Motion estimation in DCT domain

In this paper, a hierarchical dependency context model (HDCM) is firstly proposed to exploit the statistical correlations of DCT (Discrete Cosine Transform) coefficients in H.264/AVC video coding standard, in which the number of non-zero coefficients in a DCT block and the scanned position are used to capture the magnitude varying tendency of DCT coefficients. Then a new binary arithmetic coding using hierarchical dependency context model (HDCMBAC) is proposed. HDCMBAC associates HDCM with binary arithmetic coding to code the syntax elements for a DCT block, which consist of the number of non-zero coefficients, significant flag and level information. Experimental results demonstrate that HDCMBAC can achieve similar coding performance as CABAC at low and high QPs (quantization parameter). Meanwhile the context modeling and the arithmetic decoding in HDCMBAC can be carried out in parallel, since the context dependency only exists among different parts of basic syntax elements in HDCM.

This paper presents new fast full search motion estimation (ME) algorithms using discrete cosine transform (DCT) coefficients as a gradient magnitude. The proposed algorithms use intra DCT coefficients to arrange the matching scan order. The proposed algorithm 1 uses some of DCT coefficients based on the relationship between gradient magnitude and DCT coefficients, and the proposed algorithm 2 uses all of DCT alternating current (AC) coefficients in order to predict the fluctuation of a block. The proposed algorithms significantly reduce the computational complexity of the conventional algorithm while maintaining the visual quality of video.

With the wide use of various image altering tools, digital image manipulation becomes very convenient and easy, which makes the detection of image originality and authenticity significant. Among various image tampering detection tools, double JPEG image compression detector, which is not sensitive to specific image tampering operation, has received large attention. In this paper, we propose an improved double JPEG compression detection method based on noise-free DCT (Discrete Cosine Transform) coefficients mixture histogram model. Specifically, we first extract the block-wise DCT coefficients histogram and eliminate the quantization noise which introduced by rounding and truncation operations. Then, for each DCT frequency, a posterior probability can be obtained by solving the DCT coefficients mixture histogram with a simplified model. Finally, the probabilities from all the DCT frequencies are accumulated to give the posterior probability of a DCT block being authentic or tampered. Extensive experimental results in both quantitative and qualitative terms prove the superiority of our proposed method when compared with the state-of-the-art methods.

Integer Discrete Cosine Transform (DCT) reduces hardware complexity by eliminating floating point multiplication. Multiplier less multiple constant multiplication (MCM) is used to further optimize integer multiplication by replacing it with shifters and adders. As N-point DCT takes 25% of hardware complexity in high efficiency video coding (HEVC), further optimizing DCT in terms speed and power remains critical. Lately, an alternate real-valued DCT coefficients were proposed which reduced the amount of scaling used in Integer DCT coefficients. It achieved the low dynamic range, high speed and low power at same degree of approximation for DCT coefficients. In this paper, an effort is made to further approximate real-valued DCT coefficients of N-point DCT so that critical path due to MCM is reduced. Proposed approximate real-valued DCT coefficients has relatively good PSNR with optimized hardware compared to existing real-valued DCT coefficients. FPGA implementation of 1D-DCT architecture for 32-point DCT with approximate real-valued coefficients shows the reduction of 35% and 24% in area-delay product and power respectively.

It is already known that a curve estimated via the discrete cosine transform (DCT) always passes through all the measured points without using imaginary numbers in the DCT coefficients, unlike the discrete Fourier transform. Moreover, owing to its character, the DCT can be used instead of the nonlinear least-squares method to express various theoretical curves. Because the DCT is a kind of Fourier transform, there is a possibility that the DCT could be employed to draw electron density distribution maps of crystals. If so, the probability that the DCT could be used to investigate the internal structure of materials by analysing the theoretical curves would increase. This article reports an attempt to draw the electron density distribution maps of the Mg3BN3 low-pressure phase [Mg3BN3(L)] by using the DCT in order to confirm the utility of the DCT for analysing the internal structure of materials. It is found that the DCT can provide mirror-symmetric electron density distribution projection maps and a modified DCT can be used to calculate whole standard electron density distribution projection maps for the surface plane of the unit cell. Moreover, a real crystal structure that has a centre of symmetry can be determined by the DCT by transforming a 1/4 part of the mirror-symmetric electron density distribution projection map.

-With developing of computer networks and digital techniques, the electronic image are easily created, edited, reproduced and distributed. Unfortunately, illegal copy and malicious tamper are also facilitated. At present, fragile watermark is researched greatly to authenticate the veracity and integrity of electronic contents. In this paper, a chaotic watermarking scheme for authentication of popular JPEG images is proposed. The quantized DCT ( Discrete Cosine Transform ) coefficients after entropy decoding are mapped to the initial values of the chaotic system, then the generated watermark information by chaotic iteration is embedded into JPEG compressed domain. Thanks to the high sensitivity on initial values of the chaotic mapping, the very accurate localization is realized for the malicious tampers to JPEG images. Because we directly modify the DCT coefficients after quantization for embedding the watermark, the proposed method can prevent the invalidation of tamper detection by JPEG re-quantization. Furthermore, because the proposed scheme avoids a large of computation on full decoding and re-encoding process, the low complexity and a high extracting speed are obtained. Simulation results show the proposed scheme can directly localize the tampers happened on the watermarked JPEG images. Furthermore, the ability of tamper localization is very sensitivity. So the proposed scheme is very fit for the integrity authentication of the electronic image in Internet.

This paper presents an efficient image cipher based on applying the chaotic Baker Map (BM) in the Discrete Cosine Transform (DCT). The encryption module of the proposed DCT-based BM image cipher employs a DCT on the original plain-image then, the DCT coefficients of the plain-image are shuffled with the BM. Finally, the inverse DCT is applied to the shuffled plain-image DCT coefficients to obtain the final cipher-image. The decryption module of the proposed DCT-based BM image cipher employs a DCT on the input cipher-image then, the DCT coefficients of the cipher-image are inversely shuffled with the BM. Finally, the inverse DCT is applied to the inversely shuffled cipher-image DCT coefficients to obtain the original plain-image. A set of experimental tests are performed to test the validity of the proposed DCT-based BM image cipher and the performed tests demonstrated the superiority of the proposed DCT-based BM image cipher in terms of statistical, differential, sensitivity and noise immunity.

Most of hybrid motion compensated video coding standards uses a well known discrete cosine transform (DCT) at the encoder to remove redundancy from video random processes. Due to the huge number of computations required to compute the DCT, reduction of its complexity is essential to speed up video processing. To optimize the computation of the DCT, we take into account the fact that the output of the DCT has often small values. The goal is not to compute DCT coefficients that will be quantized towards zero. This method of computing only a subset of DCT coefficients is commonly referred as pruning. In this paper we propose a method to perform the pruning of the two-dimensional (2-D) DCT based on the algorithm proposed in E. Feig and S. Winograd (1992) for any number of low-frequency components.

Proposed is an efficient face recognition algorithm using the discrete cosine transform DCT Technique for reducing dimensionality and image parameterization. These DCT coefficients are examined by a MLP (Multi-Layer Perceptron) and radial basis function RBF neural networks. Their purpose is to present a face recognition system that is a combination of discrete cosine transform (DCT) algorithm with a MLP and RBF neural networks. Neural networks have been widely applied in pattern recognition for the reason that neural-networks-based classifiers can incorporate both statistical and structural information and achieve better performance than the simple minimum distance classifiers. The authors demonstrate experimentally that when DCT coefficients are fed into a back propagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. Comparison with other statistical methods like Principal component Analysis (PCA) and Linear Discriminant Analysis (LDA) is presented. Their face recognition system is tested on the computer vision science research projects and the ORL database.

To reduce the computation complexity of discrete cosine transform (DCT) and quantization in H.264 video coding, a highly effective algorithm to detect all-zero DCT coefficient blocks (AZB) before DCT is proposed, after analyzing the distribution model of DCT coefficients and the properties of the integer DCT and quantization. Experimental results show that the detection ratio of the proposed algorithm achieves up to 98% and up to 47% higher than that of the existing methodologies.

At recently, the most of the digital images are stored and transferred in their compressed format using discrete cosine transform (DCT)-based compression technique. DCT is one of the most important data compression technique due to the efforts from Joint Photographic Experts Group(JPEG). Blind steganalysis means how to detect the presence of the messages that are hidden using different types of steganography algorithms and has the ability to detect new unknown steganography algorithms. This paper presents blind steganalysis technique that can reliably detect the existence of messages hidden in JPEG files. In order to save the computation and memory cost, it is desirable to have image processing operations implemented directly in the DCT domain. The proposed method is based on extracting features directly from DCT domain through the analysis of differences between DCT coefficients before and after cropping. The extracted features are prepared as input to Support Vector Machine (SVM) to classify the image as stego (image that contain secret message) or clean (image that does not contain secret message). The experiments performed show that the proposed method yields better classification accuracy compared with other related works.

워터마킹은 소유권을 주장하기 위하여 워터마크라는 디지털 신호를 영상에 첨가하는 것이다. 본 논문에서는 기존의 DCT를 이용한 디지털 워터마킹 방법을 개선하여 비가시성과 견고성을 동시에 향상시키는 새로운 DCT 기반의 디지털 워터마킹 방법을 제안한다. 제안한 방법에서는 원 DCT 계수의 상대적 중요성이 워터마킹된 DCT 계수에서도 보존되며, 원 DCT 계수의 왜곡을 최소화시키는 워터마크 재정렬 방법을 이용함으로서 비가시성과 견고성을 향상시킨다. 실험을 통하여 제안한 방법은 기존의 워터마킹 방법보다 비가시성을 약 9～12[dB] 향상시키며, 다양한 공격에 대해 견고성이 월등히 우수함을 확인하였다. Watermarking is embedding a digital signal called as watermark into images to claim the ownership. In this paper, a new digital watermarking algorithm based on DCT (Discrete Cosine Transform) which enhances invisibility and robustness is proposed to improve contentional digital watermarking method using DCT. In the proposed method, it is possible to enhance invisibility and robustness using watermark reordering in which the relative significance of original DCT coefficients can be preserved in watermarked DCT coefficients, and the distortions of original DCT coefficients can be minimized. The experimental results show that the proposed method improves invisibility approximately 9～12[dB] and is more robust to various attacks than the conventional method.

In this paper, a copyright protection scheme that combines the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) is proposed. The proposed scheme first extracts the DCT coefficients of secret image by applying DCT. After that, image features are extracted from cover image and from DCT coefficients by applying DWT on both separately. Hiding of extracted features of DCT coefficients in the features of cover image is done using two different secret keys. Experimentation has been done using eight different attacks. Experimental results demonstrate that combining the two transforms improves the performance of the steganography technique in terms of PSNR value and the performance is better as compared to that achieved using DWT transform only. The extracted image has good visual quality also.

Statistical Computation of Discrete Cosine Transform in Video Encoders

This study dealt with the problem of estimating constant time delay embedded into a received signal that was noisy, delayed and damped image of a known reference signal. The received signal was filtered, normalized with respect to the peak value it achieved and then transformed by the Discrete Cosine Transform (DCT) into DCT coefficients. Those DCT coefficients that were most sensitive to time delay variations were selected and grouped to form the Reduced Discrete Cosine Transform Coefficients set (RDCTC). The time delays embedded in the filtered signals were efficiently encoded into those RDCTC sets. The RDCTC sets were applied to a pre trained multi layer feedforward Neural Network (NN), which computed the time-delay estimates. The network was initially trained with large sets of RDCTC vectors, in which each RDCTC vector corresponded to a signal delayed by a randomly selected constant time-delay. Using the RDCTC as input to the NN instead of the full length incoming signal itself resulted in a major reduction in the NN size. Accurate time delay estimates were obtained through simulation and compared against estimates obtained through classical cross-correlation technique.