A new video watermarking algorithm based on 1D DFT and Radon transform

In this paper, we propose a new video watermarking algorithm based on the 1D DFT (one-dimensional discrete Fourier transform) and Radon transform. The 1D DFT for a video sequence generates an ideal domain, in which the spatial information is still kept and the temporal information is obtained. With detailed analysis and calculation, we choose the frames with highest temporal frequencies to embed the fence-shaped watermark pattern in the Radon transform domain of the selected frames. The adaptive embedding strength for different locations keeps the fidelity of the watermarked video. The performance of the proposed algorithm is evaluated by video compression standard H.264 with three different bit rates; geometric attacks such as rotation, translation, and aspect-ratio changes; and other attacks like frame drop, frame swap, spatial filtering, noise addition, lighting change, and histogram equalization. The main contributions of this paper are the introduction of the 1D DFT along temporal direction for watermarking that enables the robustness against video compression, and the Radon transform-based watermark embedding and extraction that produces the robustness against geometric transformations. One of the most important advantages of this video watermarking algorithm is its simplicity and practicality.     

HEVC压缩域的视频摘要关键帧提取方法

为了提高关键帧提取的准确率，改善视频摘要的质量，提出了一种HEVC压缩域的视频摘要关键帧提取方法。首先，对视频序列进行编解码，在解码中统计HEVC帧内编码PU块的亮度预测模式数目。然后，特征提取是利用统计得到的模式数目构建成模式特征向量，并将其作为视频帧的纹理特征用于关键帧的提取。最后，利用融合迭代自组织数据分析算法(ISODATA）的自适应聚类算法对模式特征向量进行聚类，在聚类结果中选取每个类内中间向量对应的帧作为候选关键帧，并通过相似度对候选关键帧进行再次筛选，剔除冗余帧，得到最终的关键帧。实验结果表明，在Open Video Project数据集上进行的大量实验验证，该方法提取关键帧的精度为79.9%、召回率达到93.6%、F-score为86.2%，有效地改善了视频摘要的质量。      

基于关键帧和小波变换的盲视频水印算法

针对现有视频水印算法在抗几何攻击能力方面的一些不足,提出一种基于关键帧和小波变换的盲视频水印算法.依据视频帧间差欧氏距离提取视频关键帧并分块,结合人类视觉系统特性,从关键帧不同的块中选择亮度敏感性和纹理敏感性最高的块构成三维体块,进行三维小波变换,将置乱的水印图像以不同的嵌入强度嵌入到小波系数的低频区域,利用阈值门限实现水印的盲提取.结果表明,该算法提高了水印嵌入容量,对于针对视频水印的攻击具有较好的不可见性和稳健性.     

Video key frame extraction through dynamic Delaunay clustering with a structural constraint

Key frame based video summarization has emerged as an important area of research for the multimedia community. Video key frames enable an user to access any video in a friendly and meaningful way. In this paper, we propose an automated method of video key frame extraction using dynamic Delaunay graph clustering via an iterative edge pruning strategy. A structural constraint in form of a lower limit on the deviation ratio of the graph vertices further improves the video summary. We also employ an information-theoretic pre-sampling where significant valleys in the mutual information profile of the successive frames in a video are used to capture more informative frames. Various video key frame visualization techniques for efficient video browsing and navigation purposes are incorporated. A comprehensive evaluation on 100 videos from the Open Video and YouTube databases using both objective and subjective measures demonstrate the superiority of our key frame extraction method.     

A novel passive forgery detection algorithm for video region duplication

Forgery involving region duplication is one of the most common types of video tampering. However, few algorithms have been suggested for detecting this type of forgery effectively, especially for videos to which a mirroring operation was applied. In this paper, we summarize the properties of duplication forgery of video regions and propose a novel algorithm to detect this forgery. First, the algorithm extracts the feature points in the current frame. The tampered areas in the current frame are then searched, which is implemented in three steps. Finally, our algorithm detects the tampered areas in the remaining frames using spatio-temporal context learning and outputs the detection results. The experimental results demonstrate the satisfactory performance of our algorithm for detecting videos subjected to mirror operations and its higher efficiency than previous algorithms.     

Robust video watermarking based on affine invariant regions in the compressed domain

This paper proposes a novel robust video watermarking scheme based on local affine invariant features in the compressed domain. This scheme is resilient to geometric distortions and quite suitable for DCT-encoded compressed video data because it performs directly in the block DCTs domain. In order to synchronize the watermark, we use local invariant feature points obtained through the Harris-Affine detector which is invariant to affine distortions. To decode the frames from DCT domain to the spatial domain as fast as possible, a fast inter-transformation between block DCTs and sub-block DCTs is employed and down-sampling frames in the spatial domain are obtained by replacing each sub-blocks DCT of 2×2 pixels with half of the corresponding DC coefficient. The above-mentioned strategy can significantly save computational cost in comparison with the conventional method which accomplishes the same task via inverse DCT (IDCT). The watermark detection is performed in spatial domain along with the decoded video playing. So it is not sensitive to the video format conversion. Experimental results demonstrate that the proposed scheme is transparent and robust to signal-processing attacks, geometric distortions including rotation, scaling, aspect ratio changes, linear geometric transforms, cropping and combinations of several attacks, frame dropping, and frame rate conversion.     

A Novel Video Coding Framework with Tensor Representation for Efficient Video Streaming

Video compression is one among the pre-processes in video streaming. While capturing moving objects with moving cameras, more amount of redundant data is recorded along with dynamic change. In this paper, this change is identified using various geometric transformations. To register all these dynamic relations with minimal storage, tensor representation is used. The amount of similarity between the frames is measured using canonical correlation analysis (CCA). The key frames are identified by comparing the canonical auto-correlation analysis score of the candidate key frame with CCA score of other frames. In this method, coded video is represented using tensor which consists of intra-coded key frame, a vector of P frame identifiers, transformation of each variable sized block and information fusion that has three levels of abstractions: measurements, characteristics and decisions that combine all these factors into a single entity. Each dimension can have variable sizes which facilitates storing all characteristics without missing any information. In this paper, the proposed video compression method is applied to under-water videos that have more redundancy as both the camera and the underwater species are in motion. This method is compared with H.264, H.265 and some recent compression methods. Metrics like Peak Signal to Noise Ratio and compression ratio for various bit rates are used to evaluate the performance. From the results obtained, it is obvious that the proposed method performs compression with a high compression ratio, and the loss is comparatively less.

     

Extending critical bandwidth allocation techniques for stored video delivery across best‐effort networks

In this paper, we propose two new techniques for the delivery of compressed prerecorded video streams across best‐effort networks like the Internet. Current approaches for the delivery of stored video across best‐effort networks typically alter the quality of the video frames, the frame rate delivered to the user, or a combination of both. By using network feedback, these algorithms continually adjust the video quality to fit within the available network resources. These approaches, however, do not take advantage of the a priori information available from stored video streams, namely the frame sizes that the movie consists of. We will show how monitoring the a priori information and actively monitoring a client‐side buffer can help smooth the video frame rate delivered to the user, providing a more consistent quality of video. Copyright © 2001 John Wiley & Sons, Ltd.     

Memorable and rich video summarization

Video summarization can facilitate rapid browsing and efficient video indexing in many applications. A good summary should maintain the semantic interestingness and diversity of the original video. While many previous methods extracted key frames based on low-level features, this study proposes Memorability-Entropy-based video summarization. The proposed method focuses on creating semantically interesting summaries based on image memorability. Further, image entropy is introduced to maintain the diversity of the summary. In the proposed framework, perceptual hashing-based mutual information (MI) is used for shot segmentation. Then, we use a large annotated image memorability dataset to fine-tune Hybrid-AlexNet. We predict the memorability score by using the fine-tuned deep network and calculate the entropy value of the images. The frame with the maximum memorability score and entropy value in each shot is selected to constitute the video summary. Finally, our method is evaluated on a benchmark dataset, which comes with five human-created summaries. When evaluating our method, we find it generates high-quality results, comparable to human-created summaries and conventional methods.     

Video frame deletion detection based on time–frequency analysis

With the emergence of diverse multimedia editing software, a great number of edited or tampered video resources appear on the Internet, some of which can mix with the genuine ones. Digital video authenticity is an important step to make the best use of these video resources. As a common video forgery operation, frame tampering can change the video content and confuse viewers by removing or inserting some specific frames. In this paper, we explore the traces created by compression process and propose a new method to detect frame tampering based on the high-frequency features of reconstructed DCT coefficients in the tampered sequences. Experimental results demonstrate that our proposed method can effectively detect frame tampering operation, and accurately locate the breakpoint of frame tampering in the streams.     

基于残留噪声相关性的视频篡改检测算法

提出了一种基于残留噪声相关性的视频篡改检测算法.该算法利用双树复小波域局部维纳滤波的方法获取视频每帧的残留噪声,相邻两残留噪声帧对应块做相关性运算,根据相关系数的统计特性建立累积分布函数模型,设定最佳阈值,定位篡改区域.所提出的提取残留噪声的方法能更好地保留图像细节,减少残留噪声中的场景污迹.实验结果验证了该算法检测篡改视频的准确率更高.     

SIFT based video watermarking resistant to temporal scaling

In this paper, a blind video watermarking scheme is proposed which can resist temporal scaling such as frame dropping and frame rate adaptation due to scalable compression by exploiting the scale invariance property of the scale invariant feature transform (SIFT). A video scene can also be viewed from side plane where height is the number of rows in a video frame, width is the number of frames in the scene and depth is the number of columns in the frame. In this work, intensity values of selected embedding locations changed such that strong SIFT feature can be generated. SIFT features are extracted from side plane of the video. These newly generated SIFT features are used for watermark signal and are stored in the database for the authentication. A comprehensive set of experiments has been done to demonstrate the efficacy of the proposed scheme over the existing literature against temporal attacks.     

Iterative key frame selection in the rate-constraint environment

Video representation through key frames has been addressed frequently as an efficient way of preserving the whole temporal information of sequence with a considerably smaller amount of data. Such compact video representation is suitable for the purpose of video browsing in limited storage or transmission bandwidth environments. In this case, the controllability of the total key frame number (i.e. key frame rate) depending on the storage or bandwidth capacity is an important requirement for the key frame selection method. In this paper, we present a sequential key frame selection method when the number of key frames is given as a constraint. It first selects the pre-determined number of initial key frames and time-intervals. Then, it adjusts the positions of key frames and time-intervals by iteration, which reduces the distortion step by step. Experimental results demonstrate the improved performance of our algorithm over the existing approaches.     

适用于付费数字电视的视频加密方案

将广义骑士巡游与视频加密相结合,提出了一种新的视频加密方案.将原始视频每8帧划分为一个视频片断,把各个视频片断按照帧平面分解为三维空间的广义棋盘,并提出4种方案,通过骑士的跳动置乱加密视频片断,从而达到视频加密的目的.实验结果表明,该方案加密效果好,加密效率高.而且,加密视频视觉质量可控性强,满足安全性要求,在付费数字电视节目播放等方面上具有广阔的应用前景.     

Study on semantic-based video watermarking method

A new video watermarking method for the Audio Video coding Standard (AVS) is proposed. According to human visual masking properties, this method determines the region of interest for watermark embedding by analyzing video semantics, and generates dynamic robust watermark according to video motion semantics, and embeds watermarks in the Intermediate Frequency (IF) Discrete Cosine Transform (DCT) coefficients of the luminance sub-block prediction residual in the region of interest. This method controls watermark embedding strength adaptively by video textures semantics. Experiments show that this method is robust not only to various conventional attacks, but also to re-frame, frame cropping, frame deletion and other video-specific attacks.     

Exposing video inter-frame forgery via histogram of oriented gradients and motion energy image

Multidimensional Systems and Signal Processing - Inter-frame forgery is a common type of video forgery to destroy the video evidence. It occurs in the temporal domain such as frame deletion, frame...     

基于超图排序算法的视频摘要

视频摘要技术作为一种快速感知视频内容的方式得到了广泛的关注.现有基于图模型的视频摘要方法将视频帧作为顶点,通过边表示两个顶点之间的关系,但并不能很好地捕获视频帧之间的复杂关系.为了克服该缺点,本文提出了一种基于超图排序算法的静态视频摘要方法（Hyper-Graph Ranking based Video Summarization,HGRVS）.HGRVS方法首先通过构建视频超图模型,将任意多个有内在关联的视频帧使用一条超边连接;然后提出一种基于超图排序的视频帧分类算法将视频帧按内容分类;最后通过求解提出的一种优化函数来生成静态视频摘要.在Open Video Project和YouTube两个数据集上的大量主观与客观实验验证了所提HGRVS算法的优良性能.     

基于视频自然统计特性的无参考移动终端视频质量评价

针对无线网络中压缩编码及无线丢包等因素对移动终端视频的降质影响,在分析视频相邻帧差信号空-时感知统计特性的基础上,该文提出一种基于视频自然统计特性的无参考移动终端视频质量评价(NMVQA)算法。进行视频帧差空-时自然统计规律分析,确定移动终端视频失真类型对视频相邻帧差系数统计特性的影响;计算水平、垂直、主对角线和副对角线方向的帧差相邻系数乘积分布参数的时域统计特性;以多尺度帧差相邻系数的时域统计特性相关程度来衡量移动终端视频失真程度。在LIVE移动视频数据库上的实验结果表明,该文所提算法的结果与主观评价具有较好的一致性,能够准确反映人类对视频失真程度的视觉感知效果,可为实时在线调节信源码率和无线信道参数提供参考依据。     

CTU depth decision algorithms for HEVC: A survey

High Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The Coding Tree Unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64 × 64 pixels. Each CTU is then divided recursively into a number of equally sized square areas, known as Coding Units (CUs). Although this diversity of frame partitioning increases encoding efficiency, it also causes an increase in the time complexity due to the increased number of ways to find the optimal partitioning. To address this complexity, numerous algorithms have been proposed to eliminate unnecessary searches during partitioning CTUs by exploiting the correlation in the video. In this paper, existing CTU depth decision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches. Statistics approaches are further subdivided into neighboring and inherent approaches. Neighboring approaches exploit the similarity between adjacent CTUs to limit the depth range of the current CTU, while inherent approaches use only the available information within the current CTU. Machine learning approaches try to extract and exploit similarities implicitly. Traditional methods like support vector machines or random forests use manually selected features, while recently proposed deep learning methods extract features during training. Finally, this paper discusses extending these methods to more recent video coding formats such as Versatile Video Coding (VVC) and AOMedia Video 1(AV1).     

Overview of the MVC + D 3D video coding standard

3D video services are emerging in various application domains including cinema, TV broadcasting, Blu-ray discs, streaming and smartphones. A majority of the 3D video content in market is still based on stereo video, which is typically coded with the multiview video coding (MVC) extension of the Advanced Video Coding (H.264/AVC) standard or as frame-compatible stereoscopic video. However, the 3D video technologies face challenges as well as opportunities to support more demanding application scenarios, such as immersive 3D telepresence with numerous views and 3D perception adaptation for heterogeneous 3D devices and/or user preferences. The Multiview Video plus Depth (MVD) format enables depth-image-based rendering (DIBR) of additional viewpoints in the decoding side and hence helps in such advanced application scenarios. This paper reviews the MVC + D standard, which specifies an MVC-compatible MVD coding format.