WT) farms in smart grids. In our proposed system an industrial camera is embedded on a Wireless Cognitive Radio node for each WT to capture appropriate images and stream videos to the cognitive coordinator. Any packet loss in transmission between an embedded cognitive node and the coordinator can degrade peak signal-to-noise ratio (PSNR) of the received images. The image streaming is a delay sensitive transmission which should be done in harsh environments in SGs. To tackle these challenging issues, we introduce our efficient model, called JOPSS, for joint optimization of both packet size and Number of Spectrum Sensing Iterations (NSSI) during image transmission in time-restricted conditions. We define our proposed objective function as the quotient of the Overhead Time and the Effective Transmission Time (ETT). In addition, we introduce our methods based on the Minimum of Overhead Time Channel Selection (MOTS) for the efficient channel selection along with Dynamic Parameter Updating Procedure (DPUP) to benefit different strategies in Mandatory and Proactive Handoffs (MHO/PHO). The obtained results show that noticeable improvements in both PSNR and feature-similarity (FSIM) can be achieved on our models JOPSS and JOPSS-SAFE, respectively.

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Enhancing Image Watermarking With Adaptive Embedding Parameter and PSNR Guarantee

Cited by 75 publications

References 33 publications

Lifting dual tree complex wavelets transform

Lifting dual tree complex wavelets transform

Adaptive Video Watermarking against Scaling Attacks Based on Quantization Index Modulation

Image Transmission over Cognitive Radio Networks for Smart Grid Applications

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