Interblock Correlation Research Articles

Parallel multiple watermarking using adaptive Inter-Block correlation

People pay more and more attention to copyright protection and digital watermarking technology is a reliable method for copyright identification. In this paper, we propose the parallel multiple watermarking method using adaptive inter-block correlation. Considering the image texture characteristics, the texture complexity method is designed, based on which, the Circular-shaped Adjacent Block Selection and the Arch-shaped Adjacent Block Selection will be adaptively selected for adjacent block selection. The multiple watermarks are alternately embedded into the low and middle frequency band in DCT domain of the image blocks to good imperceptibility and high robustness. By adjusting the difference of projection of embedding coefficients and reference coefficients on the spreading vector, the multiple watermarks can be embedded, so the embedding capacity increases. In addition, we improve the multiple watermarks embedding based on orthogonal spreading vectors so that it can be adapted to embedding multiple watermarks in parallel based on inter-block correlation, saving embedding and extraction time. We theoretically analyze the rationality of parallel orthogonal embedding of multiple watermarks and experimentally verify the high efficiency of parallelism. Cover images of different texture complexity are included in the test dataset, and various attacks have been simulated in the experiments. Experimental results on imperceptibility, robustness, time cost and watermark capacity show the satisfied performance of the proposed scheme.

Multi-Channel Deep Networks for Block-Based Image Compressive Sensing

Incorporating deep neural networks in image compressive sensing (CS) receives intensive attentions in multimedia technology and applications recently. As deep network approaches learn the inverse mapping directly from the CS measurements, the reconstruction speed is significantly faster than the conventional CS algorithms. However, for existing network based approaches, a CS sampling procedure has to map a separate network model. This may potentially degrade the performance of image CS with block-wise sampling because of blocking artifacts, especially when multiple sampling rates are assigned to different blocks within an image. In this paper, we develop a multichannel deep network for block-based image CS by exploiting inter-block correlation with performance significantly exceeding the current state-of-the-art methods. The significant performance improvement is attributed to block-wise approximation but full image removal of blocking artifacts. Specifically, with our multichannel structure, the image blocks with a variety of sampling rates can be reconstructed in a single model. The initially reconstructed blocks are then capable of being reassembled into a full image to improve the recovered images by unrolling a hand-designed block based CS recovery algorithm. Experimental results demonstrate that the proposed method outperforms the state-of-the-art CS methods by a large margin in terms of objective metrics and subjective visual image quality. Our source codes are available at https://github.com/siwangzhou/DeepBCS.

A JPEG blocking artifact detector for image forensics

Detection of JPEG blocking artifacts in an image plays an important role in the forensic analysis. JPEG anti-forensic techniques try to remove these artifacts to disguise forensic detectors. This paper proposes a new technique for detecting JPEG blocking artifacts. Unlike in uncompressed images, when a JPEG compressed image is cropped, there is a change in the inter-block correlation of DCT coefficients due to the shifting of blocking artifacts. We propose to use this change in correlation to detect the blockiness in an image. Experiments conducted on a large set of images show that the proposed detector outperforms the existing detectors in detecting blocking artifacts in anti-forensically modified images.

Image splicing detection based on Markov features in QDCT domain

Image splicing is very common and fundamental in image tampering. Therefore, image splicing detection has attracted more and more attention recently in digital forensics. Gray images are used directly, or color images are converted to gray images before be processed in previous image splicing detection algorithms. However, most forgery images are color images. In order to make use of the color information in images, a classification algorithm is put forward which can use color images directly. In this paper, an algorithm based on Markov in quaternion discrete cosine transform (QDCT) domain is proposed for image splicing detection. First of all, color information is extracted from blocked images to construct quaternion in a whole manner, and the QDCT coefficients of quaternion blocked images can be obtained. Secondly, the expanded Markov features generated from the transition probability matrices in QDCT domain can not only capture the intra-block, but also the inter-block correlation between block QDCT coefficients. Finally, support vector machine (SVM) is exploited to classify the Markov feature vector. The experiment results demonstrate that the proposed algorithm not only make use of color information of images, but also can yield considerably better detection performance compared with the state-of-the-art splicing detection methods tested on the same dataset.

Joint-channel modeling to attack QIM steganography

This paper presents a statistical steganalysis framework to attack quantization index modulation (QIM) based steganography. The quantization process is generally modeled as an additive noise channel. The proposed method exploits the fact that plain-quantization (quantization without message embedding) decreases local-randomness (or increases local-correlation) in the resulting quantized image. Moreover, QIM-stego image exhibits relatively higher level of local-randomness than the corresponding quantized-cover, though both are obtained using same set of parameters. The local-randomness (or inter-block correlation) of the test-image is used to capture traces left behind by quantization. A parametric model is developed to characterize channel-dependent local-randomness. Maximum likelihood estimation (MLE) framework is used to estimate parameters of the distribution of local-randomness mask. Distributions of parameters, estimated from the quantized-cover and the QIM-stego images, are used to characterize quantization with and without message embedding. To investigate variations in the estimated parameters as a function of frequency, inter- (variations within each channel(or subband)) and intra-channel (across all channels), joint-channel modeling and single-channel modeling, respectively, is considered. For each approach, a set of parametric detectors based on generalized likelihood ratio test (GLRT) is used to distinguish between the cover- and the stego-images. To improve detection accuracy, decisions from both detectors are fused to generate the final stego-detection decision. Effectiveness of the proposed framework is evaluated using a dataset consisting of over 35000 test-images obtained from 880 uncompressed natural images. Experimental results show that the proposed scheme can successfully detect QIM-stego images with very low false rates. In addition, performance comparison with existing state-of-the-art also shows that the proposed method performs significantly superior than the selected methods.

Post-Processing for Blocking Artifact Reduction Based on Inter-Block Correlation

Block-based coding introduces an undesirable discontinuity between neighboring blocks in reconstructed images. This image degradation, referred to as blocking artifacts, arises mainly due to the loss of inter-block correlation in the quantization process of discrete cosine transform coefficients. In many multimedia broadcasting applications, such as a television, decoded video sequences suffer from blocking artifacts. In this paper, we present a novel post-processing algorithm based on increment of inter-block correlation aimed at reducing blocking artifacts. We first smooth the three lowest frequency discrete cosine transform (DCT) coefficients between neighboring blocks, in order to reduce blocking artifacts in the flat region, which are most sensitive to the human visual system. We then group each edge block and its matched blocks together and apply group-based filtering to increase the correlation between grouped blocks. This suppresses blocking artifacts in the edge region while preserving details. In addition, the algorithm is extended to reduce flickering artifacts as well as blocking artifacts in video sequences. Experimental results show that the proposed method successfully alleviates blocking artifacts in both images and videos coded with low bit-rates.

A new arithmetic coding model for a block-based lossless image compression based on exploiting inter-block correlation

In this paper, we investigate a new approach for a block-based lossless image compression using arithmetic coding. The conventional arithmetic encoders encode and decode images pixel by pixel in raster scan order by using a statistical model which provides probabilities for the whole source symbols to be encoded. However, in the proposed scheme, the arithmetic encoders encode an image block by block from left to right, and block-row by block-row from top to bottom. The proposed model estimates the probability distribution of each block by exploiting the high correlation between neighboring image blocks. Therefore, the probability distribution of each block of pixels is estimated by minimizing the Kullback–Leibler distance between the exact probability distribution of that block and the probability distributions of its neighboring blocks in causal order. The results of comparative experiments show significant improvements over conventional arithmetic encoders in both static and adaptive order-0 models, reducing the bitrate by an average of 15.5 and 16.4 % respectively.

Digital image splicing detection based on Markov features in DCT and DWT domain

Image splicing is very common and fundamental in image tampering. To recover people's trust in digital images, the detection of image splicing is in great need. In this paper, a Markov based approach is proposed to detect this specific artifact. Firstly, the original Markov features generated from the transition probability matrices in DCT domain by Shi et al. is expanded to capture not only the intra-block but also the inter-block correlation between block DCT coefficients. Then, more features are constructed in DWT domain to characterize the three kinds of dependency among wavelet coefficients across positions, scales and orientations. After that, feature selection method SVM-RFE is used to fulfill the task of feature reduction, making the computational cost more manageable. Finally, support vector machine (SVM) is exploited to classify the authentic and spliced images using the final dimensionality-reduced feature vector. The experiment results demonstrate that the proposed approach can outperform some state-of-the-art methods.

비트스트림의 구조 분석을 이용한 음성 부호화 방식 추정 기법

This paper addresses a blind estimation and classification algorithm of the speech compression methods by using analysis on the structure of compressed bitstreams. Various speech compression methods including vocoders are developed in order to transmit or store the speech signals at very low bitrates. As a key feature, the vocoders contain the block structure inevitably. In classification of each compression method, we use the Measure of Inter-Block Correlation (MIBC) to check whether the bitstream includes the block structure or not, and to estimate the block length. Moreover, for the compression methods with the same block length, the proposed algorithm estimates the corresponding compression method correctly by using that each compression method has different correlation characteristics in each bit location. Experimental results indicate that the proposed algorithm classifies the speech compression methods robustly for various types and lengths of speech signals in noisy environment.

A fast near-optimum block truncation coding method using a truncated K-means algorithm and inter-block correlation

AbstractBlock truncation coding (BTC) is an efficient image compression method which finds application in diverse fields. The basic problem can be viewed as a two-level quantization process. However, efficient ways for optimal threshold determination have not been discovered so far. We propose a fast BTC algorithm based on a truncated K-means algorithm, utilizing the image inter-block correlation and the fact that K-means algorithm converges very fast in this case. This produces near-optimum solution with significantly improved speed over other methods. Simulation results confirm such advantages.

JPEG image steganalysis using joint discrete cosine transform domain features

A JPEG image steganalysis scheme based on joint dis- crete cosine transform (DCT) domain features is proposed. Intrinsic characteristics of DCT coefficients, such as histogram, intrablock correlation, and interblock correlation, are exploited to construct three feature sets. Support vector machine is utilized to learn and discriminate the difference of features between cover and stego im- ages. First, the three feature sets are investigated separately to re- veal their individual capability of attacking steganographic methods. Second, the feature sets are combined to form a joint feature set with better performance. Experimental results demonstrate that all three feature sets individually succeed in attacking the four typical steganographic tools to some extent, with the intrablock feature set performing the best. Furthermore, the comparison experiments show that the jointed feature set not only outperforms the three in- dividual feature sets but also proves to be better than a previous state-of-the-art steganalysis method. © 2010 SPIE and

A SVD Based Scheme For Post Processing of DCT Coded Images

In block discrete cosine transform (DCT) based image compression, the blocking artifacts are the main cause of degradation, especially at higher compression ratio. In proposed scheme, monotone or edge blocks are identified by examining the DCT coefficients of the block itself. In the first algorithm of the proposed scheme, a signal adaptive filter is applied to sub-image constructed by the DC components of DCT coded image to exploit the residual inter-block correlation between adjacent blocks. To further reduce artificial discontinuities due to blocking artifacts, the blocky image is re-divided into blocks in such a way that the corner of the original blocks comes at the center of the new blocks. These discontinuities causes the high frequency components in the new blocks. In this paper, these high frequency components due to blocking artifacts in monotone area are eliminated using singular value decomposition (SVD) based filtering algorithm. It is well known that the random noise is hard to compress, whereas it is easy to compress the ordered information. Thus, lossy compression of noisy signal provides the required filtering of the signal itself.

Programmable Deblocking Filter Architecture for a VC-1 Video Decoder

Although the current standards (MPEG1, MPEG2, MPEG4, H.261, H.263, and H.264/MPEG4 AVC) as well as the recent VC-1, present the same basic functional elements, i.e., prediction, transformation, quantization, and entropy encoding, important changes occur in the details of each functional element. One of the details concerns the elimination of the loss in interblock correlation due to block-based prediction, transformation, and quantization. In order to overcome the loss in blocking artifacts, a deblocking filtering method is necessary to maximize coding performance and consequently improve image quality. This letter describes a programmable VC-1 deblocking filter architecture with capabilities to support different standards. The architecture has been modeled, simulated, and implemented at the register transfer level. Results show a threefold performance improvement as compared to solutions where filtering algorithms are otherwise not hardwired. Results also point to parallelism based on existing data flow, and show that real-time requirements can be met.

Ldpc codes for non-coherent block fading channels with correlation: analysis and design

LDPC codes are analyzed and optimized for a noncoherent block fading channel in which there is correlation between the blocks. This extends related work in the literature which considered independent blocks. Low-complexity estimation and estimation-decoding algorithms based on Kalman smoothing are developed, and density evolution is used to analyze and optimize the resulting code. Results indicate that significant gains can be realized by taking inter-block correlation into account, compared to architectures in which the blocks are assumed to be independent.

Content-based image retrieval through compressed indices based on vector quantized images

A multimedia database system should deal efficiently with both image compression and retrieval functions. It is critical to develop image indexing techniques that search databases based on their content in a compressed domain. We propose a new scheme, query by index image , based on vector quantization, to facilitate image retrieval in a compressed domain. The proposed algorithm exploits different index images obtained by sorting codevectors to capture various kinds of image feature. Hence, intrablock correlation and interblock correlation in an image can be efficiently represented. Our proposed algorithm not only can extract features from the pixel domain but also from a transform domain, such as that of wavelet coefficients. Experimental results demonstrate that the retrieval performance of the proposed scheme is more accurate than that of other similar methods.

Recovery of image blocks using the method of alternating projections

A technique for block-loss restoration in block-based image and video coding, dubbed recovery of image blocks using the method of alternating projections (RIBMAP), is developed. The algorithm is based on orthogonal projections onto constraint sets in a Hilbert space. For the recovery of a linear dimension N size block, a total of 8N vectors are extracted from the surrounding area of an N x N missing block. These vectors form a library from which the best matching spatial information for the missing block is extracted. Recovery vectors, including both undamaged and restored damaged pixels, are introduced. The vectors are used to find highly correlated information relating to the lost pixels. To assure continuity with the surrounding undamaged area, three additional convex constraints are formulated. Adherance to these sets is imposed using alternating projections. Simulation results using orthogonal projections demonstrate that RIBMAP recovers spatial structure faithfully. Simulation comparisons with other procedures are presented: Ancis and Giusto's hybrid edge-based average-median interpolation technique, Sun and Kwok's projections onto convex sets-based method, Hemami and Meng's interblock correlation interpolation approach, Shirani et al.'s modified interblock correlation interpolation scheme, and Alkachouh and Bellanger's fast discrete cosine transformation-based spatial domain interpolation algorithm. Characteristic of the results are those of the "Lena" JPEG image when one fourth of periodically spaced blocks in the image have errors. The peak signal-to-noise ratio of the restored image is 28.68, 29.99, 31.86, 31.69, 31.57, and 34.65 dB using that of Ancis and Giusto, Sun and Kwok, Hemami and Meng, Shirani et al., Alkachouh and Bellanger, and RIPMAP, respectively.

Performance enhancement for rate-constrained motion estimation using Kalman filter

We present a new rate-constrained [rate and distortion (R-D)] motion estimation algorithm using Kalman filter to enhance the performance of the conventional R-D motion estimation at a relative low computational cost. We try to amend the incorrect and/or inaccurate estimate of motion with higher precision by using a Kalman filter. We first obtain a measurement of the motion vector of a block by using the existing R-D motion estimation scheme. We then generate the predicted motion vector utilizing the interblock correlation in neighboring blocks. Based on these two motion information, a simple 1-D Kalman filter is applied to obtain the optimal estimate of the motion vector. Simulation results show that the proposed technique can efficiently improve the R-D motion estimation performance.

A New Motion Estimation Algorithm for Video Coding Using Adaptive Kalman Filter

This paper presents a new motion estimation algorithm to improve the performance of the existing searching algorithms at a relatively low computational cost. We try to amend the incorrect and/or inaccurate estimate of motion with higher precision by using Kalman filter. We first obtain a measurement of motion vector of a block by using the existing searching scheme. We then generate the predicted motion vector utilizing the inter-block correlation in both spatial and temporal directions. In general, the motion correlation in spatial direction is different from that in temporal direction. Therefore, if we appropriately utilize the motion correlation in spatial direction and temporal direction, the better performance would be achieved. In this paper, we will propose an adaptive Kalman filter which utilizes a model switching mechanism to select correct motion model. Simulation results show that the proposed technique can efficiently improve the motion estimation performance.

Digital watermarking technique using block correlation of DCT coefficients

AbstractIn recent years, high‐quality digital media have spread rapidly. Accompanying the diffusion of digital media which can be simply copied and edited without deterioration of quality, research on digital watermarking techniques is being pursued as a means of preventing illegal copying and protecting copyright. In this paper, we will propose a technique for digital watermarking dealing with color images. The embedding of watermark information by the proposed technique is performed by using interblock correlation of the DCT coefficients of the brightness of a color image, and the strength of the watermark information to be embedded will vary with the visual features of the image. It is strongly embedded at places where there are intense changes of the brightness value and is weakly embedded at places where there are only mild changes of the brightness value. Since no distinction between the original image and the image in which the watermark information is embedded can be made visually, third‐party estimation of the position where the embedding is performed is very difficult. Moreover, detection of the embedded watermark information can be performed without referring to the original image and the parameters used in embedding. It has been confirmed by experiments that the watermark information embedded by the proposed technique is robust to JPEG compression and Gaussian noise. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 85(9): 23–31, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.10069

An Efficient Motion Estimation Algorithm for Video Coding Using Kalman Filter

This paper presents a new motion estimation algorithm to improve the performance of the existing searching algorithms at a relatively low computational cost. We try to amend the incorrect and/or inaccurate estimate of motion with higher precision by using Kalman filter. We first obtain a measurement of motion vector of a block by using the existing searching scheme. We then generate the predicted motion vector utilizing the inter-block correlation in both spatial and temporal directions. Based on these two motion information, a three-dimensional (3D) motion model is developed and then a 3D local Kalman filter is designed to obtain the optimal estimate of motion vector.