A New Spatio-Temporal Fast Motion Estimation Algorithm

Journal of Computer Science, 2008

Block matching motion estimation was one of the most important modules in the design of any video encoder. It consumed more than 85% of video encoding time due to searching of a candidate block in the search window of the reference frame. To minimize the search time on block matching, a simplified and efficient Block Matching Algorithm for Fast Motion Estimation was proposed. It had two steps such as prediction and refinement. The temporal correlation among successive frames and the direction of the previously processed frame for predicting the motion vector of the candidate block was considered during prediction step. Different combination of search points was considered in the refinement step of the algorithm which subsequently minimize the search time. Experiments were conducted on various SIF and CIF video sequences. The performance of the algorithm was compared with existing fast block matching motion estimation algorithms which were used in recent video coding standards. The experimental results were shown that the algorithm provided a faster search with minimum distortion when compared to the optimal fast block matching motion estimation algorithms.

This paper proposes an efficient block-matching motion estimation algorithm known as prediction-based directional search (PDS). This new algorithm is applicable to a wide range of video processing applications. The algorithm uses the motion vectors in two neighboring blocks to predict a starting search point for the current block. The subsequent refining search relies on the hypothesis of monotonic block distortion surface and the center-biased characteristic of motion vector probability distribution. The cross pattern in a step and one of four possible directional rectangle search patterns in the next step are iteratively used to find the motion vector. Experiments on eleven video sequences with different characteristics shows that PDS can achieve a faster computation speed with similar or even better distortion performance compared to some existing well-known algorithms.

Pattern Recognition Letters, 2012

In video coding, research is focused on the development of fast motion estimation (ME) algorithms while keeping the coding distortion as small as possible. It has been observed that the real world video sequences exhibit a wide range of motion content, from uniform to random, therefore if the motion characteristics of video sequences are taken into account before hand, it is possible to develop a robust motion estimation algorithm that is suitable for all kinds of video sequences. This is the basis of the proposed algorithm. The proposed algorithm involves a multistage approach that includes motion vector prediction and motion classification using the characteristics of video sequences. In the first step, spatio-temporal correlation has been used for initial search centre prediction. This strategy decreases the effect of unimodal error surface assumption and it also moves the search closer to the global minimum hence increasing the computation speed. Secondly, the homogeneity analysis helps to identify smooth and random motion. Thirdly, global minimum prediction based on unimodal error surface assumption helps to identify the proximity of global minimum. Fourthly, adaptive search pattern selection takes into account various types of motion content by dynamically switching between stationary, center biased and, uniform search patterns. Finally, the early termination of the search process is adaptive and is based on the homogeneity between the neighboring blocks.

IEEE Transactions on Consumer Electronics, 2011

The new video coding standard, H.264/AVC, has achieved significant compression gain by utilizing several advanced techniques. Block-Matching Motion Estimation is one of the most important elements to reduce the motion residual. However, it results in heavy computational burden and limits the application for real-time video service. In this paper, a fast motion estimation algorithm called Simulated Annealing Adaptive Search (SAAS) is proposed to reduce the computational load. The basic idea of the proposed scheme is based on adjusting search pattern not only for each frame, but also for each block. Initially, the adaptive search pattern is performed by statistical analysis of previous frame's Motion Vector Correlation. Then the search pattern is adjusted for each block according to Predicted Motion Vector. According to motion vector correlation statistics information, search region is adaptively divided and Simulated Annealing (SA) mechanism is adopted to select search power for each region and to avoid trapping into local minima. Experimental results indicate that the proposed algorithm offers considerable improvement in computing time and motion search points at the same rate-distortion performance, compared to the conventional fast motion estimation algorithm. 1

… : NCC-2005, 28-30 January, 2005, 2005

This paper describes a computationally adaptive motion estimation algorithm by exploiting the spatial-temporal motion correlation characteristics of the video sequence. In our approach, a set of initial candidate MV's include center block and four neighboring blocks at the same location from the previous frame are used to track the motion of the current block. Among the set of initial MV candidates, the one with the smallest MAD is chosen as the reference MV candidate V R of the current block. We use the spatial correlation property between zero motion vector and reference vector V R for finding the best candidate MV and viewed as a starting point (i.e., new search center) for MV searching process. The proposed approach has been successfully tested with integer, as well as half-pixel accuracy and the results are encouraging. The experimental results reveal that the proposed algorithm saves up to 95% of computations, as compared to FSBM and the estimation accuracy is very close to that of FSBM.

2011 First International Conference on Data Compression, Communications and Processing, 2011

Block matching algorithms (BMA) are central to optimal frame prediction for motion estimation in video compression. This paper focuses on the efficiency of Hierarchical Search (HS) algorithms. The research proposed two new combinations of fast algorithms like Small Diamondshaped Search Pattern (SDSP) and Square-Shaped Search Pattern (SSSP) with a three-level Hierarchical algorithm at different levels of hierarchy. The computational complexity and efficiency for each combination algorithm were of interest. Simulation results show that the hierarchical combination algorithms are around 10% faster than the classic hierarchical algorithm with slight improvement or no significant change in video quality when compared to general HS algorithm.

Electrical and Computer …, 2010

In this paper we present a new fast motion estimation technique using spatial and temporal correlation among motion vectors. Motion vector of a block can be predicted from the motion vectors of its neighboring blocks. Statistical analyses show that the role of different neighboring blocks in the prediction process changes with time. A dynamic sorting routine is offered to let the suggested algorithm use the motion vectors of neighboring blocks that are more likely to help the search procedure. By using these motion vectors a search center is located, around which a search window is placed. The size of the search area is dependent on the number of prediction vectors that are used. Implementation results show that the proposed algorithm, while being simpler than many fast motion estimation algorithms, has compatible or superior performance.

2012 7th Colombian Computing Congress (CCC), 2012

Block-matching motion estimation is an efficient algorithm for reducing the temporal redundancy in video coding and is adopted by video coding standards. Many fast blockmatching algorithms have been devised to reduce the computational complexity without degrading the estimation quality. Seven algorithms are implemented and compared -using quality of prediction and efficiency: the full-search, the three-step search, the four-step search, the diamond search, the hexagonal block search, the multi-directional gradient descent search and the fast directional gradient descent search. The aforementioned algorithms are chosen not only because of their popularity, but also because they are rather generic and they represent different ways of cutting down the computation. The most efficient algorithms are the hexagonal block search, the multi-directional gradient descent search and the fast directional gradient descent search. The hexagonal block search produces low quality of prediction, while the two others have the best quality prediction among all analysed algorithms. Also, the hexagonal block search has shown to be less affected by the variation in the block size.

2007

The new JVT H.264 video coding standard is becoming the dominant standard, mainly due to its high coding efficiency. Among all modules in the encoder, motion estimation plays a key role since it can significantly affect the output quality of an encoded sequence. However, motion estimation requires a significant part of the encoding time. Thus, various fast motion estimation algorithms have been proposed. The H.264 reference encoder has currently adopted three fast motion estimation algorithms. These algorithms aim at reducing the search area by predicting the search points using suitable predictors. In this paper we propose a new spatio-temporal predictor and we study the effectiveness of different predictors in order to find out whether these predictors truly contribute to the decrease of motion estimation time without affecting video quality and adding undesirable complexity to the encoder.

Signal Processing: Image Communication, 1992

The conventional motion estimation algorithms used in digital television coding can roughly be classified into two categories, namely the block-matching method and the recursive method. Each of them has its own strong points. In this paper, a new type of block-based motion estimation algorithm is presented, which is based on the block-recursive (gradient) method and makes use of some of the merits of the block-matching method. For a moderate translational motion, motion estimation with a subpel precision can conveniently be obtained with only a couple of recursive searches, and for a violent or complicated motion which cannot be estimated by any block-based algorithm, the local minimum of prediction errors can always be found. Our experiments show that the proposed algorithm is efficient and reliable, and obviously superior to the conventional block-recursive algorithms and the fast block-matching algorithms. The performance of the proposed algorithm tends almost to the optimum of the full search algorithm with the same estimation precision, but the computational effort is much less than that of the full search algorithm.