视频编码量化失真估值算法研究

针对量化失真估计算法存在的误差问题,对算法中的DCT系数分布模型进行了改进,提出了基于LAP分布模型的分段建模方法。将DCT系数的尾部数据进行分离,以主体数据的建模结果作为全体数据的分布模型。以均方误差作为量化失真的衡量准则,归纳并推到上述分布模型下的失真估计算法。采用限制量化步长的方法来评估失真估计算法的准确度。实验结果表明,采用LAP分布模型对DCT系数进行分段建模,不仅提高了模型的准确度,还简化了分布模型的复杂度;由此所产生的失真估计算法在复杂度和准确度方面具有很高的兼容性,与传统的LAP失真估计算法相比,准确度有所提高。     

H.264标准中的变换和量化研究

新一代视频压缩H.264标准,不同于以往的离散余弦变换(DCT),它是基于4×4离散余弦变换和量化,其算法能通过整型变换整数精确地进行计算,避免了"逆变换误差".执行变换系数量化过程不需要进行除法运算,只进行加法和移位运算,从而极大的降低了计算的复杂程度.     

H.264变换编码和量化算法的研究

H.264是ITU和ISO联合制定的新一代视频编码标准,在多方面做出了改进。H.264标准中采用了4×4块的整数变换编码算法,有效地降低了编解码的运算量,且不存在反变换的匹配误差,精度更高。该文详细分析了整数变换编码的构造原理和H.264标准中先后采用的两种整数变换编码算法,阐述了与变换编码相关的量化过程。实验结果证明新的整数变换编码算法能够有效地提高编解码系统的整体性能。     

Methods of Image Compression by PHL Transform

In this paper an image data compression scheme based on Periodic Haar Piecewise-Linear (PHL) transform and quantization tables is proposed. Effectiveness of the compression for different classes of images is evaluated. The comparison of the compression quality using PHL and DCT transforms is given.     

On the computation and the effectiveness of discrete sine transform

The effectiveness of the discrete sine transform (DST) in terms of residual correlation as developed by Hamidi and Pearl[1] is investigated. Based on this criterion, the DST is compared with other discrete transforms such as cosine (DCT)[2] and Fourier (DFT)[3]. The odd-even property of the DST is established. Based on this, the DST is computed through the Walsh-Hadamard transform (WHT). This development parallels that of Jones et al.[4] for other transforms such as slant (ST), DFT, DCT, etc.     

一种新的JPEG图像无参考客观质量评价方法

Parsval’s原理指出,图像压缩前、后像素值的均方误差值等同与图像压缩前、后DCT系数的均方误差值,根据这一思想设计了JPEG图像无参考客观质量评价算法。假设图像的DCT系数是Laplacian分布,首先根据JPEG图像的DCT系数估计出图像压缩前的DCT系数分布参数,并由此估算出图像压缩前、后DCT系数的均方误差值。实验结果表明,该方法所用指标能准确地描述JPEG图像的质量且与空间域均方误差指标相符。     

H.264中全零块预判的新方法

为了避免全零DCT(离散余弦变换)系数进行变换与量化产生的冗余计算,从H.264中的DCT公式与量化原理出发,结合视频图像残差系数的分布规律,在分析现有算法存在的误判与漏判的基础上,提出了一种预先判断全零DCT系数块的方法。实验结果表明,与文献中的经典算法相比较,新提出的方法应用在中低比特率的视频序列中可以减少12%~37%的平均编码时间,平均压缩率提高了6%~10%。     

A robust watermarking algorithm based on QR factorization and DCT using quantization index modulation technique

We propose a robust digital watermarking algorithm for copyright protection.A stable feature is obtained by utilizing QR factorization and discrete cosine transform(DCT) techniques,and a meaningful watermark image is embedded into an image by modifying the stable feature with a quantization index modulation(QIM) method.The combination of QR factorization,DCT,and QIM techniques guarantees the robustness of the algorithm.Furthermore,an embedding location selection method is exploited to select blocks with small modifications as the embedding locations.This can minimize the embedding distortion and greatly improve the imperceptibility of our scheme.Several standard images were tested and the experimental results were compared with those of other published schemes.The results demonstrate that our proposed scheme can achieve not only better imperceptibility,but also stronger robustness against common signal processing operations and lossy compressions,such as filtering,noise addition,scaling,sharpening,rotation,cropping,and JPEG/JPEG2000 compression.     

A proposed secure multiple watermarking technique based on DWT,DCT and SVD for application in medicine

Multimedia Tools and Applications - In this paper, an algorithm for multiple watermarking based on discrete wavelet transforms (DWT), discrete cosine transform (DCT) and singular value...     

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     

An algorithm for automatic round-off error analysis in discrete linear transforms

The discrete linear transforms implemented on computers require a very great number of computations. The round-off errors inherent in the floating-point arithmetic of the computer generate errors in the results which may be fairly large.

In this paper we propose an algorithm based on the La Porte-Vignes Perturbation Method which is able to automatically analyze the round-off error in any discrete linear transform. Furthermore, this algorithm supplies the local accuracy in any discrete transform in the case of experimental data (data errors and round-off errors).     

Programmable vision processor/controller for flexibleimplementation of current and future image compression standards

The vision processor (VP) and vision controller (VC), two integrated products dedicated to video compression, are discussed. The chips implement the P×64, JPEG, and MPEG image compression standards. The VP forms the heart of the image compression system. It performs discrete cosine transform (DCT), quantization, and motion estimation, as well as inverse DCT, and inverse quantization. The highly parallel and microcode-based processor performs all of the JPEG, MPEG, and P×64 algorithms. The VC smart microcontroller controls the compression process and provides the interface to the host system. It captures pixels from a video source, performs video preprocessing, supervises pixel compression by the VP, performs Huffman encoding, and passes the compressed data to the host over a buffered interface. It takes compressed data from the host, performs coder decoding, supervises decompression via the VP, performs postprocessing, and generates digital pixel output for a video destination such as a monitor     

一种基于正则化和Watson 视觉模型的图像指纹算法

大量多媒体应用的发展使得数字图像很容易地被非法操作和篡改,提出一种基于 图像正则化和视觉特性的图像指纹算法,可以有效地实现图像的认证和识别。首先对图像进行 正则化预处理,消除几何形变对图像的影响,然后对图像进行分块DCT 变换,利用Watson 视 觉模型对DCT 系数进行处理,增大人眼敏感的频域系数在计算图像特征时的权重,经过量化形 成最终的指纹序列。在图像指纹序列生成过程中,加入密钥控制,提高了指纹的安全性。实验 结果表明,该方法的冲突概率在10–7 数量级,对JPEG 压缩、旋转、缩放等操作具有较好的稳 健性。     

一个基于矢量量化的数字水印算法

文章提出了一个新的基于矢量量化的数字水印算法,与基于DCT(DiscreteCosineTransform)、DFT(DiscreteFourierTransform)及DWT(DiscreteWaveletTransform)等的传统水印算法不同,该算法利用码书分割方法和矢量量化索引的特点,在矢量量化的不同阶段分别嵌入水印来保护原始图像的版权,水印检测不需要原始图像。实验结果表明,该方法实现的水印具有良好的不可见性,并对JPEG压缩、矢量量化压缩、旋转以及剪切等空域操作也具有较好的稳健性。     

An HVS-based adaptive coder for perceptually lossy image compression

In this paper a locally adaptive wavelet image coder is presented. It is based on an embedded human visual system model that exploits the space- and frequency-localization properties of wavelet decompositions for tuning the quantization step for each discrete wavelet transforms coefficient, according to the local properties of the image. A coarser quantization is performed in the areas of the image where the visibility of errors is reduced, thus decreasing the total bit rate without affecting the resulting visual quality. The size of the quantization step for each DWT coefficient is computed by taking into account the multiresolution structure of wavelet decompositions, so that there is no need for any side information to be sent to the decoder or for prediction mechanisms.Perceptually lossless as well as perceptually lossy compression is supported: the desired visual quality of the compressed image is set by means of a quality factor. Moreover, the technique for tuning the target visual quality allows the user to define arbitrarily shaped regions of interest and to set for each one a different quality factor.     

基于局部DCT系数的图像压缩感知编码与重构

引入了压缩感知(Compressed sensing, CS)理论, 给出了在获取局部二维 离散余弦变换(Discrete cosine transform, DCT)系数的基础上高质量地编码与重构图像的新方法. 研究了在无量化和有量化情况下, 基于局部DCT系数的图像CS最小全变差重构算法. 在对DCT系数进行量化的过程中得到含噪的局部DCT系数, 在此基础上设计了能完成CS重构的图像编解码一般流程, 并构建了实际应用系统. 实验结果表明, 对于稀疏性较强的图像, 在图像编解码系统中结合CS理论与方法能得到高质量的重构图像, 与传统的直接反离散余弦变换(Inverse DCT, IDCT)方法相比, 峰值信噪比(Peak signal to noise ratio, PSNR)最大能提高5dB以上, 对于一般图像, PSNR也有较大提高.     

Nonlinear Discrete Wavelet Transforms over Finite Sets and an Application to Binary Image Compression

The discrete wavelet transform was originally a linear operator that works on signals that are modeled as functions from the integers into the real or complex numbers. However, many signals have discrete function values. This paper builds on two recent developments: the extension of the discrete wavelet transform to finite-valued signals and the research of nonlinear wavelet transforms triggered by the introduction of the lifting scheme by Sweldens. It defines discrete wavelet transforms as bijective, translation-invariant decompositions of signals that are functions from the integers into any finite set. Such transforms are essentially nonlinear, but they can be calculated very time efficiently since only discrete arithmetic is needed. Properties of these generalized discrete wavelet transforms are given along with an elaborate example of such a transform. In addition, the paper presents some ideas to find explicit examples of discrete wavelet transforms over finite sets. These ideas are used to show that, in case the finite set is a ring, there are much more nonlinear transforms than linear transforms. Finally, the paper exploits this increased number of transforms to do lossless compression of binary images.The research is sponsored (grant no. 613.006.570) by the Dutch Science Foundation (NWO).     

扩频通信抗干扰中的局部离散余弦变换技术

提出一种新的基于局部离散余弦变换(Local discrete cosine transform，LDCT)的时频域干扰抑制方法。与传统的块变换(Discrete cosine transform，DCT)不同，LDCT基函数具有良好的时域能量集中特性，可迅速捕获信号中的时变成分，有效跟踪信号时频结构的变化，实现时频平面的任意时间分割。仿真结果表明，基于LDCT的时频域信号处理方法可有效地抑制各种脉冲型时变干扰，明显改善系统性能。     

图像压缩中的快速方向离散余弦变换

传统的二维DCT(discrete cosine transform)无法稀疏表示除水平或垂直方向以外的边缘,而具有强方向表示能力的方向预测离散余弦变换(directional prediction DCT,简称DPDCT)计算复杂度又过高.针对这些问题,提出了一种快速方向离散余弦变换(fast directional discrete cosine transform,简称FDDCT).该算法沿给定的方向模式进行变换,避免了DPDCT中的插值运算,可以快速、稀疏地表示图像中各向异性边缘信息.此外,FDDCT通过设计块边界提升,在进一步集中边缘能量的同时保证了算法的完全重构.实验结果表明,FDDCT计算复杂度不超过DCT的1.4倍;采用同样的编码方法,基于FDDCT的压缩图像与基于DCT以及DPDCT的压缩图像相比,峰值信噪比可提高0.4dB~1.6dB,而且边缘细节更加清晰、完整.     

An improved lossless data hiding scheme based on image VQ-index residual value coding

Copyright protection and information security have become serious problems due to the ever growing amount of digital data over the Internet. Reversible data hiding is a special type of data hiding technique that guarantees not only the secret data but also the cover media can be reconstructed without any distortion. Traditional schemes are based on spatial, discrete cosine transformation (DCT) and discrete wavelet transformation (DWT) domains. Recently, some vector quantization (VQ) based reversible data hiding schemes have been proposed. This paper proposes an improved reversible data hiding scheme based on VQ-index residual value coding. Experimental results show that our scheme outperforms two recently proposed schemes, namely side-match vector quantization (SMVQ)-based data hiding and modified fast correlation vector quantization (MFCVQ)-based data hiding.