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。     

Saliency detection in the compressed domain for adaptive image retargeting

Saliency detection plays important roles in many image processing applications, such as regions of interest extraction and image resizing. Existing saliency detection models are built in the uncompressed domain. Since most images over Internet are typically stored in the compressed domain such as joint photographic experts group (JPEG), we propose a novel saliency detection model in the compressed domain in this paper. The intensity, color, and texture features of the image are extracted from discrete cosine transform (DCT) coefficients in the JPEG bit-stream. Saliency value of each DCT block is obtained based on the Hausdorff distance calculation and feature map fusion. Based on the proposed saliency detection model, we further design an adaptive image retargeting algorithm in the compressed domain. The proposed image retargeting algorithm utilizes multioperator operation comprised of the block-based seam carving and the image scaling to resize images. A new definition of texture homogeneity is given to determine the amount of removal block-based seams. Thanks to the directly derived accurate saliency information from the compressed domain, the proposed image retargeting algorithm effectively preserves the visually important regions for images, efficiently removes the less crucial regions, and therefore significantly outperforms the relevant state-of-the-art algorithms, as demonstrated with the in-depth analysis in the extensive experiments.     

The spatial relationship of DCT coefficients between a block andits sub-blocks

At present, almost all digital images are stored and transferred in their compressed format in which discrete cosine transform (DCT)-based compression remains one of the most important data compression techniques due to the efforts from JPEG. In order to save the computation and memory cost, it is desirable to have image processing operations such as feature extraction, image indexing, and pattern classifications implemented directly in the DCT domain. To this end, we present in this paper a generalized analysis of spatial relationships between the DCTs of any block and its sub-blocks. The results reveal that DCT coefficients of any block can be directly obtained from the DCT coefficients of its sub-blocks and that the interblock relationship remains linear. It is useful in extracting global features in the compressed domain for general image processing tasks such as those widely used in pyramid algorithms and image indexing. In addition, due to the fact that the corresponding coefficient matrix of the linear combination is sparse, the computational complexity of the proposed algorithms is significantly lower than that of the existing methods     

L/M-fold image resizing in block-DCT domain using symmetric convolution

Image resizing is to change an image size by upsampling or downsampling of a digital image. Most still images and video frames on digital media are given in a compressed domain. Image resizing of a compressed image can be performed in the spatial domain via decompression and recompression. In general, resizing of a compressed image in a compressed domain is much faster than that in the spatial domain. We propose a novel approach to resize images with L/M resizing ratio in the discrete cosine transform (DCT) domain, which exploits the multiplication-convolution property of DCT (multiplication in the spatial domain corresponds to symmetric convolution in the DCT domain). When an image is given in terms of its 8/spl times/8 block-DCT coefficients, its resized image is also obtained in 8/spl times/8 block-DCT coefficients. The proposed approach is computationally fast and produces visually fine images with high PSNR.     

No-reference quality assessment for DCT-based compressed image

A blind/no-reference (NR) method is proposed in this paper for image quality assessment (IQA) of the images compressed in discrete cosine transform (DCT) domain. When an image is measured by structural similarity (SSIM), two variances, i.e. mean intensity and variance of the image, are used as features. However, the parameters of original copies are actually unavailable in NR applications; hence SSIM is not widely applicable. To extend SSIM in general cases, we apply Gaussian model to fit quantization noise in spatial domain, and directly estimate noise distribution from the compressed version. Benefit from this rearrangement, the revised SSIM does not require original image as the reference. Heavy compression always results in some zero-value DCT coefficients, which need to be compensated for more accurate parameter estimate. By studying the quantization process, a machine-learning based algorithm is proposed to estimate quantization noise taking image content into consideration. Compared with state-of-the-art algorithms, the proposed IQA is more heuristic and efficient. With some experimental results, we verify that the proposed algorithm (provided no reference image) achieves comparable efficacy to some full reference (FR) methods (provided the reference image), such as SSIM.     

Video saliency detection incorporating temporal information in compressed domain

Saliency detection is widely used to pick out relevant parts of a scene as visual attention regions for various image/video applications. Since video is increasingly being captured, moved and stored in compressed form, there is a need for detecting video saliency directly in compressed domain. In this study, a compressed video saliency detection algorithm is proposed based on discrete cosine transformation (DCT) coefficients and motion information within a visual window. Firstly, DCT coefficients and motion information are extracted from H.264 video bitstream without full decoding. Due to a high quantization parameter setting in encoder, skip/intra is easily chosen as the best prediction mode, resulting in a large number of blocks with zero motion vector and no residual existing in video bitstream. To address these problems, the motion vectors of skip/intra coded blocks are calculated by interpolating its surroundings. In addition, a visual window is constructed to enhance the contrast of features and to avoid being affected by encoder. Secondly, after spatial and temporal saliency maps being generated by the normalized entropy, a motion importance factor is imposed to refine the temporal saliency map. Finally, a variance-like fusion method is proposed to dynamically combine these maps to yield the final video saliency map. Experimental results show that the proposed approach significantly outperforms other state-of-the-art video saliency detection models.     

Image/video spatial scalability in compressed domain

Image/video compression is widely used in various applications with the advent of the Joint Photographic Expert Group (JPEG), Motion Picture Expert Group (MPEG), and H.261 standards. Hence, most visual data are stored in the compressed format. Spatial scalable encoding of visual data has several applications, including browsing visual databases, querying multimedia databases, interactive multimedia communications, etc. We propose two novel techniques, namely, format compatible (FC) discrete cosine transform (DCT) and format modified (FM) DCT to implement image/video spatial scalability directly in the DCT compressed domain. The FC-DCT technique can be used to manipulate the standard bit streams, such as JPEG, MPEG, etc., while the FM-DCT technique can be employed in a variety of applications which require fast processing. In contrast to the traditional spatial-domain techniques, the compressed-domain techniques remove the unnecessary decompression and recompression procedures, thus, they have the advantages of reduced computational complexity and storage requirements. Simulation results demonstrate that the proposed DCT-domain techniques can achieve a comparable performance at a much lower computational complexity compared to the spatial-domain techniques     

Arbitrary-ratio image resizing using fast DCT of composite length for DCT-based transcoder.

We propose a fast arbitrary-ratio image resizing method for transcoding of the compressed images. The downsizing process in the discrete cosine transform (DCT) domain can be implemented by truncating high-frequency coefficients, whereas the upsizing process is implemented in the DCT domain by padding zero coefficients to the high-frequency part. The proposed method combines a fast inverse and forward DCT of composite length for arbitrary-ratio upsizing or downsizing. According to the resizing ratio, truncating the high-frequency coefficients and padding zeros are appropriately considered by combining the inverse DCT and forward DCT. The proposed method shows a good peak signal-to-noise ratio and less computational complexity compared with the spatial-domain and previous DCT-domain image resizing methods.     

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.     

基于独立分量分析的压缩域彩色图像融合

提出了一种基于独立分量分析的压缩域彩色图像融合方法,其基本思想是将源彩色图像进行IHS变换,将两幅图像的亮度分量/进行小波分解,对分解后的高频系数采用由独立分量分析得出的变换矩阵进行变换,然后对变换系数采用局部方差法进行融合,而对小波分解后的低频子图像分成若干N×N的图像块,分别对每个图像块做二维DCT变换,最后对融合的系数进行反变换得到融合结果,并用客观评价标准对结果进行了定量的分析.实验结果表明:该方法在提高空间信息的基础上,较少地降低了颜色失真.     

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。     

Image postprocessing by Non-local Kuan’s filter

Blocking artifacts exist in images and video sequences compressed to low bit rates using block-based discrete cosine transform (DCT) compression standards. In order to reduce blocking artifacts, two image postprocessing techniques, DNLK filter and OCDNLK filter, are presented in this paper. A more accurate DCT domain Kuan’s filter based on Non-local parameter estimation was proposed from the linear minimum mean-square-error (MMSE) criterion. We analyze the required two assumptions for the filter theoretically. Then the DCT domain Kuan’s filter for low frequency coefficients and Non-local mean filter for high frequency AC coefficients constitute the proposed Non-local Kuan’s (NLK) filter. After that, we propose the Dual Non-local Kuan’s (DNLK) filter by applying the proposed filter in dual layer. The DNLK filter is extended to form the Overcomplete Dual Non-local Kuan’s (OCDNLK) filter by applying to the overcomplete DCT coefficients. Experimental results on coded images using test quantization tables and JPEG coded images show the effectiveness of the two methods.     

Manipulation and compositing of MC-DCT compressed video

Many advanced video applications require manipulations of compressed video signals. Popular video manipulation functions include overlap (opaque or semitransparent), translation, scaling, linear filtering, rotation, and pixel multiplication. We propose algorithms to manipulate compressed video in the compressed domain. Specifically, we focus on compression algorithms using the discrete cosine transform (DCT) with or without motion compensation (MC). Such compression systems include JPEG, motion JPEG, MPEG, and H.261. We derive a complete set of algorithms for all aforementioned manipulation functions in the transform domain, in which video signals are represented by quantized transform coefficients. Due to a much lower data rate and the elimination of decompression/compression conversion, the transform-domain approach has great potential in reducing the computational complexity. The actual computational speedup depends on the specific manipulation functions and the compression characteristics of the input video, such as the compression rate and the nonzero motion vector percentage. The proposed techniques can be applied to general orthogonal transforms, such as the discrete trigonometric transform. For compression systems incorporating MC (such as MPEG), we propose a new decoding algorithm to reconstruct the video in the transform domain and then perform the desired manipulations in the transform domain. The same technique can be applied to efficient video transcoding (e.g., from MPEG to JPEG) with minimal decoding     

基于灰度预测和灰色关联分析的离散余弦变化图像隐藏算法

Traditional information hiding algorithms cannot maintain a good balance of capacity, invisibility and robustness. In this paper, a novel blind colour image information hiding algorithm based on grey prediction and grey relational analysis in the Discrete Cosine Tran-sform (DCT) domain is proposed. First, this algorithm compresses the secret image losslessly based on the improved grey predic-tion GM(1,1) (IGM) model. It then chooses the blocks of rich texture in the cover image as the embedding regions using Double-dimension Grey Relational Analysis (DGRA). Finally, it adaptively embeds the compressed secret bits stream into the DCT domain mid-frequency coefficients, which are decided by those blocks’ Double-Dimension Grey Correlation Degree (DGCD) and Human Visual System (HVS). This method can ensure an adequate balance between invisibility, capacity and robustness. Experimental results show that the proposed algorithm is robust against JPEG compression (46.724 6 dB when the compression quality factor is 90%), Gaussian noise (45.531 3 dB when the parameter is (0,0.000 5)) etc., and it is a blind information hiding algorithm that can be extracted without an original carrier.     

A novel deep learning framework for double JPEG compression detection of small size blocks

Double JPEG compression detection plays a vital role in multimedia forensics, to find out whether a JPEG image is authentic or manipulated. However, it still remains to be a challenging task in the case when the quality factor of the first compression is much higher than that of the second compression, as well as in the case when the targeted image blocks are quite small. In this work, we present a novel end-to-end deep learning framework taking raw DCT coefficients as input to distinguish between single and double compressed images, which performs superior in the above two cases. Our proposed framework can be divided into two stages. In the first stage, we adopt an auxiliary DCT layer with sixty-four 8 × 8 DCT kernels. Using a specific layer to extract DCT coefficients instead of extracting them directly from JPEG bitstream allows our proposed framework to work even if the double compressed images are stored in spatial domain, e.g. in PGM, TIFF or other bitmap formats. The second stage is a deep neural network with multiple convolutional blocks to extract more effective features. We have conducted extensive experiments on three different image datasets. The experimental results demonstrate the superiority of our framework when compared with other state-of-the-art double JPEG compression detection methods either hand-crafted or learned using deep networks in the literature, especially in the two cases mentioned above. Furthermore, our proposed framework can detect triple and even multiple JPEG compressed images, which is scarce in the literature as far as we know.     

一种新的小波域彩色图像水印算法

针对数字产品的版权保护问题,以离散余弦变换（DCT）和奇异值分解（SVD）为基础,提出了一种新的小波域彩色图像数字水印算法。该算法主要是将水印图像的DCT系数以新的方法嵌入到载体图像中低频小波系数分块奇异值分解的奇异值中。实验结果表明了算法的可行性和有效性,并对加噪、滤波、JPEG压缩、剪切等常见的攻击有良好的鲁棒性,具有较好的实用价值。     

基于DCT变换的彩色图像置乱数字水印算法

介绍了离散余弦变换的系数特征,提出了一种基于离散余弦变换的彩色图像置乱数字水印算法。算法是将数字水印置乱加密后微小的扰动原始彩色图像经离散余弦变换后的对应系数,从而达到数字水印嵌入的目的。仿真实验表明算法简单高效,原图像与嵌入水印后图像差异小,水印提取准确,能较好的保证数字水印不可感知性,在对嵌入水印图像进行各种加噪、裁剪、旋转、锐化等处理后,水印仍有较强的鲁棒性。     

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。     

基于压缩传感的分块DCT域灰度图像水印算法

文中研究了压缩传感方法和图像水印算法,提出了一种基于压缩传感的分块余弦变换域灰度图像水印算法。该算法首先将原始二值水印图像采用压缩传感方法对其观测,从而得到观测矩阵,并将观测矩阵进行arnold置乱(阿诺德置乱)后生成待嵌入的水印图像;在嵌入水印阶段,首先将载体灰度图像进行了分块,分块的大小取决于水印图像的大小,然后将每个小分块进行二维余弦变换,计算其每个小分块中心像素的邻居(上下左右)像素均值,比较均值与对应水印像素位大小,以确定其嵌入数据,从而实现水印嵌入。为验证此算法的鲁棒性,我们进行了实验,实验表明该算法能承受一定的裁剪攻击。     

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.