Block Discrete Cosine Transform Coefficients Research Articles

Efficient histogram for region based image retrieval in the discrete cosine transform domain

<p>Recently, several approaches of content based-image retrieval (CBIR), based on the characteristics of discrete cosine transform (DCT), such as decorrelation and concentration of energy in only a few coefficients, have been proposed. To reduce the semantic gap between high level search and low level patterns, a new concept based on region based search region-based image retrieval (RBIR) has been proposed. Recently, we proposed to use shape-adaptive (SA) DCT in a new RBIR system. In this paper, we propose an efficient histogram optimization suited to our region-based concept. This histogram takes into account the pattern’s from the SA-DCT of the border blocks as well as the DCT coefficients of the internal blocks. Our proposed scheme has greatly improved the results compared to region-based reference methods. Regionbased search is limited to the object of interest only, i.e. a region of the query image can only match a region of another image in the database.</p>

Image forgery detection based on statistical features of block DCT coefficients

Abstract Majority of the existing detection algorithms are able to deal with either type of forgery (splicing or copy-move). However, if we require a unified approach, we need to combine two previous works for each one of the alterations. In order to solve this problem, the authors present a new algorithm for the detection of splicing and copy-move forgery in the same instance. In this paper, a forgery detection technique is proposed which exploits the artifacts originated due to manipulations performed on JPEG encoded images. In JPEG compression technique, an image is divided into non-overlapping blocks of size 8x8 pixels and discrete cosine transform (DCT) coefficients are evaluated for each block independently. When a JPEG compressed image is tampered, there is a change in the statistical properties of AC components of block DCT coefficients. To capture this change, we propose to use standard deviation and count of non-zero DCT coefficients corresponding to each of the AC frequency components independently. The images are cropped by removing a few rows and columns from the top left corner and suggested features are evaluated for test image and its cropped version. The extracted feature vector is used with the support vector machine (SVM) for the classification of authentic and forged images. Experiments are conducted on a standard dataset of pre- and post-processed forged images CASIA vl.O and v2.0 to consolidate the theoretical concept of the proposed technique. Also, the comparative analysis is performed to showcase better detection rates compared with the state-of-the-art methods.

Compression of signs of DCT coefficients for additional lossless compression of JPEG images

One of the main approaches to additional lossless compression of JPEG images is decoding of quantized values of discrete cosine transform (DCT) coefficients and further more effective recompression of the coefficients. Values of amplitudes of DCT coefficients are highly correlated and it is possible to effectively compress them. At the same time, signs of DCT coefficients, which occupy up to 20% of compressed image, are often considered unpredictable. In the paper, a new and effective method for compression of signs of quantized DCT coefficients is proposed. The proposed method takes into account both correlation between DCT coefficients of the same block and correlation between DCT coefficients of neighbor blocks. For each of 64 DCT coefficients, positions of 3 reference coefficients inside the block are determined and stored in the compressed file. Four reference coefficients with fixed positions are used from the neighbor blocks. For all reference coefficients, 15 frequency models to predict signs of a given coefficient are used. All 7 probabilities (that the sign is negative) are mixed by logistic mixing. For test set of JPEG images, we show that the proposed method allows compressing signs of DCT coefficients by 1.1 ... 1.3 times, significantly outperforming nearest analogues.

Secure and Robust Digital Image Watermarking Using Coefficient Differencing and Chaotic Encryption

This paper presents a chaotic encryption-based blind digital image watermarking technique applicable to both grayscale and color images. Discrete cosine transform (DCT) is used before embedding the watermark in the host image. The host image is divided into $8\times 8$ nonoverlapping blocks prior to DCT application, and the watermark bit is embedded by modifying difference between DCT coefficients of adjacent blocks. Arnold transform is used in addition to chaotic encryption to add double-layer security to the watermark. Three different variants of the proposed algorithm have been tested and analyzed. The simulation results show that the proposed scheme is robust to most of the image processing operations like joint picture expert group compression, sharpening, cropping, and median filtering. To validate the efficiency of the proposed technique, the simulation results are compared with certain state-of-art techniques. The comparison results illustrate that the proposed scheme performs better in terms of robustness, security, and imperceptivity. Given the merits of the proposed scheme, it can be used in applications like e-healthcare and telemedicine to robustly hide electronic health records in medical images.

A novel hybrid of DCT and SVD in DWT domain for robust and invisible blind image watermarking with optimal embedding strength

To optimize the tradeoff between imperceptibility and robustness properties, this paper proposes a robust and invisible blind image watermarking scheme based on a new combination of discrete cosine transform (DCT) and singular value decomposition (SVD) in discrete wavelet transform (DWT) domain using least-square curve fitting and logistic chaotic map. Firstly cover image is decomposed into four subbands using DWT and the low frequency subband LL is partitioned into non-overlapping blocks. Then DCT is applied to each block and several particular middle frequency DCT coefficients are extracted to form a modulation matrix, which is used to embed watermark signal by modifying its largest singular values in SVD domain. Optimal embedding strength for a specific cover image is obtained from an estimation based on least-square curve fitting and provides a good compromise between transparency and robustness of watermarking scheme. The security of the watermarking scheme is ensured by logistic chaotic map. Experimental results demonstrate the better effectiveness of the proposed watermarking scheme in the perceptual quality and the ability of resisting to conventional signal processing and geometric attacks, in comparison with the related existing methods.

No-reference image quality assessment based on localized discrete cosine transform for JPEG compressed images

In this paper, we propose a new no-reference image quality assessment for JPEG compressed images. In contrast to the most existing approaches, the proposed method considers the compression processes for assessing the blocking effects in the JPEG compressed images. These images have blocking artifacts in high compression ratio. The quantization of the discrete cosine transform (DCT) coefficients is the main issue in JPEG algorithm to trade-off between image quality and compression ratio. When the compression ratio increases, DCT coefficients will be further decreased via quantization. The coarse quantization causes blocking effect in the compressed image. We propose to use the DCT coefficient values to score image quality in terms of blocking artifacts. An image may have uniform and non-uniform blocks, which are respectively associated with the low and high frequency information. Once an image is compressed using JPEG, inherent non-uniform blocks may become uniform due to quantization, whilst inherent uniform blocks stay uniform. In the proposed method for assessing the quality of an image, firstly, inherent non-uniform blocks are distinguished from inherent uniform blocks by using the sharpness map. If the DCT coefficients of the inherent non-uniform blocks are not significant, it indicates that the original block was quantized. Hence, the DCT coefficients of the inherent non-uniform blocks are used to assess the image quality. Experimental results on various image databases represent that the proposed blockiness metric is well correlated with the subjective metric and outperforms the existing metrics.

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.

SVD-based digital image watermarking using adaptive generated watermark

In this paper, an evolutionary-based algorithm for digital image watermarking is proposed. Robustness and imperceptibility are two important properties of digital image watermarking which conflict with each other. A singular value decomposition (SVD)-based intelligent algorithm is applied to make a trade-off between robustness and imperceptibility. In the proposed algorithm, first, the original image is divided into 8×8 blocks. Then some special blocks are transformed into discrete cosine transform (DCT) domain. SVD decomposition of DCT coefficients of special blocks is the next stage of the method. Finally, a binary watermark image is embedded into the singular values by a quantisation method. The major property of the proposed method is the generation of a binary watermark image using genetic algorithm to solve the optimisation problem between robustness and imperceptibility. So the watermark could be variable and image adaptive. The simulation results show improvement in robustness of the proposed method against several attacks such as noise contamination, filtering, joint photographic experts group (JPEG) compression and some geometrical attacks like cropping, rotation and rescaling in comparison with a recently similar existing work.

Combined significance map coding for still image compression

Set partitioning in hierarchical trees (SPIHT) was known for its relatively simple implementation and flexible scalability when it is combined with discrete wavelet transform (DWT). The authors propose a method called combined significance map coding (CSMC) to improve the coding efficiency of SPIHT when used with block-based discrete cosine transform (DCT). CSMC groups some blocks and encodes the combined significance map of one to several blocks together. Lots of bits spent in significance map coding can be saved when the trees constructed with block DCT coefficients have similar locality. From our simulation results, CSMC improves significantly when in comparison with the original SPIHT coder using DWT and DCT. It also yields better performance than JPEG2000, and even outperforms the non-scalable H.264 intra-mode coder for some test images. No coding table is required, and fine rate/quality scalability property of SPIHT is still preserved.

A Visual Inpainting Method Based on the Compressed Domain

Visual inpainting is an interpolation problem that restores an image or a frame with missing or damaged parts. Over the past decades, a number of computable models of visual inpainting have been developed, but most of these models are based on the pixel domain. Little theoretical and computational work of visual inpainting is based on the compressed domain. In this paper, a visual inpainting model in the discrete cosine transform (DCT) domain is proposed. DCT coefficients of the non-inpainting blocks are utilized to get block features, and those block features are propagated to the inpainting region iteratively. The experimental results with I frames of MPEG4 are presented to demonstrate the efficiency and accuracy of the proposed algorithm.

Bidirectional conversion between DCT coefficients of blocks and their subblocks

In the context of the discrete Fourier transform (DFT) and the discrete cosine transform (DCT), we present efficient methods for bidirectional conversion between transform coefficients of a signal block (one- or two-dimensional) and transform coefficients of the signal's subblocks. The DFT case is used to exemplify the underlying theoretical issues while the DCT case is considered for its practical importance. For typical DCT block sizes, our algorithms result in a 20% savings in multiplications over the fastest existing methods and have no penalty in reordering operations.

Reducing artifacts in block-coded images using adaptive constraints

This study aims to reduce blocking artifacts in compressed images coded by the block discrete cosine transform (BDCT). The theory of projection onto convex sets (POCS) is applied. Two adaptive constraints are used. The first constraint corresponds to a model that simulates an image before compression using a Delaunay triangular mesh. The mesh is constructed by dividing each block into a set of triangles. The number of triangles in a block is determined by the local block activity. The second constraint is a narrow quantization constraint, where the bound of a BDCT coefficient is also determined by the local block activity. The block activity is measured by the ac components of the BDCT coefficients by incorporating the human visual system model. Our technique produces significantly improved results after a single iteration, and compares favorably with existing methods.

Blocking artifacts reduction using discrete cosine transform

The block discrete cosine transform (BDCT) is one of the most frequently used tools for image coding. At high compression ratios, the BDCT often brings about blocking artifacts owing to coarse quantization of its coefficients. This paper presents new algorithms for reducing the blocking artifacts. The algorithms use the BDCT itself to reduce the blocking artifacts. Moreover the block positions of the BDCT are shifted from the block positions of the BDCT in the coding process. To reduce the blocking artifacts, the BDCT coefficients are suppressed properly in response to the distribution of the BDCT coefficients in the coding process. To evaluate the performance of the algorithms, the coding model is defined and the simulation experiments are made. The experimental results show the algorithms have the effects of reducing the blocking artifacts and increasing the signal-to-noise ratio (SNR).

An error concealment algorithm for images subject to channel errors

We present an algorithm to conceal bit errors in still images and image sequences that are coded using the discrete cosine transform (DCT) and variable length codes (VLCs). No modification is necessary to an existing encoder, and no additional bit rate is required. The concealment algorithm is kept simple so that real-time decoding and concealment is possible. A single bit error in these images can cause a block to split into several blocks or several blocks to merge into one. This causes the DCT coefficients of all subsequent blocks to be correctly decoded but stored in the wrong location in the image. Furthermore, the DC coefficient of all subsequent blocks may be incorrect. The error concealment algorithm uses transform domain information to identify the location of the affected blocks and to correct errors. The image quality after error concealment is shown to be significantly improved.

Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images

The reconstruction of images from incomplete block discrete cosine transform (BDCT) data is examined. The problem is formulated as one of regularized image recovery. According to this formulation, the image in the decoder is reconstructed by using not only the transmitted data but also prior knowledge about the smoothness of the original image, which complements the transmitted data. Two methods are proposed for solving this regularized recovery problem. The first is based on the theory of projections onto convex sets (POCS) while the second is based on the constrained least squares (CLS) approach. For the POCS-based method, a new constraint set is defined that conveys smoothness information not captured by the transmitted BDCT coefficients, and the projection onto it is computed. For the CLS method an objective function is proposed that captures the smoothness properties of the original image. Iterative algorithms are introduced for its minimization. Experimental results are presented. >