The illegal distribution of a digital movie is a common and significant threat to the film industry. With the advent of high-speed broadband Internet access, a pirated copy of a digital video can now be easily distributed to a global audience. A possible means of limiting this type of digital theft is digital video watermarking whereby additional information, called a watermark, is embedded in the host video. This watermark can be extracted at the decoder and used to determine whether the video content is watermarked. This paper presents a review of the digital video watermarking techniques in which their applications, challenges, and important properties are discussed, and categorizes them based on the domain in which they embed the watermark. It then provides an overview of a few emerging innovative solutions using watermarks. Protecting a 3D video by watermarking is an emerging area of research. The relevant 3D video watermarking techniques in the literature are classified based on the image-based representations of a 3D video in stereoscopic, depth-image-based rendering, and multi-view video watermarking. We discuss each technique, and then present a survey of the literature. Finally, we provide a summary of this paper and propose some future research directions.

An Overview of Digital Video Watermarking

In this article, we review the algorithms for rotation, scaling and translation (RST) invariant image watermarking. There are mainly two categories of RST invariant image watermarking algorithms. One is to rectify the RST transformed image before conducting watermark detection. Another is to embed and detect watermark in an RST invariant or semi-invariant domain. In order to help readers understand, we first introduce the fundamental theories and techniques used in the existing RST invariant image watermarking algorithms. Then, we discuss in detail the work principles, embedding process, and detection process of the typical RST invariant image watermarking algorithms. Finally, we analyze and evaluate these typical algorithms through implementation, and point out their advantages and disadvantages.

A survey of RST invariant image watermarking algorithms

This paper proposes a novel content-based image watermarking method based on invariant regions of an image. The invariant regions are self-adaptive image patches that deform with geometric transformations. Three different invariant-region detection methods based on the scale-space representation of an image were considered for watermarking. At each invariant region, the watermark is embedded after geometric normalization according to the shape of the region. By binding watermarking with invariant regions, resilience against geometric transformations can be readily obtained. Experimental results show that the proposed method is robust against various image processing steps, including geometric transformations, cropping, filtering, and JPEG compression.

/pdf/image-watermarking-based-on-invariant-regions-of-scale-space-43puz5kxr7.pdf

Image watermarking based on invariant regions of scale-space representation

In this paper presents a novel reversible watermarking scheme. The proposed scheme use an interpolation technique to generate residual values named as interpolation error. Additionally apply the additive expansion to these interpolation-errors, this project achieve a highly efficient reversible watermarking scheme which can guarantee high image quality without sacrificing embedding capacity. The experimental results show the proposed reversible scheme provides a higher capacity and achieves better image quality for watermarked images. The computational cost of the proposed scheme is small.

Reversible image watermarking using interpolation technique

http://www.scielo.org.mx/pdf/jart/v12n1/v12n1a12.pdf

Robust Image Watermarking Theories and Techniques: A Review

A robust digital image watermarking scheme that combines image feature extraction and image normalization is proposed. The goal is to resist both geometric distortion and signal processing attacks. We adopt a feature extraction method called Mexican hat wavelet scale interaction. The extracted feature points can survive a variety of attacks and be used as reference points for both watermark embedding and detection. The normalized image of an image (object) is nearly invariant with respect to rotations. As a result, the watermark detection task can be much simplified when it is applied to the normalized image. However, because image normalization is sensitive to image local variation, we apply image normalization to nonoverlapped image disks separately. The disks are centered at the extracted feature points. Several copies of a 16-bit watermark sequence are embedded in the original image to improve the robustness of watermarks. Simulation results show that our scheme can survive low-quality JPEG compression, color reduction, sharpening, Gaussian filtering, median filtering, row or column removal, shearing, rotation, local warping, cropping, and linear geometric transformations.

https://ir.nctu.edu.tw:443/bitstream/11536/27980/1/000181685800006.pdf

A feature-based robust digital image watermarking scheme

A robust digital image watermarking scheme that combines image feature extraction and image normalization is pro- posed. The goal is to resist both geometric distortion and signal pro- cessing attacks. We adopt a feature extraction method called Mex- ican Hat wavelet scale interaction. The extracted feature points can survive a variety of attacks and be used as reference points for both watermark embedding and detection. The normalized image of an image (object) is nearly invariant with respect to rotations. As a re- sult, the watermark detection task can be much simplified when it is applied to the normalized image. However, because image nor- malization is sensitive to image local variation, we apply image nor- malization to nonoverlapped image disks separately. The disks are centered at the extracted feature points. Several copies of a 16-bit watermark sequence are embedded in the original image to im- prove the robustness of watermarks. Simulation results show that our scheme can survive low-quality JPEG compression, color re- duction, sharpening, Gaussian filtering, median filtering, row or column removal, shearing, rotation, local warping, cropping, and linear geometric transformations.

/pdf/a-feature-based-robust-digital-image-2nb4m39jka.pdf

A Feature-Based Robust Digital Image

Ubiquitous blurry images degrade viewing experiences and performance of video analysis. To be applicable to consumer electronics, the large amount of parameters and high computational load of deblurring network have to be avoided. The existing SRN+ has a small number of parameters (3.9M) and comparable performance. To improve the quality of outputs of a deblurring network (e.g., SRN+) without changing its architecture, this paper proposes the pseudo label based order task for training the discriminator. The proposed funnel soft label further reduces the problem of vanishing gradient during training SRN+ (generator), and the adversarial loss combined with weighted scale-level losses improves quality of deblurring. For GoPro dataset, the proposed scheme outperforms the light version of the state-of-the-art MPRNet in PSNR (+1dB) and number of parameters (70%).

Efficient Scale-Recurrent Network Using Generative Adversarial Network for Image Deblurring

The state-of-the-art (SOTA) transformer tracker TransT achieves high tracking accuracy. Nevertheless, the time and space complexity of its attention operation is quadratic to the spatial dimension of feature vectors. Thus it is difficult to deploy TransT on resource constrained devices. This paper proposes Local Information Patch Attention Free Transformer (LIP-AFT) based Local Information Patch Self-Attention Free Transformer (LIPS-AFT) and Local Information Patch Cross-Attention Free Transformer (LIPC-AFT) for linear time and space complexity and high accuracy. LIP-AFT benefits from global connectivity between patches while it focuses on naïve strong local attention patterns. The proposed tracker outperforms both SOTA trackers and TransT with various SOTA attention algorithms on accuracy and complexity. Moreover, its inference phase runs at 41 fps on RTX 2070S GPUs.

Efficient Visual Tracking Using Local Information Patch Attention Free Transformer

Versatile Video Coding (VVC) enables high coding gain on 360-degree equiangular cubemap (EAC) projection format. Nevertheless, the R-$\boldsymbol{\lambda}$ model based rate control of VTM cannot accurately estimate $\boldsymbol{\lambda}$ of SKIP CTUs so that the bitrate error is increased. Thus this paper proposes a SKIP CTU predictor for VVC interframe rate control. Different from the existing SKIP block ratio based rate control scheme, the predictor runs before encoding the current CTU. It predicts whether the current CTU has high percentage of SKIP CUs or not instead of predicting whether the optimal mode of a CU is SKIP mode or not. The value of CTU-level $\boldsymbol{\lambda}$ of the predicted SKIP CTU is estimated using $\boldsymbol{\lambda}$ values in spatially adjacent CTUs to decrease the bitrate error. The lightweight neural network based predictor has only 199 parameters. Bit starvation in later coded frames is reduced by constraining frame-level quantization parameters in intra frames. Tests show that the proposed scheme outperforms state-of-the-art rate control methods on 360-degree 8K and 6K EAC videos on bitrate accuracy.

Chih-Wei Tang

Papers

A feature-based robust digital image watermarking scheme

A Feature-Based Robust Digital Image

Efficient Scale-Recurrent Network Using Generative Adversarial Network for Image Deblurring

Efficient Visual Tracking Using Local Information Patch Attention Free Transformer

VVC Based Rate Control Using SKIP CTU Predictor