Sum Of Absolute Difference Values Research Articles

A video steganalysis method based on coding cost variation

Aiming at the problems existing in existing steganalysis algorithms, this article proposes Motion Vector Coding Cost Change video steganalysis features based on Improved Motion Vector Reversion-Based features and Subtractive Probability of Coding Cost Optimal Matching features based on Subtractive Probability of Optimal Matching features from the perspective of the change of coding cost. Motion Vector Coding Cost Change features can be well consistent with the coding cost before recoding by analyzing the sub-pixel coding cost of recoding. By counting the sub-pixel coding costs of motion vectors before and after video recoding, the Sum of Absolute Difference values of motion vectors instead of predicted residuals are applied to steganalysis and detection, and the steganographic algorithm based on motion vectors is effectively detected. Experiments show that Motion Vector Coding Cost Change features have higher detection accuracy than Add-or-Subtract-One, Improved Motion Vector Reversion-Based, and other typical features in various steganography methods, and Subtractive Probability of Coding Cost Optimal Matching features have higher detection effect and better robustness than Subtractive Probability of Optimal Matching features.

Energy‐efficient system‐on‐chip reconfigurable architecture design for sum of absolute difference computation in motion estimation process of H.265/HEVC video encoding

SummaryMotion estimation is the important and computationally intensive part of any video encoding. The objective of this paper is to design and analyze the coarse and fine reconfiguration of processing element‐based hardware design for block matching–based motion estimation in H.265/HEVC video processing. Sum of absolute difference (SAD) is the commonly used criteria for block matching in the motion estimation process. User input is taken as the parameter for coarse reconfiguration, and the threshold value in SAD is considered for the fine reconfiguration. Processing elements are the hardware units designed for performing SAD calculation. In this work, the hardware for block‐based motion estimation for H.265/HEVC encoder is designed using multiple processing elements, which will calculate the SAD values. A system‐on‐chip architecture is implemented and verified the optimization in terms of power and area utilization by reconfiguring the architecture dynamically based on the input video quality requirement.

RANSAC algorithm for matching inlier correspondences in video stabilisation

In order to stabilise jittery video, we need to find a transform which reduces the distortion between frames. To find this transformation, feature points must be identified in consecutive frames. In the existing methods, correspondences between feature points are found by considering sum of squared differences as matching cost between respective points but by this method, many of the point correspondences are obtained having limited accuracy. To rectify this problem, we proposed here, M-estimator SAmple Consensus (MSAC) algorithm which is variant of random sample consensus (RANSAC) algorithm. In our proposed method, inlier and outlier feature points are found by conventional RANSAC algorithm. Then to match these inlier feature points MSAC algorithm is used which give the robust estimate of transformation between consecutive video frames. The MSAC algorithm is repeated multiple times and at each run the cost of the end result is calculated via Sum of Absolute Differences between both image frames. Sum of absolute difference (SAD) measures the distortion between two frames by evaluating the similarity between image blocks. On the basis of SAD values, affine transform is derived. This transform gives details about the camera motion and is capable to improve the image plane. It is clear from simulation results, inliers correspondences get exactly coincident which gives more favourable results thus stabilising jittery videos.

Accurate Frame Rate Up-Conversion for Advanced Visual Quality

Motion-compensated frame rate up-conversion (FRUC) is a method for providing better video image quality than non-motion-based methods. However, incorrect motion vector (MV) prediction can lead to continued induction of debris and artifacts. In this paper, a motion compensated FRUC algorithm is proposed, which is able to acquire more accurate MVs by referencing neighboring frames with hierarchical motion filed construction block size and an MV mapping stage. The block size of hierarchical MV filed construction has been enlarged in order to acquire more motion information, and the size of the MV mapping and image interpolation stage has been reduced to obtain a more accurate interpolated result. In order to smooth the MV, a median filter is considered with sum of absolute difference value and constructed with an adaptive MV smoothing method. For the purpose of suppressing the debris, a noise removal filter is applied after the interpolation operation. According to the experimental result, the proposed approach is able to improve visual quality performance by 0.82 dB on average. Subjective analysis shows that annoying artifacts are significantly reduced.

Application of Seasonal Holt-Winter model for the prediction of exchange rate volatility

Prediction of exchange rates volatility is a challenging problem in investigation of financial markets. Various econometrical models are widely used in predicting of foreign exchange rates. Construction of optimal model is complicated problem due to many factors which can influent volatility of foreign exchange rate. Values and impact of factors on the exchange rate are changing over time; therefore investors should be cautious for the fact that there always be a certain level of risk investing in foreign currency. The article investigates the dynamics of hourly exchange rates of the Euro (EUR) against USA dollar (USD). Foreign exchange (forex) rate dynamic is a complex process that can be better understood through investigation of its characteristics. The idea of describing fluctuations by calculating sum of absolute differences (SAD) of a time series is developed. SAD value time series are supposed to be easier predicted since it takes into account of only the magnitude of exchange rate oscillation ignoring its direction. For the analysis and investigation of exchange rates volatility is applied both additive and multiplicative versions of Holt – Winter exponential smoothing techniques. These methods are appropriate for series with a linear trend and seasonal variations. The analysis of EUR/USD exchange rates data in major financial centers showed that they obey intraday periodical variations, so it was used the mentioned model. Two different statistics: mean absolute error (MAE) and root mean squared error (RMSE) were applied for selection of optimal parameters of the model. The modeling results of models were compared. After investigation it was noted that the best prediction results are given by simplified multiplicative model.DOI: http://dx.doi.org/10.5755/j01.ee.26.4.5210

Low-complexity motion estimation design using modified XOR function

Video coding techniques which are characterized by huge computational burden extensively consume power. Motion estimation with block matching criterion utilizing sum of absolute differences (SAD) with variable block size is the main source for such complexity and huge power consumption. In this work, we introduce a modified matching criterion in bit-level that lowers the computational complexity and hardware implementation when compared with the many SAD implementations introduced in the literature. We show that the number of hardware resources illustrated by the number of logic gates that are used in our design is much less than the number of the gates that are used in the traditional SAD and others in the literature. This leads in turn to a reduction in hardware complexity and consumed power. These are achieved by making our design rely on reusing the partial SAD values of smaller sub-blocks and then providing them to the compare and select unit as early as they are ready. Moreover, the final 41 motion vectors of motion estimation overlap in time and hence lower the number of output buses of the hardware implementation which results in a reduction in complexity as well. The video quality is only reduced by 0.17 dB while the bit-rate is increased only by 0.58 % in our simplified hardware architecture. The system logic synthesis is performed using the widely used FPGA platform. It produces 6.2 k LUT with a maximum operating frequency of 293 MHz (180 [email protected]).

Early search termination for fast motion estimation

This paper proposes a novel method for early termination of a motion search. It first introduces an estimator of the sum of absolute difference (SAD) between the current block and a search point. After analyzing the SAD estimator, it proposes the SAD condition to decide whether the current search point is around a minimum point or not. The counter condition is used to evaluate the current search point as an early search termination cue. After the current search point is considered as a minimum point, the search is stopped immediately. The proposed algorithm can easily be combined with most existing fast motion estimation algorithms to further reduce computational cost. While previous thresholding techniques have focused on the correlation of SAD values between a current block and its neighboring blocks, the proposed algorithm studies its own characteristics to predict the threshold. Hence, the proposed termination scheme can complement the previous early search termination techniques by merging the termination methods together, in order to further reduce computational cost. Experimental results demonstrate that the proposed algorithm successfully reduces the computational cost by combining the previous early search termination techniques.

이진트리 구조에 따른 구간별 탐색 후보점을 이용한 비디오 코딩의 움직임 추정

Abstract In this paper, we propose a new fast block matching algorithm for block matching using the temporal and spatially correlation of the video sequence and local statistics of neighboring motion vectors. Since the temporal correlation of the video sequence between the motion vector of current block and the motion vector of previous block. The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(sum of absolute difference) value by the predicted motion vectors of neighboring blocks around the same block of the previous frame and the current frame and the predictor candidate point on each division region by binary-tree structure. Experimental results show that the proposed algorithm has the capability to dramatically reduce the search points and computing cost for motion estimation, comparing to fast FS(full search) motion estimation and other fast motion estimation. Key Words : Block Matching Algorithm, Motion Estimation

Block-matching algorithm based on differential evolution for motion estimation

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame (macro block, MB) can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing the sum of absolute differences (SAD) produced by the MB of the current frame over a determined search window from the previous frame. The SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. The most straightforward BM method is the full search algorithm (FSA), which finds the most accurate motion vector, exhaustively calculating the SAD values for all the elements of the search window. Over this decade, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the cost of poor accuracy. In this paper, a new algorithm based on differential evolution (DE) is proposed to reduce the number of search locations in the BM process. To avoid computing several search locations, the algorithm estimates the SAD values (fitness) for some locations using the SAD values of previously calculated neighboring positions. As the proposed algorithm does not consider any fixed search pattern or any other different assumption, a high probability for finding the true minimum (accurate motion vector) is expected. In comparison with other fast BM algorithms, the proposed method deploys more accurate motion vectors, yet delivering competitive time rates.

Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost.

Lognormal Fitting of Particle Size Distribution Data Monitored in Animal Buildings: Linear versus Nonlinear Regression Approach

Lognormal distribution is the most frequently used particle size distribution (PSD) model in agricultural air quality research, and a proper determination of volume median diameter (VMD) and geometric standard deviation (GSD) is crucial for model applications. In this study, the prevalent linear regression approach was compared to the nonlinear regression approach with PSD data monitored in 15 animal buildings using four particle size analyzers: Horiba, DSP, Coulter, and Malvern. Results showed that the linear regression approach significantly underestimated VMD values, and the resulting relative errors were higher overall than those yielded by the nonlinear regression approach. As a measure of model approximation, the values of the sum of absolute differences (SAD) derived from linear regression were considerably larger than those offered by nonlinear regression. The relative errors and SAD values both suggested a poorer performance of the linear regression approach. The SAD values and the relative errors in estimated VMD and GSD decreased as R2 approached 1. However, even at R2 = 0.98, the linear regression approach misestimated VMD values by up to 30% (or 14.6 µm) and yielded SAD values of around 20% to 40%. A strong interference of minor PSD peaks on the linear regression results was identified, which partly explains the inferior performance of the linear regression approach as compared to the nonlinear regression approach. For future relevant studies, the limitations of the linear regression approach should be recognized, and a preliminary comparison between different PSD parameter estimation approaches is recommended.

비디오 코딩을 위한 인접블록 움직임 벡터를 이용한 예측 탐색 알고리즘

동영상의 현재 블록의 움직임 벡터와 이전 블록의 움직임 벡터는 시간적 상관성을 갖고 있다. 본 논문에서는 비디오의 시공간적인 특성과 인접 블록 움직임 벡터의 통계적 특성을 이용하는 새로운 예측 탐색 알고리즘을 제안한다. 제안된 ANBA는 이전 프레임과 현재 프레임의 인접 블록의 움직임 벡터들의 평균값으로 구한 후보 벡터와 현재 블록의 이전 시점의 움직임 벡터의 2개의 후보 벡터 중에서 가장 작은 SAD 값을 갖는 점을 정확한 움직임 벡터를 찾기 위한 초기 탐색점 위치로 결정한다. 실험 결과 제안된 방식은 MVFAST와 PMVFAST에 비해 PSNR 값에 있어서 평균적으로 0.01~0.64dB 개선되고 영상에 따라 최고 1.06dB 정도 우수한 결과를 나타내었다. There is the temporal correlation of the video sequence between the motion vector of current block and the motion vector of previous block. In this paper, we propose a new prediction search algorithm for block matching using the temporal and spatial correlation of the video sequence and local statistics of neighboring motion vectors. The proposed ANBA(Adaptive Neighboring-Block Search Algorithm) determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(Sum of Absolute Difference) value by the predicted motion vectors of neighboring blocks around the same block of the previous frame and the current frame and use a previous motion vector. Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved up to the 1.06dB as depend on the video sequences and improved about 0.01~0.64dB over MVFAST and PMVFAST.

Motion Estimation Optimization for H.264/AVC Using Source Image Edge Features

The H.264/AVC coding standard processes variable block size motion-compensated prediction with multiple reference frames to achieve a pronounced improvement in compression efficiency. Accordingly, the computation of motion estimation increases in proportion to the product of the number of reference frame and the number of intermode. The mathematical analysis in this paper illustrates that the motion-compensated prediction errors are mainly determined by the detailed textures in the source image. The image block being rich in textures contains numerous high-frequency signals, which make variable block size and multiple reference frame techniques essential. On the basis of rate-distortion theory, in this paper, the spatial homogeneity of an image block is made as a relative concept with respect to the current quantization step. For the homogenous block, its futile reference frames and intermodes can be eliminated efficiently. It is further revealed that the sum of absolute differences value of an image block is mainly determined by the sum of its edge gradient amplitude and the current quantization step. Consequently, the image content-based early termination algorithm is proposed, and it outperforms the original method adopted by JVT reference software. Moreover, the dynamic search range algorithm based on the edge gradient amplitude of source image block is analyzed. One eminent advantage of the proposed edge-based algorithms is their efficiency to the macroblock-pipelining architecture, and another desirable feature is their orthogonality to fast block-matching algorithms. Experimental results show that when these algorithms are integrated with hybrid unsymmetrical-cross multi-hexagongrid search, an averaged 31.4-60.0% motion estimation time can be saved, whereas the averaging BDPSNR loss is 0.0497 dB for all tested sequences.

Macroblock and Motion Feature Analysis to H.264/AVC Fast Inter Mode Decision

One fast inter mode decision algorithm is proposed in this paper. The whole algorithm is divided into two stages. In the pre-stage, by exploiting spatial and temporal information of encoded macrobocks (MBs), a skip mode early detection scheme is proposed. The homogeneity of current MB is also analyzed to filter out small inter modes in this stage. Secondly, during the block matching stage, a motion feature based inter mode decision scheme is introduced by analyzing the motion vector predictor's accuracy, the block overlapping situation and the smoothness of SAD (sum of absolute difference) value. Moreover, the rate distortion cost is checked in an early stage and we set some constraints to speed up the whole decision flow. Experiments show that our algorithm can achieve a speed up factor of up to 53.4% for sequences with different motion type. The overall bit increment and quality degradation is negligible compared with existing works.

Hybrid Temporal Error Concealment Methods for Block-Based Compressed Video Transmission

Boundary matching algorithm (BMA) and decoder motion vector estimation (DMVE) are two well-known temporal error concealment methods using the matching-based approach. In these two methods, the motion vector of each missing block is estimated by choosing one among candidate motion vectors which minimizes a sum of absolute differences (SAD) between boundary pixels of the corrupted macroblock. In general, the performance of DMVE is better than that of BMA. However, depending on the location or pattern of the corrupted block, BMA produces higher visual quality than DMVE. In this paper, we propose two types of hybrid error concealment methods; switching method and blending method. The switching method chooses one of two results obtained by BMA and DMVE based on the normalized SAD values. In the blending method, the weighted sum of the results concealed by the aforementioned two methods is utilized to improve the performance of error concealment. In order to reduce blocking artifacts further, the modified overlapped-block motion compensation is adaptively applied to the concealed blocks. Simulation results show that the proposed methods outperform other techniques in terms of subjective visual quality as well as PSNR performance.

A New EDI-based Deinterlacing Algorithm

In this paper, we propose a new deinterlacing algorithm using edge direction field, edge parity, and motion expansion scheme. The algorithm consists of an EDI (edge dependent interpolation)-based intra-field deinterlacing and inter-field deinterlacing that uses block-based motion detection. Most of the EDI algorithms use pixel-by-pixel or block-by-block distance to estimate the edge direction, which results in many annoying artifacts. We propose the edge direction field, and estimate an interpolation direction using the field and SAD (sum of absolute differences) values. The edge direction field is a set of edge orientations and their gradient magnitudes. The proposed algorithm assumes that a local minimum around the gradient edge field is most probably the true edge direction. Our approach provides good visual results in various kinds of edges (horizontal, narrow and weak). And we propose a new temporal interpolation method based on block motion detection. The algorithm works reliably in scenes which have very fast moving objects and low SNR signals. Experimental results on various data set show that the proposed algorithm works well for the diverse kinds of sequences and reconstructs flicker-free details in the static region1.

움직임 벡터 예측 후보들과 적응적인 탐색 패턴을 이용하는 블록 정합 알고리즘

In this paper, we propose the prediction search algorithm for block matching using the temporal/spatial correlation of the video sequence and the renter-biased property of motion vectors The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(Sum of Absolute Difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate pint in each search region and the predicted motion vector from the neighbour blocks of the current frame. And the searching process after moving the starting point is processed a adaptive search pattern according to the magnitude of motion vector Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved up to the 0.75dB as depend on the video sequences and improved about 0.05∼0.34dB on an average except the FS (Full Search) algorithm.

Adaptive rate control using nonlinear regression

This paper presents a simple, fast, and accurate rate-control algorithm using nonlinear regression that plays a central role in estimation theory. We measure a conditional mean by estimating a joint probability density function (PDF) using Parzen's (1962) window. The training data pick up the nonlinear rate-distortion (R-D) relationship between the quantization parameter (QP) and the bits spent for each macroblock depending on the sum of absolute differences (SAD). We increase the accuracy of this joint PDF by clustering the training data depending on the QP values within admissible ranges. This localization helps understand image characteristics more accurately. Then we apply the adaptive vector quantization (AVQ) to simplify the conditional mean estimation of the rate given the SAD and QP values. This information is stored into three look-up tables. They contain the localized R-D function on macroblock basis. We use these tables to find the optimal QP values in least-mean-square sense for a given bit budget of the current frame. Simulation results show that the proposed algorithm outperforms the informative MPEG-4 rate-control algorithm in terms of reproduced image quality and coding efficiency. Our algorithm gives better image quality using much fewer bits. Most of all, it keeps the bit rate very accurately due to the accuracy of the conditional mean estimator that solves the nonlinear R-D function.