Adaptive two-step edge-based partial distortion search algorithm using motion vector prediction

Quality control for real-time video applications, such as the very-low-bit-rate video conferencing or the high quality video entertainment, is usually absent from many traditional fast block motion estimators. A novel block-matching algorithm for fast motion estimation, named adjustable partial distortion search algorithm (APDS), is proposed. It is a new normalized partial distortion comparison method with adjustability on prediction accuracy against searching speed using a quality factor k. When k is set to 0, APDS could act as the normalized partial distortion search algorithm (NPDS). When k is set to 1, APDS perform as the conventional partial distortion search algorithm (PDS) and could give the best quality as obtained in the full search algorithm (FS). In addition, it uses a halfway-stop technique with progressive partial distortion (PPD) to increase the chance of rejection of impossible candidate motion vectors at very early stages. Simulations with PPD show that there is 24-62 times computational reduction with 0.31-0.83 dB PSNR performance degradation, as compared to the FS algorithm. Experimental results show that APDS could provide PSNR performance very close to the full search algorithm with speedup ratios of 18 times, and to NPDS with 32 times, respectively, as compared to the FS algorithm.

The growing popularity of high resolution video and the continuously increasing demands for high quality video on mobile devices are producing stronger needs for more efficient video encoder. Concerning these desires, HEVC, a newest video coding standard, has been developed by a joint team formed by ISO/IEO MPEG and ITU/T VCEG. Its design goal is to achieve a 50% compression gain over its predecessor H.264 with an equal or even higher perceptual video quality. Motion Estimation (ME) being as one of the most critical module in video coding contributes almost 50%-70% of computational complexity in the video encoder. This high consumption of the computational resources puts a limit on the performance of encoders, especially for full HD or ultra HD videos, in terms of coding speed, bit-rate and video quality. Thus the major part of this work concentrates on the computational complexity reduction and improvement of timing performance of motion estimation algorithms for HEVC standard. First, a new strategy to calculate the SAD (Sum of Absolute Difference) for motion estimation is designed based on the statistics on property of pixel data of video sequences. This statistics demonstrates the size relationship between the sum of two sets of pixels has a determined connection with the distribution of the size relationship between individual pixels from the two sets. Taking the advantage of this observation, only a small proportion of pixels is necessary to be involved in the SAD calculation. Simulations show that the amount of computations required in the full search algorithm is reduced by about 58% on average and up to 70% in the best case. Secondly, from the scope of parallelization an enhanced TZ search for HEVC is proposed using novel schemes of multiple MVPs (motion vector predictor) and shared MVP. Specifically, resorting to multiple MVPs the initial search process is performed in parallel at multiple search centers, and the ME processing engine for PUs within one CU are parallelized based on the MVP sharing scheme on CU (coding unit) level. Moreover, the SAD module for ME engine is also parallelly implemented for PU size of 32×32. Experiments indicate it achieves an appreciable improvement on the throughput and coding efficiency of the HEVC video encoder. In addition, the other part of this thesis is contributed to the VLSI architecture design for finding the first W maximum/minimum values targeting towards high speed and low hardware cost. The architecture based on the novel bit-wise AND scheme has only half of the area of the best reference solution and its critical path delay is comparable with other implementations. While the FPCG (full parallel comparison grid) architecture, which utilizes the optimized comparator-based structure, achieves 3.6 times faster on average on the speed and even 5.2 times faster at best comparing with the reference architectures. Finally the architecture using the partial sorting strategy reaches a good balance on the timing performance and area, which has a slightly lower or comparable speed with FPCG architecture and a acceptable hardware cost

This paper proposes a motion estimation scheme to reduce the computational complexity of multilayer motion estimation for scalable video coding. Based on the result of the motion estimation of the lower resolution layer referred to as base layer, we developed a new approach for exploring the search range of the enhancement layer with high coding efficiency. This approach is based on the activity defined as the absolute difference between the motion vector predictor and the final motion vector. Based on the correlation of the activities between neighboring layers, an inter-layer activity model was developed using a curve-fitted linear equation to exploit the activity in the base layer for deciding the search center and the search range of the enhancement layer. Each activity pair in the neighboring layers is used to associate the relevant macroblock to one of two groups; boundary region and interior region. The base-layer motion vector predictor is basically selected over all the activity regions; for each activity region, the proposed motion estimation algorithm decides whether to include the median motion vector predictor or not. Minimal sufficient search range is also decided from the inter-layer activity prediction factor that is adjusted to the given sequence. The proposed scheme reduced the execution time of motion estimation by 99.26% at the cost of 1.56% bit-rate increase and 0.048 dB peak signal-to-noise ratio (PSNR) decrease on average compared with the conventional full-search algorithm. The fast full-search block matching algorithm can also be incorporated to obtain the extra CPU time reduction in the motion estimation process. By adopting the fast full-search block matching algorithm (FFSBMA) in JSVM reference software, the CPU time was reduced by up to 91.84% and the memory bandwidth was reduced by 90% at the sacrifice of 1.27% bit-rate increase and 0.041 dB PSNR decrease on average compared with the FFSBMA only.

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.

A new and fast motion-estimation algorithm based on partial block distortion using sorted significant features including bit-plane and mean is proposed. The proposed algorithm can obtain relatively accurate motion vectors with a reduced computational load. Simulation results show that the proposed method achieves its MSE performance very close to the full search method, while requiring only 6-8% of the computation needed by the full search. Furthermore, the performance of our method is better than other algorithms based on partial block distortion search.

In order to optimize the delay of data transmission and the routing efficiency of routing search algorithm, an adaptive routing search algorithm for SRIO network based on chaotic ant colony algorithm is proposed. This paper analyzes the principle of SRIO network transfer transaction, and selects routing information by exchanging properties of SRIO network packets. Using chaotic ant colony algorithm, the LEACH protocol of SRIO network is established to search the best relay node adaptively, and the SRIO network routing adaptive search algorithm is designed. Experimental results show that the proposed algorithm can effectively reduce the delay of data transmission and optimize the routing efficiency.

H.264/AVC is the outstanding and significant video compression standard developed by ITU-T/ISO/IEC Joint Video Team (JVT). Motion estimation (ME) plays a key role in H.264/AVC, it concerns greatly on computational complexity especially when using the full search (FS) algorithm. Although many fast ME algorithms have been proposed to reduce the huge calculation complexity instead of FS, the ME still can not satisfy the critical real-time applicationpsilas needs. In this paper, a fast integer pixel variable block motion estimation algorithm based on JVT which accepted UMHexagonS algorithm is presented for H.264/AVC encoder. With special considerations on the motion activity of the current macro-block, several techniques, i.e., adaptive search strategies have been utilized to significantly improve the video coding performance. The simulation results analysis shows that the proposed algorithm maintains an unnoticeable quality loss in terms of PSNR on average compared with FS and reduces nearly 20% motion estimation time compared with UMHexagonS while maintaining coding efficiency.

To reduce the amount of computation of the full search (FS) algorithm for fast motion estimation, we propose a new and fast matching algorithm without degradation of predicted images as in conventional FS. The computational reduction without any degradation in the predicted image comes from fast removal of impossible motion vectors. We obtain faster removal of inappropriate motion vectors using efficient matching units from the localization of complex area in image data. In this paper, we show three properties in block matching of motion estimation. Experimentally, we reduce unnecessary computations by about 30% with our algorithm compared with the conventional fast matching scan algorithm.

An adaptive and fast fractional pixel search algorithm in H.264

Mechanically stabilized earth walls are among the most commonly used soil-retaining structural systems in the construction industry. This study addresses the optimum design problem of mechanically stabilized earth walls using a recently developed metaheuristic optimization algorithm, namely adaptive dimensional search. For a cost efficient design, different types of steel reinforcement as well as reinforced backfill soil are treated as discrete design variables. The performance of the adaptive dimensional search algorithm is investigated through cost optimization instances of mechanically stabilized earth walls under realistic design criteria specified by standard design codes. The numerical results demonstrate the efficiency and robustness of the adaptive dimensional search algorithm in minimum cost design of mechanically stabilized earth walls and further highlight the usefulness of design optimization in engineering practice.

This paper presents a different types of block matching algorithms using motion estimation are evaluated where the effects of the macro block size will be reviewed that range from the very basic Full Search Algorithm to the recent Adaptive Rood Pattern Search Algorithm. Seven different types of block matching algorithms are implemented. Each algorithm is evaluated using different video sequences and comparisons are made through computation time and peak signal noise ratio (PSNR). The simulation results showed that the ARPS algorithm is very effective in reducing the computations time and achieves very good peak signal to noise ratio values. Thus ARPS algorithm is more efficient than the conventional searching algorithms for video compression standard. The algorithms that are evaluated in this paper are widely accepted by the video compressing community and have been used in implementing various standards, ranging from MPEG1 / H.261 to MPEG4 / H.263.

This paper presents a new motion estimation (ME) strategy for video coding. Such a strategy makes use of stopping rules in the ME process, which considers both the motion vector coding cost and desired minimum quality for each macroblock used. In a rate-distortion sense, the obtained results by the proposed algorithm are superior to the ones achieved with the full search (FS) algorithm. Moreover, the strategy reduces the mean number of searching points. Through simulations, by using an H.263 encoder, we verify that the proposed algorithm is more effective for video coding than the one used in the TMN11-LC (low complexity mode decision) FS implementation.

Although advanced motion vector prediction (AMVP) modes based on motion estimation (ME) are selected significantly less due to the merge mode newly adopted in the high-efficiency video coding (HEVC), integer ME (IME) still occupies a large amount of computation in HEVC because the HEVC supports a highly flexible block partitioning structure. Introduction of the merge mode in HEVC substantially affects the optimal search algorithm of IME. Therefore, this gives a chance to reduce the computational complexity of ME with marginal drop of the compression efficiency. This paper proposes a new IME algorithm that significantly reduces its search ranges. Computational complexity of the proposed algorithm is reduced without serious degradation in coding efficiency by obtaining additional accurate motion vector predictors (MVPs) in a bottom-up order and searching narrow regions around multiple MVPs. These bottom-up MVPs are obtained from the prediction units (PUs) in the coding units (CUs) in the hierarchically lower level, which share the pixel area either completely or partially with the current PU. Although the encoder should perform the proposed IME in a bottom-up order from the smallest CUs to the larger CUs, several other processes, such as fractional ME and merges mode are performed in a top-down order to exploit several fast algorithms already adopted in the HEVC test model encoder. To keep compatibility with HEVC standard, the bottom-up MVPs are utilized for only IME. The bitstream is generated using standard AMVP. According to the simulation results, it is confirmed that the proposed algorithm improves the accuracy of MVPs, which leads to the reduction of IME computational complexity up to 81.95% on average with the Bjontegaard delta bitrate of 0.39%.

Block motion estimation using full search is computationally intensive. Previously proposed fast algorithms reduce the computation by limiting the number of searching locations. This is accomplished at the expense of less accuracy of motion estimation and gives rise to an appreciably higher mean squared error (MSE) for motion compensated images. We present a new fast and efficient search algorithm for block motion estimation that produces better quality performance and less computational time compared with a three-step search (TSS) algorithm. The proposed algorithms are based on the ideas of the dithering pattern for pixel decimation, multiple-candidate for pixel-decimation-based full search, and center-based distribution of the motion vector. From the experimental results, the proposed algorithm is superior to the TSS in both quality performance (about 0.2 dB) and computational complexity (about half).

Block motion estimation using full search is computationally intensive. Previously proposed fast algorithms reduce computation by limiting the number of search locations and search directions in a predefined search region. This is accomplished at the expense of accuracy of motion estimation and a large mean squared error for motion-compensated images, especially for image sequences with large displacement and rotation. In this paper, a novel efficient search algorithm for block motion estimation is presented to produce better performance than some fast search algorithms that have been developed, such as three step search, orthogonal search, 2D-logarithmic search, four step search, and block-based gradient descent search, in large displacement and rotation image cases. The proposed algorithm is based on the notion of locally multi-scale operation, search of global minimum, and two layer search strategy. Experimental results show that the proposed algorithm produces anticipative performance while costing much less computation power than the full search algorithm.