Fast motion estimation based on total least squares for video encoding

Motion estimation (ME) has widely been used in video codecs, such as MPEG and ITU-T H.261, to remove interframe redundancy. This paper describes a fast full-search ME method for programmable processors with a multiply-accumulator. It is based on the block matching method that evaluates block mismatch by the mean square error. In order to reduce the computational load of the ME, a fast two-dimensional FIR filtering algorithm is applied, and the redundancy of the overlapped area computation among the reference blocks is eliminated. When the block size is 16/spl times/16 and the number of reference blocks in the search window is 32/spl times/32, this method reduces the computational load to 22.6% without degrading the motion tracking capability.

An enhanced constrained one-bit transform (C1BT)-based fast motion estimation (ME) method is proposed. Binary transform-based ME identifies the proper motion vector by transforming the 8-bit pixels in the original image into a low bit depth of the bit plane, substantially reducing the computational complexity and facilitating hardware implementation by using the number of non-matching points (NNMPs) instead of the sum of absolute difference (SAD). However, a motion block size is N × N , therefore the dynamic range of NNMP (0 ≤ NNMP ≤ N × N ) is decreased to 1/256 compared to that of the SAD with 8-bit images (0 ≤ SAD ≤ 256 × N × N ). The higher the NNMP between the current bit plane and the previous bit plane, the more probable that it will be the block most similar to each other. Therefore, the matching error criterion of NNMP is extended to improve ME performance. Experimental results show that the proposed algorithm improves the performance of ME accuracy by 0.27, 0.38 and 0.67 dB compared to the C1BT-based ME, two-bit transform (2BT)-based ME and 1BT-based ME, respectively.

Motion estimation (ME) is a time-consuming algorithm to find a matching block in the search area for video applications, such as video compression. Motion estimation algorithm includes full search algorithm and fast search algorithm. If the width of the motion estimation algorithm search area is twice the size of the block, half of the search area for adjacent blocks overlaps. In view of this, this paper proposes a search area data reuse method for fast search motion estimation. With the proposed data reuse implementation, only the first block needs to read the data of the entire search area from the off-chip memory. The other blocks only need to read half the original search area. In this way, time of access to external memory is reduced, and running time of the algorithm is also decreased. Experimental results of diamond search show that the search area data reuse methods can reduce the running time by 40% to 60% compared with the algorithm of no use data reuse, and it can also reduce the power consumption by 62% to 73%. Compared with other methods in the literature, the proposed method also performs better on running time and power consumption.

Summary In this study, we show significant improvements over conventional pressure-transient analysis on the basis of nonlinear regression by using total least squares (TLS), which minimizes errors in both pressure and time simultaneously. To our knowledge, TLS has not been applied to pressure-transient analysis before in this sense. TLS regression is not an easy problem to solve mathematically, especially for nonlinear pressure-transient-model functions. In this work, we compare four different versions of TLS. We formulate a robust approximation of the TLS solution, which can handle a variety of tests and reservoir models yet does not compromise the performance of the analysis. We show that our technique reduces ambiguity in the estimation of parameters to a large extent, especially in the presence of noise in time. Using our TLS algorithm, we obtain much narrower confidence intervals on the parameter estimates of a variety of real data sets, compared to the conventional least squares (LS) approach. For synthetic data sets, we observe that the TLS estimates are often closer to the true values than estimates made with LS, especially for poorly determined problems. When the deviation is in pressure only, TLS and LS results are comparable. However, in the presence of deviations in time in addition to pressure, the performance of TLS algorithms is substantially better. We, therefore, expect that our technique will provide more accurate estimation of reservoir parameters, allowing for better forecasting of reservoir performance.

This paper presents an image-based velocity control method for a VTOL UAV. For increased autonomy, a realtime embedded global motion estimation process using efficient feature tracking was developed. The VTOL vehicle is assumed to be equipped with a minimum sensor suite — i.e. a camera, altitude sensor and IMU — and to hover over a featured flat plane. This paper first presents a fast feature-based global motion estimation method, and then proposes a control strategy based on this method. Finally, experimental results using a quadrotor demonstrate the performance of the proposed control strategy and feature-based motion estimation.

In this paper, an efficient and fast global motion estimation method is proposed. This method is a generic one that can accommodate various global motion models, ranging from a simple translation model to an eight parameters perspective model. At the first stage, a simple filter is implemented to classify different parts of the frames. Since pixels from homogeneous regions and highly textured regions do not contribute much in global motion estimation, it is proposed that they-be excluded from processing. The remained pixels are fed into a three-level hierarchical motion estimation system and a dynamic sprite is eventually generated. From the experimental results, we can conclude that the proposed global motion estimation system manages to speed up the processing time for sprite generation while maintaining quality in the reconstructed frames.

The Total Least Squares (TLS) method is a generalized least square technique to solve an overdetermined system of equations Ax\simeqb . The TLS solution differs from the usual Least Square (LS) in that it tries to compensate for arbitrary present in both A and b . In certain problems the perturbations of A and b are linear functions of a common noise source vector. In this case we obtain a generalization of the TLS criterion called the Constrained Total Least Squares (CTLS) method by taking into account the linear dependence of the terms in A and b . If the columns of A and b are linearly related then the CTLS solution is obtained in terms of the largest eigenvalue and corresponding eigenvector of a certain matrix. The CTLS technique can be applied to problems like Maximum Likelihood Signal Parameter Estimation, Frequency Estimation of Sinusoids in white or colored by Linear Prediction and others.

For video compression, motion estimation is popularly employed to exploit temporal correlation existing in video sequences. If we employ the full search block matching algorithm for estimating motion vectors, it requires very heavy computational complexity. Although several fast block matching algorithms have been proposed to solve this problem, they sacrifice their reconstructed image quality. In this paper, we derive optimal search patterns for fast block matching motion estimation. By analyzing the block matching algorithm as a function of the block size and the shape, we find optimal search patterns for initial motion estimation. The proposed idea can provide an analytical ground for the current MPEG-2 proposals. In addition, we propose a new fast motion estimation algorithm using adaptive search patterns, considering matching criteria and statistical properties of object displacement. In order to select an appropriate search pattern, we exploit the relationship between the motion vector and the frame difference of each block. By changing the search pattern adaptively, we can improve the motion prediction accuracy, while reducing the required computational complexity compared to other fast block matching algorithms.

Most of the fast search motion estimation algorithms reduce the computational complexity of motion estimation (ME) greatly by checking only a few search points inside the search area. In this paper, we propose a new algorithm--multi-layer motion estimation (MME) which reduces the computational complexity of each distortion measure instead of reducing the number of search points. The conventional fast search motion estimation algorithms perform ME on the reference frame with full distortion measure; on the contrary, the MME performs ME on the layers with partial distortion measures to enhance the computational speed of ME. A layer is an image which is derived from the reference frame; each macro-pixel value in the layer represents the sum of the values of the corresponding pixels in the reference frame. A hierarchical quad-tree structure is employed in this paper to construct multiple layers from the reference frame. Experimental results on different video sequences show evidence that many motion vectors have been found similar both in the reference frame and the layers. The effectiveness of the proposed MME algorithm is compared with that of some state-of-the-art fast block matching algorithms with respect to speed and motion prediction quality. Experimental results on a wide variety of video sequences show that the proposed algorithm outperforms the other popular conventional fast search motion estimation algorithms computationally while maintaining the motion prediction quality very close to the full-search algorithm. Moreover, the proposed algorithm can achieve a maximum of 97.99 % speed-improvement rate against the fast full-search motion estimation algorithms which are based on hierarchical block matching process. The proposed MME performs the motion estimation on the layers by using three types of search patterns. The derivation of these search patterns exploits the characteristic of the center-biased motion vector distribution and that of less intensive block distortion measurement of the layers.

In this paper, a new technique called Mean Predictive Block Matching (MPBM) is proposed. This technique is based on adaptive rood pattern search algorithm, in which the size is dynamically determined for each macroblock, based on the mean of two motion vectors of the neighboring macroblocks instead of one as is the case with adaptive rood patter search. The proposed technique attempts to decrease the computation time between the current macroblock and the macroblocks in the reference frame, by stopping the calculation of the sum absolute different between the pixels in the current and the reference frame macroblocks when the current uncompleted sum absolute value is greater than the previous calculated one. Experimental results showed that the proposed technique reduces the search time of the macroblock matching, as well as achieves better resolution for fast block motion estimation in comparison to the standard block matching algorithms.

In this paper, we propose a fast block motion estimation scheme by exploiting the correlations of motion vectors (MV) existing in spatially- and temporally-adjacent as well as hierarchically-related blocks. The basic idea is to use the information obtained from the corresponding block at a coarser resolution level and spatio-temporal neighboring blocks at the same level in order to select a good set of initial MV candidates and then perform further local search to find the best matching MV. In order to further reduce computational complexity, the sign pattern vector (SPV) is defined and used for block matching, as opposed to the pixel intensity values used in the conventional block matching methods. By using the SPV definition, a data block can be presented by a mean and a set of binary sign patterns with a much reduced data set. The block matching motion estimation is then divided into mean matching and binary phase matching. The proposed technique enables a significant reduction in computational complexity compared with the conventional block matching motion estimation (ME) because binary phase matching only involves Boolean logic operations. This scheme also significantly reduces the data transfer time between the frame buffer and motion estimator. Our test results indicate that the performance of the proposed scheme is comparable with the full-search block matching under the same search ranges, however, the proposed scheme has a speed-up factor ranging from 250 to 370 in comparison with full search.

Predictive fast Motion Estimation (ME) algorithms have been widely used in video CODECs due to their performance efficiency and low computational complexity. In this thesis, a new block-based fast motion estimation technique named Dynamic Predictive Search Algorithm (DPSA) is developed, which can be considered in predictive zonal search category. The proposed approach is based on the observation that temporally and spatially adjacent macro-blocks are not just statically correlated, but also dynamic alterations in their motion content are highly coherent. DPSA introduces a new set of six candidate predicted motion vectors. For early termination criteria, DPSA modifies termination procedure of already existing EPZS algorithm. Performance of this newly proposed algorithm has been compared to four other state-of-the-art algorithms implemented on JVT, H.264 standard software platform. Experimental results have proven that DPSA accomplishes up to 38% compression ratio enhancement achieved by a process with more 14.75% less computational complexity and up to0.47 dB higher PSNR values over the EPZS. It also manages to have up to 13% speed up over EPZS algorithm. Because of its simplicity and low computational complexity DPSA is energy efficient for portable video processing in computation- or power-constrained applications and easy to be implemented on both FPGA- and Microcontroller-based embedded systems. Also, higher compression ratio makes DPSA more compatible with limited capacity storage media, and limited band-width transmission networks.

This paper proposes a fast motion estimation algorithm for variable block-sizes by utilizing motion vector bottom-up procedure for H.264. The refined motion vectors of adjacent small blocks are merged to predict the motion vectors of larger blocks for reducing the computation. Experimental results show that our proposed method has lower computational complexity than full search, fast full search and fast motion estimation of the H.264 reference software JM93 with slight quality decrease and little bit-rate increase.

Multi-grid method is applied to optical flow estimation in ultrasound image sequences. The existing multigrid method is modified to accommodate the nonlinearity in the motion model. The nonlinear iteration, instead of the linear one, is proposed for the approximate solution in the finest grid. The proposed method was tested in both of panoramic imaging and the spatio-temporal noise reduction. The results showed that its performance was not distinguishable from the one-level method in spites of quite less computational burden.

본 논문은 움직임벡터차의 확률을 이용하여 움직임 탐색 범위를 결정하는 새로운 고속 움직임 추정 방법을 제안한다. 제안된 방법은 일종의 적응적 탐색영역 방법이다. 움직임벡터차의 분포에 대한 조사하고 그 분포의 파라미터를 최대우도추정방법으로 추정한다. 이 추정된 분포를 이용하여 탐색 범위는 움직임벡터차의 지정된 확률에 의해 효과적으로 제한될 수 있음을 보인다. 실험적으로 제안된 방법의 성능이 화질면에서 전역탐색법과 유사하며 복잡도 면에서는 중대한 감소를 입증하였다. 뿐만 아니라, 제안된 방법이 기존의 적응적 탐색영역 방법에 비해 훨씬 더 효과적으로 탐색영역을 결정함을 보여주었다. This paper presents a new fast motion estimation method where search ranges are determined by the probabilities of motion vector differences (MVDs), which is an adaptive/dynamic search range (ASR) method. The MVDs' distribution is investigated and its parameter is estimated by the maximum likelihood estimator. With the estimated distribution, we show that the search ranges can be efficiently restricted by a prefixed probability for MVDs. Experimental results showed that the performance of the proposed method is very similar to that of the full search algorithm in PSNR but it enables significant reduction in the computational complexity. In addition, they revealed that the proposed method determine the search ranges much more efficiently than the conventional ASR methods.