Multilevel Successive Elimination Algorithm Research Articles

Multi-level rate-constrained successive elimination algorithm tailored to suboptimal motion estimation in HEVC

In the context of motion estimation for video coding, successive elimination algorithms (SEAs) significantly reduce the number of candidates evaluated during motion estimation without altering the resulting optimal motion vector. Nevertheless, SEA is often only used in conjunction with exhaustive search algorithms (e.g., full search). In this paper, we combine the multi-level successive elimination algorithm (ML-SEA) and the rate-constrained successive elimination algorithm (RCSEA) and show that they can be advantageously applied to suboptimal search algorithms. We demonstrate that the savings brought about by the new multi-level RCSEA (ML-RCSEA) outweigh the pre-computational costs of this approach for the Test Zonal (TZ) Search algorithm found in the HM reference encoder. We propose a novel multi-level composition pattern for performing RCSEA on an asymmetric partitioning. We introduce a double-check mechanism for RCSEA, and show that on average, it avoids computing 71% of motion vector (MV) costs. We also apply the proposed ML-RCSEA to bi-predictive refinement search and leverage a cost-based search ordering to remove 56% of error metric computations, on average. When compared to the HM reference encoder, our experiments show that the proposed solution reduces the TZ Search time by approximately 45%, contributing to an average encoding time reduction of about 7%, without increasing the Bjøntegaard delta rate (BD-Rate).

Efficient motion estimation using two-bit transform and modified multilevel successive elimination

Reduced bit representations of video sequences are employed in order to mitigate the computational complexity of block matching algorithms. The idea is to explore computationally cheaper matching criteria than the conventional sum of absolute difference or fuzzy theory. Such low cost matching criteria result in substantial time saving and can therefore achieve real time video compression. To reduce the computational burden of such algorithms even further, we used them in orthogonal to the algorithms which reduces the number of search points in the search window. The speedup in computation as a result of using two-bit transform is rectified by imposing a modified version of multilevel successive elimination algorithm in orthogonal form. Experimental results show that the numbers of search points can be reduced by an average of 95% for sequences with low motion content and by an average of 85% for sequences with relatively high motion content. For all the results the same peak-signal-to-noise ratio of the reconstructed frames is maintained as that of the two-bit transform.

Fast motion estimation algorithm using multilevel distortion search in Walsh–Hadamard domain

Block-matching motion estimation (BME) can efficiently reduce the temporal redundancy between the successive video sequences in video compression coding system. In this study, a fast BME algorithm using multilevel distortion search in Walsh–Hadamard domain is proposed to reduce the computational burden and speed up coding process. First, the proposed algorithm divides the block into several sub-blocks. Then, the Walsh–Hadamard transform is applied to these sub-blocks. Finally, the proposed algorithm calculates the partial block matching distortion by utilising a novel back diagonal search scheme which can quickly reject unnecessary candidate block in a multilevel manner. Experimental results show that the proposed algorithm effectively reduces the number of operations in block distortion calculation meanwhile maintains the best motion estimation matching quality. Compared with the full search, the proposed algorithm can reduce 87.19% computational complexity without any degradation of the peak signal to noise ratio. In addition, compared with the partial distortion search algorithm, successive elimination algorithm, multilevel successive elimination algorithm and the transform-domain successive elimination algorithm, the proposed algorithm can also save 68.27, 70.09, 37.81 and 37.44% computational complexity, respectively. Moreover, the proposed algorithm can also be easily incorporated into any block-based template search motion estimation algorithm.

Simple low-dimensional features approximating NCC-based image matching

This paper proposes new low-dimensional image features that enable images to be very efficiently matched. Image matching is one of the key technologies for many vision-based applications, including template matching, block motion estimation, video compression, stereo vision, image/video near-duplicate detection, similarity join for image/video database, and so on. Normalized cross correlation (NCC) is one of widely used method for image matching with preferable characteristics such as robustness to intensity offsets and contrast changes, but it is computationally expensive. The proposed features, derived by the method of Lagrange multipliers, can provide upper-bounds of NCC as a simple dot product between two low-dimensional feature vectors. By using the proposed features, NCC-based image matching can be effectively accelerated. The matching performance with the proposed features is demonstrated using an image database obtained from actual broadcast videos. The new features are shown to outperform other methods: multilevel successive elimination algorithm (MSEA), discrete cosine transform (DCT) coefficients, and histograms, achieving very high precision while only slightly sacrificing recall.

2:1 Candidate Position Subsampling Technique for Fast Optimal Motion Estimation

The candidate position subsampling technique (CPST) basically chooses candidates in a search window at a sampling rate. The 2:1 CPST chooses half the candidates, and then selects one or more candidates that are considered as to be close to the optimal motion vector before conducting a fine search. The fine search is conducted by checking four neighbors of the chosen candidate(s) referred to as winner(s). The CPST can be combined with a fast optimal block-matching algorithm, such as the multilevel successive elimination algorithm (MSEA), in order to reduce the number of computations used in rejecting the nonbest candidate. We propose a new 2:1 CPST fitted to the MSEA. The proposed algorithm adopts a new condition for the winner which helps to find the best candidate efficiently. Moreover, a fast motion estimation step is used to reduce the number of computations of the MSEA, and the peak signal-to-noise ratio (PSNR) compensation step is adopted to guarantee that the PSNR performance of the proposed algorithm is very close to that of the full search. Experimental results show that the proposed algorithm reduces the computational loads of the MSEA by 47.26% on average with only -0.027 dB PSNR degradation in the worst case.

Fast Optimal Motion Estimation Based on Gradient-Based Adaptive Multilevel Successive Elimination

In this paper, we propose a fast and optimal solution for block motion estimation based on an adaptive multilevel successive elimination algorithm. This algorithm is accomplished by applying a modified multilevel successive elimination algorithm (SEA) with the elimination order determined by the sum of the gradient magnitudes of each subblock and the elimination process terminated by comparing the above sum with a threshold. In addition a fast approximate motion estimation method and the accumulated distortion scheme are employed to make the proposed algorithm even more efficiently. Experimental results show that the proposed adaptive multilevel successive elimination strategy (AdaMSEA) algorithm significantly outperforms other previous optimal motion estimation algorithms, including SEA, MSEA, and FGSE on a wide variety of video sequences. Finally, we modify the proposed AdaMSEA to an approximate motion estimation algorithm to achieve very fast computational speed, and the experimental results show superior performance of this approximate algorithm over some fast motion estimation algorithms.

Adaptive Fast Full Search Algorithm using Partitioned Region and Optimized Search Order

In this paper, an adaptive fast full search algorithm is proposed. The fast full search algorithm only reduces the complexity of the block matching algorithm used for motion estimation of video coding. Since the spiral search method used in conventional algorithms starts searching at the center of the search range and then moves the candidate position to estimate motion pixel by pixel, it is best for slow motion pictures. The spiral search method usually has better performance than a full search using the raster scan search method. However, the proposed algorithm is best for sequences which have numerous rapid motions. In the first of two proposed stages, using a predicted motion vector, we determine the region search order based on gradient distances among all regions after partitioning the search range. In the second stage, by means of region order, the matching error of all candidate positions is calculated in an optimized search order which is derived from the local image complexity of the current macro-block to search. The proposed algorithm, based on a multilevel successive elimination algorithm, reduces the complexity of motion estimation up to 96.6% on average, compared with the conventional full search algorithm without any loss of image.

Lossy Strict Multilevel Successive Elimination Algorithm for Fast Motion Estimation

This paper presents a simple and effective method to further reduce the search points in multilevel successive elimination algorithm (MSEA). Because the calculated sea values of those best matching search points are much smaller than the current minimum SAD, we can simply increase the calculated sea values to increase the elimination ratio without much affecting the coding quality. Compared with the original MSEA algorithm, the proposed strict MSEA algorithm (SMSEA) can provide average 6.52 times speedup. Compared with other lossy fast ME algorithms such as TSS and DS, the proposed SMSEA can maintain more stable image quality. In practice, the proposed technique can also be used in the fine granularity SEA (FGSEA) algorithm and the calculation process is almost the same.

Predictive fine granularity successive elimination for fast optimal block-matching motion estimation

Given the number of checking points, the speed of block motion estimation depends on how fast the block matching is. In this paper, a new framework, fine granularity successive elimination (FGSE), is proposed for fast optimal block matching in motion estimation. The FGSE features providing a sequence of nondecreasing fine-grained boundary levels to reject a checking point using as little computation as possible, where block complexity is utilized to determine the order of partitioning larger sub-blocks into smaller subblocks in the creation of the fine-grained boundary levels. It is shown that the well-known successive elimination algorithm (SEA) and multilevel successive elimination algorithm (MSEA) are just two special cases in the FGSE framework. Moreover, in view that two adjacent checking points (blocks) share most of the block pixels with just one pixel shifting horizontally or vertically, we develop a scheme to predict the rejection level for a candidate by exploiting the correlation of matching errors between two adjacent checking points. The resulting predictive FGSE algorithm can further reduce computation load by skipping some redundant boundary levels. Experimental results are presented to verify substantial computational savings of the proposed algorithm in comparison with the SEA/MSEA.

Fast Full-Search Motion Estimation Based on Multilevel Successive Elimination Algorithm

An improved fast full-search algorithm for motion estimation is proposed. The computational process of the sum of absolute difference and the multilevel successive elimination algorithm (MSEA) are theoretically analysed. Based on these analyzes, a new detection criterion and an estimation of the starting level are proposed in order to reduce the computations required for judging invalid candidate blocks. The simulation results show that the proposed algorithm achieves a computational timesaving of about 40% compared with the MSEA.

Improved multilevel successive elimination algorithm using efficient decision conditions

The computational process of the sum of the absolute difference (SAD) and the multilevel successive elimination algorithm (MSEA) are theoretically analyzed, and a new improved MSEA is proposed. The proposed algorithm reduces the computations required for judging invalid candidate blocks. The simulation results show that the proposed algorithm achieves a computational time savings of about 30 to 40% without additional hardware, compared to the MSEA.

Efficient multilevel successive elimination algorithms for block matching motion estimation

The authors present fast algorithms to reduce the computations of block matching algorithms, for motion estimation in video coding. Efficient multilevel successive elimination algorithms are based on the multilevel successive elimination. Efficient multilevel successive elimination algorithms consist of four algorithms. The first algorithm is given by the sum of absolute difference between the sum norms of sub-blocks in a multilevel successive elimination algorithm (MSEA) using the partial distortion elimination technique. By using the first algorithm, computations of MSEA can be reduced further. In the second algorithm, the sum of absolute difference (SAD) is calculated adaptively from large value to small value according to the absolute difference values between pixels of blocks. By using the second algorithm, the partial distortion elimination in the SAD calculation can occur early, therefore the computations of MSEA can be reduced. The second algorithm is useful not only with MSEA, but also with all kinds of block matching algorithms. In the third algorithm, the elimination level of the MSEA can be estimated. Accordingly, the computations of the MSEA related to the level lower than the estimated level can be reduced. The fourth algorithm is, first of all, to search the motion vector over the half sampled search points. At the second search, the authors search the unsampled search points around the tested search points where the motion vector may exist from the first search results. The motion estimation accuracy of the fourth algorithm is nearly 100% and the computations can be greatly reduced.

An improved search algorithm for motion estimation using adaptive search order

An adaptive search order (ASO) algorithm is presented to speed up the block motion estimation in digital video coding. According to the motion trend, a table of the adaptive search order is defined. For each searching iteration, a better search order is derived and then the best matched block can be found in the early search stage. Some experimental results demonstrate the computational advantage of the proposed improved algorithm when compared to previous algorithms, such as the full search algorithm, the successive elimination algorithm, the block sum pyramid algorithm, and the multilevel successive elimination algorithm.

A multilevel successive elimination algorithm for block matching motion estimation

An efficient algorithm is proposed to reduce the computation cost of block matching algorithms for motion estimation in video coding. Based on a new insight in block matching algorithms, we extend the successive elimination algorithm to a multilevel case. By using the sum norms of the blocks and the subblocks, tighter and tighter decision boundaries can be obtained for eliminating the search positions. The efficiency of the proposed algorithm combined with the full search algorithm and several fast search algorithms is verified by simulation results.