Adaptive sampling for compressed sensing based image compression

The compressed sensing (CS) theory has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. In this paper, we focus on how to improve the sampling efficiency for CS-based image compression by using our proposed adaptive sampling mechanism on the block-based CS (BCS), especially the reweighted one. To achieve this goal, two solutions are developed at the sampling side and reconstruction side, respectively. The proposed sampling mechanism allocates the CS-measurements to image blocks according to the statistical information of each block so as to sample the image more efficiently. A generic allocation algorithm is developed to help assign CS-measurements and several allocation factors derived in the transform domain are used to control the overall allocation in both solutions. Experimental results demonstrate that our adaptive sampling scheme offers a very significant quality improvement as compared with traditional non-adaptive ones.     

Fast DCT domain filtering using the DCT and the DST

A method for efficient spatial domain filtering, directly in the discrete cosine transform (DCT) domain, is developed and proposed. It consists of using the discrete sine transform (DST) and the DCT for transform-domain processing on the in JPEG basis of the previously derived convolution-multiplication properties of discrete trigonometric transforms. The proposed scheme requires neither zero padding of the input data nor kernel symmetry. It is demonstrated that, in typical applications, the proposed algorithm is significantly more efficient than the conventional filtered spatial domain and earlier proposed DCT domain methods. The proposed method is applicable to any DCT-based image compression standard, such as JPEG, MPEG, and H.261.     

Image coding using lapped biorthogonal transform

The wireless sensor network utilizes image compression algorithms like JPEG, JPEG2000, and SPIHT for image transmission with high coding efficiency. During compression, discrete cosine transform (DCT)–based JPEG has blocking artifacts at low bit-rates. But this effect is reduced by discrete wavelet transform (DWT)–based JPEG2000 and SPIHT algorithm but it possess high computational complexity. This paper proposes an efficient lapped biorthogonal transform (LBT)–based low-complexity zerotree codec (LZC), an entropy coder for image coding algorithm to achieve high compression. The LBT-LZC algorithm yields high compression, better visual quality with low computational complexity. The performance of the proposed method is compared with other popular coding schemes based on LBT, DCT and wavelet transforms. The simulation results reveal that the proposed algorithm reduces the blocking artifacts and achieves high compression. Besides, it is analyzed for noise resilience.     

Image representation by compressive sensing for visual sensor networks

This paper addresses the image representation problem in visual sensor networks. We propose a new image representation method for visual sensor networks based on compressive sensing (CS). CS is a new sampling method for sparse signals, which is able to compress the input data in the sampling process. Combining both signal sampling and data compression, CS is more capable of image representation for reducing the computation complexity in image/video encoder in visual sensor networks where computation resource is extremely limited. Since CS is more efficient for sparse signals, in our scheme, the input image is firstly decomposed into two components, i.e., dense and sparse components; then the dense component is encoded by the traditional approach (JPEG or JPEG 2000) while the sparse component is encoded by a CS technique. In order to improve the rate distortion performance, we leverage the strong correlation between dense and sparse components by using a piecewise autoregressive model to construct a prediction of the sparse component from the corresponding dense component. Given the measurements and the prediction of the sparse component as initial guess, we use projection onto convex set (POCS) to reconstruct the sparse component. Our method considerably reduces the number of random measurements needed for CS reconstruction and the decoding computational complexity, compared to the existing CS methods. In addition, our experimental results show that our method may achieves up to 2 dB gain in PSNR over the existing CS based schemes, for the same number of measurements.     

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。     

Image content analysis for sector-wise JPEG fragment classification

In this paper, we propose a sector-wise JPEG fragment classification approach to classify normal and erroneous JPEG data fragments with the minimum size of 512 bytes per fragment. Our method is based on processing each read-in sector of 512 bytes with using the DCT coefficient analysis methods for extracting the features of visual inconsistencies. The classification is conducted before the inverse DCT and can be performed simultaneously with JPEG decoding. The contributions of this work are two-folds: (1) a sector-wise JPEG erroneous fragment classification approach is proposed (2) new DCT coefficient analysis methods are introduced for image content analysis. Testing results on a variety of erroneous fragmented and normal JPEG files prove the strength of this operator for the purpose of forensics analysis, data recovery and abnormal fragment inconsistencies classification and detection. Furthermore, the results also show that the proposed DCT coefficient analysis methods are efficient and practical in terms of classification accuracy. In our experiment, the proposed approach yields a false positive rate of 0.32% and a true positive rate of 96.1% in terms of erroneous JPEG fragment classification.     

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.     

遥感图像自适应分层量化的快速DCT压缩法

依据遥感图像的频谱特性,提出一种自适应分层量化的快速DCT图像压缩算法,在对原始图像快速DCT之后,根据图像频谱特性自适应修正JPEG量化表,再用新量化表分层量化DCT系数。真实遥感图像压缩实验表明,在同等压缩比下,提出的方法比标准JPEG方法速度快,且峰值信噪比增加1～2dB,并能实现嵌入式码流图像压缩。     

Dictionary based surveillance image compression

Common image compression techniques suitable for general purpose may be less effective for such specific applications as video surveillance. Since a stationed surveillance camera always targets at a fixed scene, its captured images exhibit high consistency in content or structure. In this paper, we propose a surveillance image compression technique via dictionary learning to fully exploit the constant characteristics of a target scene. This method transforms images over sparsely tailored over-complete dictionaries learned directly from image samples rather than a fixed one, and thus can approximate an image with fewer coefficients. A set of dictionaries trained off-line is applied for sparse representation. An adaptive image blocking method is developed so that the encoder can represent an image in a texture-aware way. Experimental results show that the proposed algorithm significantly outperforms JPEG and JPEG 2000 in terms of both quality of reconstructed images and compression ratio as well.     

JPEG encryption for image rescaling in the encrypted domain

This work proposes a novel protocol of encrypting the JPEG image suitable for image rescaling in the encrypted domain. To protect the privacy of original content, the image owner perturbs the texture and randomizes the structure of the JPEG image by enciphering the quantized Discrete Cosine Transform (DCT) coefficients. After receiving the encrypted JPEG image, the service provider generates a rescaled JPEG image by down-sampling the encrypted DCT coefficients. On the recipient side, the encrypted JPEG image rescaled by the service provider can be decrypted to a plaintext image with a lower resolution with the aid of encryption keys. Experimental results show that the proposed method has a good capability of rescaling the privacy-protected JPEG file.     

Robust digital signature for multimedia authentication

Image authentication verifies the originality of an image by detecting malicious manipulations. This goal is different from that of image watermarking which embeds into the image a signature surviving most manipulations. Most existing methods for image authentication treat all types of manipulation equally (i.e., as unacceptable). However, some applications demand techniques that can distinguish acceptable manipulations (e.g., compression) from malicious ones. In this paper, we describe an effective technique for image authentication, which can prevent malicious manipulations but allow JPEG lossy compression. The authentication signature is based on the invariance of the relationship between the DCT coefficients at the same position in separate blocks of an image. This relationship will be preserved when these coefficients are quantized in a JPEG compression process. Our proposed method can distinguish malicious manipulations from JPEG lossy compression regardless of how high the compression ratio is. We also show that, in different practical cases, the design of the authenticator depends on the number of recompression times, and whether the image is decoded into integral values in the pixel domain during the recompression process. Theoretical and experimental results indicate that this technique is effective for image authentication.     

基于距离度量学习的DCT域JPEG图像检索

由于特征有限,传统基于欧式距离的压缩域检索性能并不理想。本文引入距离度量学习技术,研究压缩域图像检索,提出了一种基于距离度量学习的离散余弦变换（DCT）域联合图像专家小组（JPEG）图像检索方法。首先,提出了一种更有效的 DCT 域特征提取方法;其次,运用距离度量学习技术训练出一个更加有效的度量矩阵进行检索。在 Corel5000上的图像检索实验表明,新方法有效提高了检索准确度。     

A novel blind robust image watermarking in DCT domain using inter-block coefficient correlation

This paper presents a novel blind watermarking algorithm in DCT domain using the correlation between two DCT coefficients of adjacent blocks in the same position. One DCT coefficient of each block is modified to bring the difference from the adjacent block coefficient in a specified range. The value used to modify the coefficient is obtained by finding difference between DC and median of a few low frequency AC coefficients and the result is normalized by DC coefficient. The proposed watermarking algorithm is tested for different attacks. It shows very good robustness under JPEG image compression as compared to existing one and also good quality of watermark is extracted by performing other common image processing operations like cropping, rotation, brightening, sharpening, contrast enhancement etc.     

一种新的静止图象压缩编码算法

本文提出了一种新的静止图象压缩编码算法,即VQ+DPCM+DCT算法,并与JPEG标准的基本系统进行了比较。实验结果表明,新算法的压缩比有较大提高。     

基于DCT变换的内嵌静止图像压缩算法

提出了一种有效的基于离散余弦变换(DCT)的内嵌子带图像编码算法.Xiong等人提出的EZDCT算法采用零树结构实现了一种内嵌DCT编码器,且其性能优于JPEG.本文指出DCT的零树结构在内嵌DCT算法中并非很有效,同时提出了一种不依赖零树结构的简便、高效的内嵌DCT子带编码算法.实验结果表明本文算的压缩性能(PSNR)比EZDCT高约0.5~1.5dB,且接近当前最通用的内嵌小波SPIHT算法,在对某些图像压缩时还优于SPIHT算法.     

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.     

A Gaussian derivative based version of JPEG for image compressionand decompression

The compression and decompression of continuous-tone images is important in document management and transmission systems. This paper considers an alternative image representation scheme, based on Gaussian derivatives, to the standard discrete cosine transformation (DCT), within a Joint Photographic Experts Group (JPEG) framework. Depending on the computer arithmetic hardware used, the approach developed might yield a compression/decompression technique twice as fast as the DCT and of (essentially) equal quality.     

一种基于人眼对比度敏感视觉特性的图像自适应量化方法

为了提高图像的压缩比和压缩质量,结合人眼对比度敏感视觉特性和图像变换域频谱特征,该文提出一种自适应量化表的构建方法。并将该表代替JPEG中的量化表,且按照JPEG的编码算法对3幅不同的彩色图像进行了压缩仿真实验验证,同时与JPEG压缩作对比分析。实验结果表明:与JPEG压缩方法相比,在相同的压缩比下,采用自适应量化压缩后,3幅解压彩色图像的SSIM和PSNR值分别平均提高了1.67%和4.96%。表明该文提出的结合人眼视觉特性的自适应量化是一种较好的、有实用价值的量化方法。     

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FPGA的高分辨率图像DCT域增强

为了提高高分辨率图像的质量,实现快速的图像增强算法,提出在离散余弦变换（DCT）的对比度测度下,通过DCT矩阵中不同频率的系数关系对DCT系数块进行分类,对不同类型的系数块做不同强度的自适应增强算法,并在FPGA上得到实现。提出的方法在不影响原始图像压缩性能的情况下有效地增强了图像明亮或黑暗区域的细节,同时减少了因图像增强而带来的压缩图像块效应。给出算法原理及在FPGA上的具体实现方法,并给出了实验结果。结果表明,该算法在改善图像主、客观质量方面和运算效率上都能够达到较好的效果。