Efficient audio integrity verification algorithm using discrete cosine transform

Audio recordings have been used as evidence for long times. Multimedia processing advancement makes it difficult to be completely sure about what is heard is the truth. This paper presents a promising approach for integrity verification of recorded audio signals using discrete cosine transform. This approach is based on self embedding concept which embeds block-based marks extracted from the same audio signal after being transformed into 2-D format into other blocks according to a specific algorithm. After the self-embedding process, the data is converted back into 1-D style which represents a marked audio signal. The 1-D audio signal is converted into a 2-D format and then converted back into a 1-D format using the popular lexicographic ordering scheme utilized in image processing. Reverse processes are executed to extract the verification marks from the audio signal throughout the integrity verification process. Based on the extracted audio signal properties, the integrity of the marked audio signal is evaluated. Different audio processing tasks and attacks are implemented to examine the suitability of the proposed algorithm for verifying the integrity of high-confidentiality recorded audio data. The results show that the efficient ability of the proposed approach to verify integrity and detect attacks.     

Fourier domain scoring: a novel document ranking method

Current document retrieval methods use a vector space similarity measure to give scores of relevance to documents when related to a specific query. The central problem with these methods is that they neglect any spatial information within the documents in question. We present a new method, called Fourier Domain Scoring (FDS), which takes advantage of this spatial information, via the Fourier transform, to give a more accurate ordering of relevance to a document set. We show that FDS gives an improvement in precision over the vector space similarity measures for the common case of Web like queries, and it gives similar results to the vector space measures for longer queries.     

A comparison of fast inverse discrete cosine transform algorithms

The discrete cosine transform (DCT) is often applied to image compression to decorrelate picture data before quantization. This decorrelation results in many of the quantized transform coefficients equaling zero, hence the compression gain. At the decoder, very few nonzero quantized transform coefficients are received, so the input to the inverse DCT is sparse, greatly reducing the required computation. This paper describes different styles of implementations of fast inverse DCTs designed especially for sparse data and compares them on workstation processors.This research has been sponsored by ARPA     

Two-dimensional discrete cosine transform on sliding windows

The discrete cosine transform (DCT) has been successfully used for a wide range of applications in digital signal processing. While there are efficient algorithms for implementing the DCT, its use becomes difficult in the sliding transform scenario where the transform window is shifted one sample at a time and the transform process is repeated. In this paper, a new two-dimensional sliding DCT (2-D SDCT) algorithm is proposed for fast implementation of the DCT on 2-D sliding windows. In the proposed algorithm, the DCT coefficients of the shifted window are computed by exploiting the recursive relationship between 2-D DCT outputs of three successive windows. The theoretical analysis shows that the computational requirement of the proposed 2-D SDCT algorithm is the lowest among existing 2-D DCT algorithms. Moreover, the proposed algorithm enables independent updating of each DCT coefficient.     

基于余弦变换和小波变换的数字水印算法

在互联网络技术和多媒体技术普及的今天,数字水印技术已经成为目前信息安全技术领域的一个重要方向。本文提出了一种基于离散余弦变换和离散小波变换的数字水印算法,以一个长度为n服从N(0,1)正态分布的随机数序列作为水印信号嵌入到图像最具感知意义的频率部分。实验结果表明,此算法具有良好的视觉效果和鲁棒性。     

一种基于zigzag扫描的DCT鲁棒数字盲水印算法

针对提高水印算法的鲁棒性进行研究,提出一种基于zigzag扫描的DCT鲁棒数字盲水印算法。算法对分块后DCT域的中、低频系数进行zigzag扫描得到一维数列,利用每个数列数学范数比值和量化索引调制原理实现数字水印的嵌入,然后通过最小距离公式对水印提取。与其他算法相比,实验结果显示算法能够抵抗多种攻击,具有较好的透明性与鲁棒性。水印提取过程不需要原载体图像,实现水印信息盲提取。     

On the embedding limits of the discrete cosine transform

This paper investigates the embedding capacity limits of high-capacity data hiding in color images based on a locally Adaptive-Region Discrete Cosine Transform (AR-DCT) frequency domain data hiding scheme, and explores the relationship between hiding capacity and image quality. It also compares the embedding capacities of various steganography schemes which have been recently published in the literature. Experimental results confirm that our proposed scheme successfully enhances hiding capacity while maintaining acceptable image quality and concludes that the capacity for our DCT hiding scheme can achieve extremely high bit rates of 20 bits-per-pixel, which is much higher than other DCT-based approaches, as well as other spatial and frequency domain schemes.     

Video coding using geometry based block partitioning and reordering discrete cosine transform

Geometry based block partitioning(GBP) has been shown to achieve better performance than the tree structure based block partitioning(TSBP) of H.264.However,the residual blocks of GBP mode after motion compensation still present some nonvertical/non-horizontal orientations,and the conventional discrete cosine transform(DCT) may generate many high-frequency coefficients.To solve this problem,in this paper we propose a video coding approach by using GBP and reordering DCT(RDCT) techniques.In the proposed approach,GBP is first applied to partition the macroblocks.Then,before performing DCT,a reordering operation is used to adjust the pixel positions of the residual macroblocks based on the partition information.In this way,the partition information of GBP can be used to represent the reordering information of RDCT,and the bitrate can be reduced.Experimental results show that,compared to H.264/AVC,the proposed method achieves on average 6.38% and 5.69% bitrate reductions at low and high bitrates,respectively.     

Video compression using frame redundancy elimination and discrete cosine transform coefficient reduction

The need of human beings for better social media applications has increased tremendously. This increase has necessitated the need for a digital system with a larger storage capacity and more processing power. However, an increase in multimedia content size reduces the overall processing performance. This occurs because the process of storing and retrieving large files affects the execution time. Therefore, it is extremely important to reduce the multimedia content size. This reduction can be achieved by image and video compression. There are two types of image or video compression: lossy and lossless. In the latter compression, the decompressed image is an exact copy of the original image, while in the former compression, the original and the decompressed image differ from each other. Lossless compression is needed when every pixel matters. This can be found in autoimage processing applications. On the other hand, lossy compression is used in applications that are based on human visual system perception. In these applications, not every single pixel is important; rather, the overall image quality is important. Many video compression algorithms have been proposed. However, the balance between compression rate and video quality still needs further investigation. The algorithm developed in this research focuses on this balance. The proposed algorithm exhibits diversity of compression stages used for each type of information such as elimination of redundant and semi redundant frames, elimination by manipulating consecutive XORed frames, reducing the discrete cosine transform coefficients based on the wanted accuracy and compression ratio. Neural network is used to further reduce the frame size. The proposed method is a lossy compression type, but it can reach the near-lossless type in terms of image quality and compression ratio with comparable execution time.

     

High-capacity steganography: a global-adaptive-region discrete cosine transform approach

An increasing number of spatial and frequency domain data hiding techniques have been proposed to address the relatively low embedding capacities of image-based steganography. These techniques have brought promise of higher embedding capacities, albeit at the expense of lower perceptibility. This work proposes a new discrete cosine transform (DCT) approach for color image steganography and implements a global-adaptive-region (GAR) embedding scheme that allows for extremely high embedding capacities while maintaining enhanced perceptibility. The idea is to adapt the variable region size, used to hide the data, in each DCT block of the cover image to the amount of correlation of the image values in the corresponding block. We will demonstrate how this new technique achieves enhanced hiding capacities and perceptibility compared to other spatial, Fourier, and adaptive-region DCT based steganography schemes.     

离散余弦变换域图像数字水印技术

基于变换域的水印技术由于其较好的鲁棒性和安全性成为了目前水印技术领域研究的热点.详细地分析了目前离散余弦变换域的图像数字水印技术,对离散余弦变换域的算法进行分类比较,并对拉伸因子a的取值以及对图像水印嵌入效果的影响进行了讨论,提出了一些有效的水印方案,实验结果表明了其对常见攻击的抵抗性.分析和讨论了DCT变换域图像数字水印技术在嵌入水印方面的实现.     

基于离散小波变换和离散余弦变换域的扩频水印盲提取算法

针对扩频水印的盲提取问题,提出了一种在数字音频中扩频水印的盲提取算法。算法将扩频后的水印信息隐藏在音频文件小波分解的低频系数再做离散余弦变换(DCT)后的第5个系数中。提取时在扩频序列及其长度均未知的情况下,采用二次谱和奇异值分解(SVD)的方法对嵌入时使用的扩频参数进行估计,实现了数字音频中扩频水印的盲提取。仿真实验表明,所提算法在未知扩频参数的情况下能提取出归一化系数(NC)为1的水印图像并且水印的鲁棒性也很强,在加噪、低通滤波等攻击下估计出的扩频序列正确率能达到90%以上,恢复出的水印图像清晰可见,归一化系数都在0.98以上。     

An algebraic method for synthesizing fast algorithms of discrete cosine transform of arbitrary size

An algebraic method for synthesizing fast algorithms of the discrete cosine transform (DCT) of arbitrary size is proposed. The method is based on the polynomial algebra $\mathbb{F}{{[x]} \mathord{\left/ {\vphantom {{[x]} {p(x)}}} \right. \kern-0em} {p(x)}}$ associated with the DCT. The fast DCT algorithm comes as a result of the step-by-step decomposition of this algebra. In turn, the decomposition requires step-by-step factorization of the polynomial p(x). This problem is solved using Galois??s theory, which allows finding all the subfields of the splitting field of the polynomial p(x) where p(x) can be factorized.     

Reduction of discrete cosine transform/ quantisation/inverse quantisation/inverse discrete cosine transform computational complexity in H.264 video encoding by using an efficient prediction algorithm

This study develops a novel prediction algorithm to effectively save the computational complexity of discrete cosine transform (DCT), quantisation (Q), inverse Q (IQ), and inverse DCT (IDCT) in video encoding for H.264 applications. Based on the DC value of the DCT coefficients that is equal to the sum of residual data in the 4times4 sub-macroblock (sub-MB), a mathematical model is built to develop a prediction algorithm for reducing the computations in the DCT/Q/IQ/IDCT process. Experimental results and comparisons demonstrate that the proposed prediction algorithm significantly reduces the encoding time while incurring little additional overhead, and lowers the bit rate with little peak signal-to-noise ratio degradation.     

Wavelet domain-based video noise reduction using temporal discrete cosine transform and hierarchically adapted thresholding

A novel spatio-temporal filter for video denoising, which operates entirely in the wavelet domain, is proposed. For effective noise reduction, the spatial and temporal redundancies that exist in the wavelet domain representation of a video signal are exploited. First, a 2D discrete wavelet transform is applied to the input noisy frames. This is followed by a discrete cosine transform (DCT), which is applied to the temporal subband coefficients to minimise the redundancy among the consecutive frames. The DCT transformed, noise-free coefficients in the different wavelet domain subbands for the original image sequence are modelled using a prior having a generalised Gaussian distribution. On the basis of this prior, filtering of the noisy wavelet coefficients in each subband is carried out using a new, low-complexity wavelet shrinkage method, which utilises the correlation that exists between subsequent resolution levels. Experimental results show that the proposed scheme outperforms several state-of-the-art spatio-temporal filters in terms of both the peak signal-to-noise ratio and the visual quality     

适用于H.264的直接二维离散余弦变换

DCT(discrete cosine transform)变换作为视频压缩技术的重要组成部分,在H.264中起到了关键作用.由于H.264的算法复杂度高,因此需要研究更加高效的算法,DCT变换是H.264的重要组成部分,所以提高其效率将使整个系统效率得到很大的提高.在H.264中,用行列法实现4×4的二维离散余弦变换(2D-DCT),并对变换系数作了近似来简化计算,由此也引入了误差.提出了利用多项式变换直接分解2D-DCT的方法,给出了新的蝶式计算流图,提高了计算速度.通过实验对比得出新算法的频率特性比H.264的方法更接近标准的DCT,去相关的效果更好,因此对不同序列图像的压缩性能得到了提高.     

适用于分布式视频编码框架的整数离散余弦变换算法

鉴于H.264的整数离散余弦变换(DCT)算法及其量化方法复杂度高,难以直接应用于分布式视频编码(DVC)框架的现状,提出了一种基于大跨度定长(步长为2的正整数次方)量化的整数DCT算法及变换基生成方法。该算法充分地利用整数DCT基的可伸缩特性寻找最迎合硬件工作原理的变换基,在保证“小”变换基的同时将编码器的伸缩量化阶段“转移”到解码器一端以降低编码器复杂度。在“转移”过程中,该算法利用DCT系数饱和放大保证图像质量,利用DCT系数的溢出上限保证算法的可靠性,通过减小基偏差提高压缩性能。实验结果表明,与H.264对应模块相比,该算法的量化方式便于位平面提取,在图像质量达到准无损压缩的前提下将编码器的伸缩量化阶段的运算量缩减至16次整型常量加法运算,图像质量与压缩率的性价比提升了23.9%,适用于分布式编码框架。     

OFDM系统中基于离散余弦变换的信道估计

提出了两种基于离散余弦变换（DCT）的信道估计方法,适用于多径衰落的正交频分复用（OFDM）系统。利用DCT的能量压缩特性,能有效消除传统基于离散傅立叶变换（DFT）估计算法在信道延时不是采样周期整数倍,或系统子栽波数不等于有用于载波数时产生的频谱泄漏。根据DCT与DFT的等效关系,通过构造不同的对称数据序列,推出两种相应的基于DCT信道估计,其中一种具有更好的性能,另一种更利于实现。仿真和分析结果表明：在多径衰落信道下,两种基于DCT的方法能有效降低频谱泄漏造成的信道估计误差,具有比基于DFT方法更好的性能。     

Detection of copy-move image forgery based on discrete cosine transform

Since powerful editing software is easily accessible, manipulation on images is expedient and easy without leaving any noticeable evidences. Hence, it turns out to be a challenging chore to authenticate the genuineness of images as it is impossible for human’s naked eye to distinguish between the tampered image and actual image. Among the most common methods extensively used to copy and paste regions within the same image in tampering image is the copy-move method. Discrete Cosine Transform (DCT) has the ability to detect tampered regions accurately. Nevertheless, in terms of precision (FP) and recall (FN), the block size of overlapping block influenced the performance. In this paper, the researchers implemented the copy-move image forgery detection using DCT coefficient. Firstly, by using the standard image conversion technique, RGB image is transformed into grayscale image. Consequently, grayscale image is segregated into overlying blocks of m × m pixels, m = 4.8. 2D DCT coefficients are calculated and reposition into a feature vector using zig-zag scanning in every block. Eventually, lexicographic sort is used to sort the feature vectors. Finally, the duplicated block is located by the Euclidean Distance. In order to gauge the performance of the copy-move detection techniques with various block sizes with respect to accuracy and storage, threshold D_similar = 0.1 and distance threshold (N)_d = 100 are used to implement the 10 input images in order. Consequently, 4 × 4 overlying block size had high false positive thus decreased the accuracy of forged detection in terms of accuracy. However, 8 × 8 overlying block accomplished more accurately for forged detection in terms of precision and recall as compared to 4 × 4 overlying block. In a nutshell, the result of the accuracy performance of different overlying block size are influenced by the diverse size of forged area, distance between two forged areas and threshold value used for the research.

     

Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology

Discrete cosine transform (DCT) is a powerful transform to extract proper features for face recognition. After applying DCT to the entire face images, some of the coefficients are selected to construct feature vectors. Most of the conventional approaches select coefficients in a zigzag manner or by zonal masking. In some cases, the low-frequency coefficients are discarded in order to compensate illumination variations. Since the discrimination power of all the coefficients is not the same and some of them are discriminant than others, so we can achieve a higher true recognition rate by using discriminant coefficients (DCs) as feature vectors. Discrimination power analysis (DPA) is a statistical analysis based on the DCT coefficients properties and discrimination concept. It searches for the coefficients which have more power to discriminate different classes better than others. The proposed approach, against the conventional approaches, is data-dependent and is able to find DCs on each database. The simulations results of the various coefficient selection (CS) approaches on ORL and Yale databases confirm the success of the proposed approach. The DPA-based approaches achieve the performance of PCA/LDA or better with less complexity. The proposed method can be implemented for any feature selection problem as well as DCT coefficients. Also, a new modification of PCA and LDA is proposed namely, DPA-PCA and DPA-LDA. In these modifications DCs which are selected by DPA are used as the input of these transforms. Simulation results of DPA-PCA and DPA-LDA on the ORL and Yale database verify the improvement of the results by using these new modifications.