基于局部线性嵌入的视频拷贝检测方法

该文提出了基于局部线性嵌入(LLE)的视频哈希方法,该方法首先利用一个图模型选取代表帧,然后以四阶累积量作为视频在高维空间的特征并利用LLE对视频进行降维,利用视频在3维空间中投影点的范数构造视频哈希序列来实现视频拷贝检测。实验证明该方法具有较好的鲁棒性和区分性。     

一种基于加速近端梯度法的视频散列算法研究

散列算法已经被广泛应用于视频数据的索引。然而,当前大多数视频散列方法将视频看成是多个独立帧的简单集合,通过综合帧的索引来对每个视频编制索引,在设计散列函数时往往忽略了视频的结构信息。首先将视频散列问题建模为结构正规化经验损失的最小化问题。然后提出一种有监管算法,通过利用结构学习方法来设计高效的散列函数。其中,结构正规化利用了出现于视频帧(与相同的语义类别存在关联)中的常见局部视觉模式,同时对来自同一视频的后续帧保持时域一致性。证明了通过使用加速近端梯度(APG)法可有效求解最小化目标问题。最后,基于两个大规模基准数据集展开全面实验(150 000个视频片断,1 200万帧),实验结果证明了该方法性能优于当前其他算法。     

基于Zernike矩和熵的图像感知哈希算法

提出了基于Zernike矩和熵特征的数字图像感知哈希算法。算法利用Zernike矩计算参考方向,以计算等面积环块和等角度扇形块内的熵作为感知特征,并通过量化处理构造哈希序列。算法利用哈希码之间的欧氏距离作为图像内容相似性的判定依据。实验结果表明,该算法对加性噪声、JEPG压缩、几何变换等操作具有较好的鲁棒性,且对于内容不同的图像有较好的区分度。     

分布式压缩视频编码的帧分类重构方法研究

在很多的应用场景中需要具有低复杂度的视频编码器,新兴的分布式视频编码和压缩感知技术正好适用于这些场景中,因而出现了一种新的视频编码方案——分布式压缩视频编码。在现有的一些分布式压缩视频编码方案中,视频帧在编码端是独立编码,在解码端进行联合解码,具体来说就是关键帧独立解码,非关键帧在由关键帧生成的边信息的帮助下进行解码,这就忽略了非关键帧之间的相关性。本文提出一个新的分布式视频编码方案,将非关键帧分为主非关键帧和次非关键帧,主非关键帧利用关键帧生成地边信息进行解码,而次非关键帧先利用相邻的主非关键帧进行观测值预测,然后再利用关键帧生成的边信息进行解码。实验结果表明,在本文提出的框架下,非关键帧的重构质量提高了有2dB~4dB。      

Four-dimensional matrix Walsh transform for lossless compression of color video

This article presents a coding method for the lossless compression of color video.In the proposed method,four-dimensional matrix Walsh transform(4D-M-Walsh-T)is used for color video coding.The whole n frames of a color video sequence are divided into '3D-blocks' which are image width(row component),image height(column component),image width(vertical component)in a color video sequence,and adjacency(depth component)of n frames(Y,U or V)of the video sequence.Similar to the method of 2D-Walsh transform,4D-M-Walsh-T is 4D sub-matrices,and the size of each sub-matrix is n.The method can fully utilize correlations to encode for lossless compression and reduce the redundancy of color video,such as adjacent pixels in one frame or different frames of a video at the same time.Experimental results show that the proposed method can achieve higher lossless compression ratio(CR)for the color video sequence.     

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.     

基于局部Zernike矩的RST不变水印

旋转、缩放和平移(RST)等几何攻击能够破坏水印检测的同步性,而使常规水印检测失败,提出了一种基于图像局部Zernike矩的RST不变水印算法.利用图像归一化后的Zernike矩幅度具有RST不变的性质,将由Zernike矩重构的水印图像在空域嵌入原始图像的局部中,并提取该区域的Zernike矩幅度矢量作为水印矢量.水印检测时,计算局部区域的Zernike矩,并与已知Zernike矩矢量计算均方误差根(RMSE)判断水印存在与否.与使用图像全局Zernike矩相比,使用局部矩使得水印具有更好的旋转不变性,同时水印对于缩放、JPEG压缩和噪声等攻击也具有较好的检测性能.     

基于Krawtchouk矩不变量和核模糊聚类的快速分形编码

针对基于分类的快速分形编码方法存在着编码速度与解码质量间的矛盾,鉴于Krawtchouk矩不变量具有在仿射变换下保持不变的特性和核模糊聚类在处理非线性问题上的突出优势,本文首次将这两者引入到分形编码中,提出了基于Krawtchouk矩不变量和核模糊聚类的自适应分类快速分形编码方法。首先根据Domain块的方差将其粗分类,再根据Domain块的Krawtchouk矩不变量利用核模糊聚类对Domain块细分类。实验结果表明,与其他基于分类的快速分形编码方法相比,在解码图像质量提高的同时,大大加快了分形编码的速度。     

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.

     

基于Tchebichef不变矩的多比特抗几何攻击图像盲水印算法

现有的大多数水印算法对旋转、平移和尺度变换等几何攻击的鲁棒性比较差,微小的几何攻击都有能导致水印检测器失效,因此水印算法对几何攻击具有鲁棒性非常重要。本文提出了一种基于Tchebichef不变矩实现的多比特几何攻击不变性图像盲水印算法。文中具体介绍了Tchebichef不变矩的构造方法,水印是事先产生的与原始图像无关的信号,嵌入过程中将水印嵌入到图像的Tchebichef不变矩中实现几何攻击不变性。水印检测过程中采用独立分量分析技术实现真正意义上的多比特水印盲检测。文中具体分析了所提出的水印算法的计算复杂度,实验过程中采用通用水印测试软件Stirmark对所提出的水印算法进行鲁棒性测试,实验数据说明这种水印算法对Stirmark具有很好的鲁棒性。     

Improving video quality by predicting inter-frame residuals based on an additive 3D-CNN model

Video compression is essential for uploading videos to online platforms which usually have bandwidth limitations. However, the compression reduces the visual quality. To overcome this problem, the visual quality of the low bitrate compressed videos for various standards, including H.264 and HEVC in decoders, needs to be improved. Accordingly, this paper proposes a novel method for improving video quality based on 3D convolutional neural networks (CNNs). This method is totally compatible with the encoders of video compression standards, i.e., H.264, VVC, and HEVC, and can be implemented easily. In particular, the proposed neural network model receives five frames of the low bitrate compressed video as input and subsequently predicts the compression error of frames using the first and fifth frames. Finally, it reconstructs an improved version of the frame with high quality. The CNN is an Additive (3D) model that can predict the eliminated inter-frame redundancies resulting from compression. Our goal is to increase the peak signal to noise ratio (PSNR) and structural index similarity (SSIM) of the luminance (Y) and chrominance (U, V) frames in the video. Additive 3D-CNN achieves an average of 12.4%, 9.9% and 5% BD-rate increases for LP, LB and RA for the Y component. The results indicate that the new proposed algorithm outperforms the previous methods in terms of PSNR, SSIM, and BD-rate.     

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.     

智能型目标识别系统

介绍了图像目标识别技术中的图像分割，不变性参数提取和目标分类，利用图像目标的均匀性和相应知识自适应地分割和提取图像目标，被提取的每个图像目标的不变性参数由归一化过程和Ｚｅｒｎｉｋｅ矩提取，并利用ＭＰＮＮ模型将图像目标分类，实验结果该识别系统能识别光照不均匀或复杂背景下的图像目标。     

PSO-based Fusion Method for Video Super-Resolution

In this study, video super-resolution using particle swarm optimization (PSO) is proposed to super-resolve low-resolution (LR) frames. The proposed super-resolution method consists of three main modules, i.e., supersampling, spatio-temporal classification, and frame fusion using PSO. In the proposed method, the LR frames are super-resolved to high-resolution frames through the fusion of four full-resolution frames. One of four full-resolution frames is obtained using direct spatial interpolation, and the other three are obtained using motion compensation with given reference frames. The essence of the proposed method is the spatio-temporal classification mechanism that exploits the temporal variation between frames and the spatial energy inside the frame. Using the classification results, PSO is used to determine the optimal weights for frame fusion. Simulation results show that the proposed fusion method successfully improves the perceptual quality and the average peak signal-to-noise ratio (PSNR) in super-resolved frames.     

SHIP: Cross-layer mobility management scheme based on Session Initiation Protocol and Host Identity Protocol

In law enforcement applications such as surveillance and forensics, video is often presented as evidence. It is therefore of paramount importance to establish the authenticity and reliability of the video data. This paper presents an intelligent video authentication algorithm which integrates learning based Support Vector Machine classification with Singular Value Decomposition watermarking. During video capture and storage, intrinsic local correlation information is extracted from the frames and embedded in the frames at local levels. Tamper detection and classification is performed using the inherent video information and embedded correlation information. The proposed algorithm is independent of the choice of watermark and does not require any key to store. Further, it is robust to global tampering such as frame addition and removal, local attacks such as object alteration and can differentiate between acceptable operations and malicious tampering. Experiments are performed on an extensive database which contains non-tampered videos and videos with several types of tampering. The results show that the proposed algorithm outperforms existing video authentication algorithms.     

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。     

结合卷积神经网络的HEVC帧内编码压缩改进算法

近年来,卷积网络深度学习已在图像处理、目标检测等领域取得巨大成功。受其启发,将卷积神经网络(CNN)应用于传统视频压缩标准已成为一个新的研究热点。本文提出一种集成卷积神经网络的高效视频编码(HEVC)压缩改进算法,将下采样过程、HEVC的编解码过程、上采样及质量增强过程集成为一体。为高效提取视频帧的结构特征,在所提压缩算法中集成了两个卷积神经网络。提出了一种下采CNN(DwSCNN)代替双三次下采,在有效降低分辨率的同时保留细节信息,得到更为紧凑的低分辨率视频序列,将此低分辨率视频序列通过HEVC帧内编码进行进一步的数据量压缩,通过提出一个质量增强CNN(PPCNN)来改善解码后恢复到原始分辨率的降质视频序列。实验结果显示,本文压缩改进算法在低码率段与标准HEVC相比,能达到更好的质量重建,并且在接近一致的PSNR值时,能节省39.46%的时间和11.04%的比特率,本文算法的视频压缩性能优于HEVC标准算法和相关文献方法。     

一种空间域Wyner-Ziv视频编码系统的性能改进算法

分布式视频编码是建立在Slepian-Wolf和Wyner-Ziv信息编码理论基础上的全新视频编码框架,具有编码复杂度低,编码效率较高,抗误码性能好的特点.本文首先简单介绍了一种典型的分布式视频编码实现方案——空间域Wyner-Ziv视频编码,随后提出一种空间域Wyner-Ziv视频编码系统的性能改进算法,该算法在不增加编码复杂度的基础上,在解码端利用双向运动估计预测获取更高质量的边信息,同时采用基于Huber-Markov随机场约束的联合迭代解码算法重建图像.实验结果表明,在相同的输出码流情况下,本文改进算法在解码端重建图像的峰值信噪比与空间域Wyner-Ziv视频编码算法相比平均提高2dB,并且主观效果有所改善.     

Online Dictionary Learning Based Intra-frame Video Coding

In this paper, we propose an online learning based intra-frame video coding approach, exploiting the texture sparsity of natural images. The proposed method is capable of learning the basic texture elements from previous frames with convergence guaranteed, leading to effective dictionaries for sparser representation of incoming frames. Benefiting from online learning, the proposed online dictionary learning based codec (ODL codec) is able to achieve a goal that the more video frames are being coded, the less non-zero coefficients are required to be transmitted. Then, these non-zero coefficients for image patches are further quantized and coded combined with dictionary synchronization. The experimental results demonstrate that the number of non-zero coefficients of each frame decreases rapidly while more frames are encoded. Compared to the off-line mode training, the proposed ODL codec, learning from video on the fly, is able to reduce the computational complexity with fast convergence. Finally, the rate distortion performance shows improvement in terms of PSNR compared with the K-SVD dictionary based compression and H.264/AVC for intra-frame video at low bit rates.     

Robust image hash in Radon transform domain for authentication

Image authentication has become an emergency issue in the digital world as it can be easily tampered with the image editing techniques. In this paper, a novel robust hashing method for image authentication is proposed. The reported scheme first performs Radon transform (RT) on the image, and calculates the moment features which are invariant to translation and scaling in the projection space. Then discrete Fourier transform (DFT) is applied on the moment features to resist rotation. Finally, the magnitude of the significant DFT coefficients is normalized and quantized as the image hash bits. Experimental results show that the proposed algorithm can tolerate almost all the typical image processing manipulations, including JPEG compression, geometric distortion, blur, addition of noise, and enhancement. Compared with other approaches in the literature, the reported method is more effective for image authentication in terms of detection performance and the hash size.