An efficient hybrid search algorithm for fast block matching in video coding

Many suboptimal motion vector search algorithms have been proposed because the full search algorithm, which is an optimal method, requires huge computational requirements. These algorithms find motion vectors simply from the center of the search window. In this paper we propose an efficient motion vector search algorithm (GLS), which exploits the global motion information obtained from the previous three frames and the local motion information regarding the motion vectors of the neighboring blocks of the current block in order to predict the initial search point. GLS searches for a motion vector from this initial search point, instead of the center of the search window, using either the diamond search algorithm (DS) or the unrestricted small diamond search algorithm (USDS) which performs its search always with a smaller diamond search pattern. USDS has the same search pattern as that of the last search step in DS. Experimental results show that GLS is a faster and more accurate motion vector search method than other suboptimal methods.

In block motion estimation, a search pattern with a different shape or size has a very important impact on search speed and distortion performance. A square-shaped search pattern is adopted in many popular fast algorithms. Recently, a diamond-shaped search pattern was introduced in fast block motion estimation and has exhibited a faster search speed. Based on an in-depth examination of the influence of the search pattern on speed performance, we propose a novel algorithm using a hexagon-based search pattern to achieve further improvement. The hexagon-based search pattern is investigated in comparison with diamond search pattern and demonstrates significant speedup gain over the diamond-based search. Analysis shows that a speed improvement rate of the hexagon-based search (HEXBS) algorithm over the diamond search (DS) algorithm can be over 80% for locating some motion vectors in certain scenarios. In short, the proposed HEXBS algorithm can find the same motion vector with fewer search points than the DS algorithm. Generally speaking, the larger the motion vector, the more search points the. HEXBS algorithm can save, which is further justified by experimental results.

In this paper, a modified diamond search (MDS) algorithm is proposed for fast motion estimation based on the well known diamond search (DS) algorithm. A set of computationally efficient algorithms that can be applied to any block matching algorithm and is applied to the DS as a study case achieves higher complexity reduction than DS algorithm without further relative PSNR (peak signal to noise ratio) degradation compared to full search (FS). First, dynamic internal stop search (DISS) algorithm is used to reduce the internal redundant SAD (sum of absolute difference) operations between the current and the candidate blocks using an accurate dynamic threshold. Second, a dynamic external stop search (DESS) greatly reduces the unnecessary operations by skipping all the irrelevant blocks in the search area. In addition, early search termination and adaptive pattern selections techniques are applied to the proposed MDS as initialization steps to achieve even higher complexity reduction. The accuracy of the proposed model threshold equations guarantee not to fall into a local minima. Experiments show that the proposed MDS algorithm reduces the computations greatly up to 99% and 20% compared with the conventional FS algorithm and DS respectively with no significant degradation in both the PSNR and the bit-rate.

H.26L is a new recommendation for moving picture coding proposed by ITU-T, in which 1/4-pel and 1/8-pel fractional pixel motion compensation is added to achieve more accurate motion description and higher compression efficiency. However, at the same time the complexity of the fractional pixel motion estimation also increases, requiring a total of 24 fractional-pel positions to be checked in 1/8-pel motion vector search, while some fast integer pixel motion search algorithms have decreased the searching pixels to about ten. Therefore, reducing the computational load for fractional pixel motion search is both necessary and significant. A novel paraboloid prediction based fractional pixel search algorithm is first proposed in this paper. In any case (1/2 pixel, 1/4 pixel or 1/8 pixel resolution), roughly a computation reduction of 62.5% in fractional pixel motion estimation can be achieved compared with the full fractional pixel motion search algorithm. Experimental results prove that the strategy keeps good performance in preserving image quality and has little influence on the bit rate.

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

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.

The overlapped block motion compensation (OBMC) scheme provides an effective way to reduce block artifacts in video coding, and is adopted as an optional mode in the H.263 standard. However the motion vector search complexity increases significantly due to the motion interdependency of OBMC. In this work, we present a new OBMC motion vector search algorithm which reduces the computational complexity while preserving the decoded video quality. Unlike other iterative algorithms previously developed, our proposed algorithm requires no iteration during the motion vector search process. Furthermore, a fast search algorithm is adopted to reduce the search complexity. It is demonstrated by extensive experimental results that the proposed algorithm reduces a substantial amount of complexity and achieves a similar PSNR result in comparison with the existing iterative methods.

This paper proposes a parallel architecture for a successive elimination algorithm (SEA), which is used in block matching motion estimation. SEA effectively eliminates the search points within the search window and thus decreases the number of matching evaluation instances that require very intensive computations compared to the standard full search algorithm (FSA). The proposed architecture for SEA decreases the time to calculate the motion vector by 57 percent compared to FSA. The performance while applying the SEA to several standard video clips has been shown to be same compared to the standard FSA. The proposed architecture uses 16 processing elements accompanied with use of intelligent data arrangement and memory configuration. A technique for reducing external memory accesses has also been developed. The proposed architecture for SEA provides an efficient solution for applications requiring real-time motion estimations. For, it serves to compute motion vectors in less amount of time while requiring almost same power and some increase in area compared to a similar architecture for implementing the full search algorithm. A register-transfer level implementation as well as simulation results on benchmark video clips are presented. Relevant design statistics on area and power for comparing between SEA and FSA implementations are also provided.

In this paper two existing algorithms named as Cross-Diamond Search algorithm CDS and Fast Objected- Base Efficient Three Step Search algorithm are used. The cross-diamond search algorithm employs two diamond search patterns (a large and small) and a halfway-stop technique. It finds small motion vectors with fewer search points than the DS algorithm while maintaining similar or even better search quality. The efficient Three Step Search (E3SS) algorithm requires less computation and performs better in terms of PSNR. Modified objected block-base vector search algorithm (MOBS) fully utilizes the correlations existing in motion vectors to reduce the computations. Fast Objected- Base Efficient (FOBE) Three Step Search algorithm combines E3SS and MOBS. By combining these two existing algorithms CDS and MOBS, a new algorithm is proposed with reduced computational complexity without degradation in quality.

This paper investigates a novel motion vector outlier rejection method based on using mean shift clustering on block motion vectors. The accuracy of compressed domain global motion estimation techniques is largely influenced by its ability to counter the outlier motion vectors. These outliers occur in the block motion vector field due to moving objects, noise or due to large matching errors as a result of the encoders priority on rate distortion optimization. In the present work it is shown that by using mean shift clustering on block motion vectors, those clusters which correspond to outlier motion vectors can be identified. Once detected these clusters are kept out of the global motion estimation process thereby increasing the robustness of estimated camera parameters. The proposed method is compared with existing state-of-the-art outlier removal methods using synthetic and real video sequences to establish and validate its superiority.

This paper proposes a new and efficient algorithm for block matching motion estimation. It reduces search times and improves performance of motion estimation. Instead of using the fixed first search point to be alike previous search algorithms to search motion vector, the proposed method finds more accurate first search point as to compensating search area using temporal correlation of motion vector. Therefore, the proposed method could reduce search times and improve the accuracy of motion estimation. The main idea of proposed method is based on the property of consistent directivity and center-biased distributions of motion vector. Simulation results show that the PSNR values are improved up to the 3.4 dB as depend on the image sequences and advanced about 1.5 dB on an average. The results of the comparison show that the performance of the proposed algorithm is better than those of other fast search algorithms whether the image sequence contains fast or slow motion, and is similar to the performance of the Full Search algorithm. Simulation results also show that the performance of the proposed scheme gives better subjective picture quality than the other fast search algorithms.

Motion estimation is a core process within video coding schemes, because it enables the transmission and storage of video signals, while using a lower bit rate. Various methods have been proposed for motion estimation. A full search algorithm is considered to be the optimal method, but it suffers from a high computational burden. In order to reduce the computations, this paper proposes an adaptive two-step edgebased partial distortion search algorithm. The proposed algorithm reduces computations by reducing the total search point and adjusting the search range. The proposed algorithm is 147 times faster than full search (FS), 12 times faster than normalized partial distortion search, and 1.75 times faster than a two-step edge based partial distortion search (TS-EPDS). It also shows a high video quality, in comparison with FS, in terms of PSNR. The proposed algorithm is suitable for real-time implementation of high-quality digital video applications.

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.

The test sequencing problem is optimized to reduce test cost and ensure the performance of Fault detection and diagnosis (FDD), which is the intersection of FDD and Design for Testability (DFT). Considering the false and missing alarm of complex system diagnosis, this paper proposed an optimal test sequence strategy for fault diagnosis under unreliable test based on the quasi-depth first search (QDFS) algorithm. Firstly, the method established a heuristic evaluation function considering fault diagnosis abilities, information entropy, test cost and the reliability of the test. Then, the quasi-depth first search algorithm is used to build the fault diagnosis strategy. The theory and experiments demonstrate that the cost based on the QDFS method is much better than greed algorithm. Therefore, the proposed method could be used to design the strategy for fault diagnosis under unreliable test.

The recently developed video compression standard, H.264/AVC surpasses the performance of previous video standards, such as MPEG-2, MPEG-4(part2), and H.263 and is therefore expected to be selected as the video standard for most digital video applications. The widely distributed infrastructure, however, continues to use the previous standards. Heterogeneous video transcoding offers a significant key to the resolution of this problem. This paper suggests a new algorithm for H.264/AVC to MPEG-2 transcoding that uses motion vector clustering to reduce the computation time with no loss of quality. Such a clustering method can reduce the number of candidate motion vectors that are gathered during the H.264 decoding stage. These candidate motion vectors consider the correlation between the direction and distance of the motion vectors in the variable blocks in H.264/AVC. The candidate motion vector that has the least distortion is then selected in the MPEG-2 encoder. The MPEG-2 encoder can therefore use the best motion vector without carrying out computations for motion estimation. The experimental results show that the proposed method can maintain a good level of video quality while reducing the computational complexity by a considerable 64%, on average, compared to a cascade transcoder.