压缩域DCT系数对图像视频检索影响的研究

通过对图像在原始数据域和压缩数据域上的表现差异性进行统计分析，进一步研究了基于DCT变换的图像视频压缩域部分解码DCT信息，并运用于MPEG4的编解码的程序实现。随后提出了一种提高压缩域图像视频检索效率的方法，进而针对Intel X86处理器平台改进了DC 2AC的快速算法。     

基于DCT压缩域的图像检索方法

本文提出了一种基于DCT(Discrete Cosine Transform)压缩域的图像检索方法.对于DCT编码的图像数据,在不需要完全解码的情况下,直接抽取图像的内容特征进行图像检索.首先,重组DCT域的频率系数,使其具有方向性、多分辨率等特点,并利用这些特点提取图像的大致轮廓.接着统计图像轮廓的连通直方图(CRH:Connected-Region Histogram),进行图像检索.并利用DC图的灰度直方图对检索结果进行重新排序.这种检索方法对灰度、旋转、平移等都具有一定的鲁棒性,具有较好的检索效果.     

基于加权DCT系数的图像检索算法

文章针对DCT在图像编码中的重要地位,进行了基于DCT压缩域的图像检索性能的研究。以MPEG-7中的颜色布局描述符为图像特征,根据DCT系数的能量分布特征,选取适当数量的DCT系数,并对所选取DCT系数赋以不同的权重得到新的颜色特征,采用欧氏距离作为相似度量直接在DCT域内对彩色图像进行检索。并在此基础上研究了DCT系数权重的选择与图像检索性能之间的关系,最后对检索结果进行了比较详细的分析。结果表明,根据具体情况对DCT系数选择适当的权重可以得到比较满意的检索结果。     

一种JPEG2000压缩域图像检索方法

该文首先分析JPEG2000压缩域数据结构,在此基础上,提出信息树的概念,并提出一种新的树距离度量方法。接着提出一种JPEG2000压缩域图像检索方法,实验结果表明,该文方法能取得明显的检索效果。     

基于嵌入式零树小波编码直方图图像检索

图像和视频应用的快速增长，使得根据图像和视频内容进行查询的技术变得越来越重要，人们提出了许多基于像素域或压缩域的图像检索技术，因为多媒体数据库通常具有相当大的数据量，所以基于像素域图像检索技术的计算复杂度相当大，因此，许多文献提出更快的基于压缩域的图像检索技术，本文提出一种改进的基于嵌入式零树小波编码直方图的图像检索技术，特征提取综合考虑图像的颜色，纹理，频率和空间信息，所有的特征可以在压缩过程中自动得到，图像检索的过程就是匹配待检索图像和来自数据库的侯选图像的索引，实验证明这种方法具有好的检索性能。     

计算技术与计算机及网络设备 计算机检索、机器翻译编辑、自动标引、自动文摘等

0628170基于特征点匹配和哈希法的图像检索方法〔刊,中〕/于淼//微计算机应用.—2006,27(4).—397-400(G)0628171视频运动特征提取模块的实现〔刊,中〕/张培珍//计算机工程与设计.—2006,27(10).—1780-1781,1785(L)0628172基于统计特征的DCT压缩域纹理图像检索方法〔刊,中〕/张曦煌//计算机工程与设计.—2006,27(7).—1282-1285(L)提出了一种基于离散余弦变换(Discrete CosineTransfrom,DCT)的纹理图像的检索方法。该方法在DCT压缩域,通过直接对DCT系数计算,获得图像纹理的统计特征,并作为检索的依据。理论分析和实验结果都表明,该…     

快速小波熵在图像检索中的应用

图像检索技术是多媒体应用中的关键技术,现有的基于内容的图像检索技术大都是基于非压缩域的.随着各种压缩标准的推出与普及,人们开始研究直接在压缩域的图像检索技术.由于小波变换在图像压缩中的应用,文献中提出了基于小波直方图的图像检索技术,但该技术的计算复杂性高,检索速度较慢.提出了快速小波熵的图像检索技术以减少小波直方图技术的计算复杂性并提高了检索速度.     

一种分形域基于内容的图像检索方法

基于内容的图像检索是多媒体、网络通信及计算机等应用研究领域的一项关键技术。该文提出了一种在分形压缩域直接进行基于内容的图像检索方法。该方法不需要对查询图像进行分形变换，因此可以提高检索速度，降低检索复杂度。仿真结果表明，使用该文提出的方法，能够有效地进行分形域基于内容的图像检索，比较大幅度地降低了检索时间，优于试验中其他3种方法。     

基于DCT压缩域的纹理图像分类

该文提出了一种在基于离散余弦变换(DCT,Discrete Cosine Transform)压缩域进行纹理图像分类的方法。此方法主要利用了DCT域能量具有方向性的特点,直接在游程长编码(RLE,Run LengthEncoding)形成的码流中根据这种方向性来提取、组织特征矢量,对纹理图像进行分类。此方法只需对压缩图像进行简单的Huffman解码,要处理的数据量较少,处理速度快。实验结果表明,这种分类方法具有较高的准确性。     

DCT域中MPEG7主色描述符的提取

该文在MPEG7的基础上提出了DCT域内直接提取主色描述符的新方法。这种方法节省了对图像的解压缩的过程,因而大大的提高了对于压缩图像进行特征提取的速度和效果。作为整个箅法的一部分,一种自动阈值提取的算法也在该文中给予了描述。这种方法可以减少因人为设定经验阈值而带来的不确定性,使算法更具鲁棒性。对比检索试验结果也说明本算法是一个高速有效的算法。新算法主要用于压缩图像库或互联网上的相似检索。     

基于重组DCT系数子带能量直方图的图像检索

现在许多图像采用JPEG格式存储,检索这些图像通常要先解压缩,然后提取基于像素域的特征矢量进行图像检索。己有文献提出直接在DCT域进行图像检索的方法,这样可以降低检索的时间复杂度。本文提出对JPEG图像的DCT系数利用多分辨率小波变换的形式进行重组,对整个数据库中所有图像的DCT系数重组得到的若干子带,分别建立子带能量直方图,而后采用Morton顺序建立每幅图像的索引,并采用变形B树结构组织图像数据库用于图像检索。     

基于内容的图像检索技术研究

在对海量的图像数据进行检索时,传统的基于数值/字符的信息检索技术并不能满足要求.因此,基于内容的图像检索技术(CBIR:Content-Based Image Retrieval)的研究应运而生,并引起了广泛关注.本文主要讨论CBIR研究中的一些关键问题:图像的内容特征及其提取、特征之间的相似度计算、查询条件的表达、检索性能的评价、压缩域的图像检索技术等等,并指出了一些可值得深入研究的方向.     

一种具有旋转不变性的压缩域纹理图像分类方法

该文提出了一种具有旋转不变性的、基于DCT压缩域的纹理图像分类方法。此方法主要是在DCT压缩域中合理组织频率系数,使其具有与小波分解相类似的图像多分辨率结构,由此设计了抗旋转能力强的纹理图像分类算法。这些操作直接在游程长编码(RLE)形成的码流上进行,处理的数据量较少,处理速度快。实验结果表明,这种分类方法具有较高的准确性。     

Fast Inverse Motion Compensation Algorithms for MPEG and for Partial DCT Information

In prior work, we developed a fast inverse motion compensation method that can be implemented directly on the DCT domain representation derived from the compressed bitstreams conforming to MPEG, H.261, and H.263 standards. That work was restricted to compressed-domain representations wherein the motion-vectors have integer pel accuracy. Here, we extend this work to fractional-pel accurate motion-vectors. We also extend the prior work to speed up the inverse motion compensation process in the DCT domain by explicitly exploiting the sparseness of the DCT domain representation. Using partial DCT information, we show that the DCT domain method has substantially lower operation count than the conventional spatial domain approach which requires decompression followed by inverse motion-compensation.     

Efficient Cloud Image Retrieval System Using Weighted-Inverted Index and Database Filtering Algorithms

With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scale image/video databases is the most important retrieval control target. In this paper, a cloud based content-based image retrieval (CBIR) scheme is presented. Database-categorizing based on weighted-inverted index (DCWⅡ) and database filtering algorithm (DFA) is used to speed up the features matching process. In the DCWⅡ, the weights are assigned to discrete cosine transform (DCT) coefficients histograms and the database is categorized by weighted features. In addition, the DFA filters out the irrelevant image in the database to reduce unnecessary computation loading for features matching. Experiments show that the proposed CBIR scheme outperforms previous work in the precision-recall performance and maintains mean average precision (mAP) about 0.678 in the large-scale database comprising one million images. Our scheme also can reduce about 50% to 85% retrieval time by pre-filtering the database, which helps to improve the efficiency of retrieval systems.     

A multiresolution approach for rotation invariant texture image retrieval with orthogonal polynomials model

In this paper, a simple and an efficient Content Based Image Retrieval which is based on orthogonal polynomials model is presented. This model is built with a set of carefully chosen orthogonal polynomials and is used to extract the low level texture features present in the image under analysis. The orthogonal polynomials model coefficients are reordered into multiresolution subband like structure. Simple statistical and perceptual properties are derived from the subband coefficients to represent the texture features and these features form a feature vector. The efficiency of the proposed feature vector extraction for texture image retrieval is experimented on the standard Brodatz and MIT’s VisTex texture database images with the Canberra distance measure. The proposed method is compared with other existing retrieval schemes such as Discrete Cosine Transformation (DCT) based multiresolution subbands, Gabor wavelet and Contourlet Transform based retrieval schemes and is found to outperform the existing schemes with less computational cost.     

Content-based image retrieval using H.264 intra coding features

Efficient multimedia retrieval has become a vital issue because more audio and video data are now available. This paper focuses on content-based image retrieval (CBIR) in the compression domain (CPD). The retrieval features are extracted based on I-frame coding information in H.264. This paper proposes using a local mode histogram as the texture feature to match images and applying the residual coefficients to filter non-confident modes. The geometrical correspondence between two images is also considered. The experimental results show that the proposed method can substantially reduce computational and memory resource consumption, and provides similar performance compared with methods that extract features from decompressed images.     

一种基于DCT域统计特征的JPEG图像隐写分析

提出了一种基于DCT域统计特征的JPEG图像隐写分析算法。该算法在分析JPEG图像的DCT域统计特性的基础上,提取了8维特征向量,通过LSSVM分类器对待测图像进行分类,以达到检测载密图像的目的。算法实现简单、计算复杂度低。实验结果表明,该算法检测速度快,具有较高的检测正确率,能够实现针对各类JPEG图像信息隐藏算法的有效检测。     

Joint image splicing detection in DCT and Contourlet transform domain

Splicing is a fundamental and popular image forgery method and image splicing detection is urgently called for digital image forensics recently. In this paper, a Markov based approach is proposed to detect image splicing. The paper applies the Markov model in the block discrete cosine transform (DCT) domain and the Contourlet transform domain. First, the original Markov features of the inter-block between block DCT coefficients are improved by considering the different frequency ranges of each block DCT coefficients. Then, additional features are extracted in Contourlet transform domain to characterize the dependency of positions among Contourlet subband coefficients. And these features are extracted from single color channel for gray image while extracted from three color channels for color image. Finally, Support Vector Machines (SVMs) are exploited to classify the authentic and spliced images for the gray image dataset while ensemble classifier to the color image dataset. The experiment results demonstrate that the proposed detection scheme outperforms some state-of-the-art methods when applied to Columbia Image Splicing Detection Evaluation Dataset (DVMM), and ranks fourth in phase 1 on the Live Ranking of the first Image Forensics Challenge.