Fast Motion Estimation Method Research Articles

An Adjacency Encoding Information-Based Fast Affine Motion Estimation Method for Versatile Video Coding

Versatile video coding (VVC), a new generation video coding standard, achieves significant improvements over high efficiency video coding (HEVC) due to its added advanced coding tools. Despite the fact that affine motion estimation adopted in VVC takes into account the translational, rotational, and scaling motions of the object to improve the accuracy of interprediction, this technique adds a high computational complexity, making VVC unsuitable for use in real-time applications. To address this issue, an adjacency encoding information-based fast affine motion estimation method for VVC is proposed in this paper. First, this paper counts the probability of using the affine mode in interprediction. Then we analyze the trade-off between computational complexity and performance improvement based on statistical information. Finally, by exploring the mutual exclusivity between skip and affine modes, an enhanced method is proposed to reduce interprediction complexity. Experimental results show that compared with the VVC, the proposed low-complexity method achieves 10.11% total encoding time reduction and 40.85% time saving of affine motion estimation with a 0.16% Bjøontegaard delta bitrate (BDBR) increase.

A New Rate-Complexity-Distortion Model for Fast Motion Estimation Algorithm in HEVC

In the high efficiency video coding (HEVC) standard, motion estimation (ME) adopts a quadtree coding structure and a larger search range to improve the coding performance. These advanced coding tools, however, dramatically increase the computational complexity. To accelerate ME, fast methods have been proposed that reduce ME complexity at the expense of rate-distortion (R-D) performance. However, none of these methods can claim their tradeoff to be optimal. In this paper, we propose an optimal fast motion estimation (FME) algorithm based on an analytical model of rate-complexity-distortion (R-C-D). We extend the traditional R-D model by introducing the ME complexity into it, which enables us to explicitly express the R-D performance under different complexity budgets. Based on the R-C-D model, the proposed FME finds the R-C-D optimized search ranges for some representative prediction units (PUs), which are then extended or refined dynamically according to motion characteristics for neighboring PUs. The proposed FME enables an R-D performance close to that of full search. When compared with the default FME method in the reference software of HEVC, the proposed fast algorithm can reduce the complexity by over $80\%$ while improving the R-D performance. Furthermore, our proposed FME algorithm is hardware friendly, as regular data flow enables high data reuse efficiency.

A Novel Search Area Data Reuse Method for Memory Access Optimization of Fast Search Motion Estimation

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.

Motion Classification-Based Fast Motion Estimation for High-Efficiency Video Coding

High efficiency video coding (HEVC), the latest video coding standard, is becoming popular due to its excellent coding performance. However, the significant gain in performance is achieved at the cost of substantially higher encoding complexity than its precedent H.264/AVC, in which motion estimation (ME) is the most time-consuming module that effectively removes temporal redundancy. Test zone search (TZS) is adopted as the default fast ME method in the reference software of HEVC; however, its computational complexity is still too high for real-time applications. Several fast ME algorithms have been recently proposed to further reduce ME complexity; however, these approaches typically lead to non-negligible performance loss. To address this problem, this paper proposes a motion classification-based fast ME algorithm. By exploring the motion relationship of neighboring blocks and the coding cost characteristic, the prediction unit (PU) is first categorized into one of three classes, namely, motion-smooth PU, motion-medium PU and motion-complex PU. Then different search strategies are carefully designed for PUs of each class according to their respective motion and content characteristics. Furthermore, a fast search priority-based partial internal termination scheme is presented to rapidly skip impossible positions that speeds up cost computation during the ME process. Extensive experimental results demonstrate that the proposed algorithm achieves as much as 12.47% and 20.25% reductions in total encoder complexity when compared with TZS under low delay P and random access configuration, respectively, with negligible rate-distortion degradation; thus, it outperforms state-of-the-art fast ME algorithms in terms of both coding performance and complexity reduction.

Fast Motion Estimation Based on Content Property for Low-Complexity H.265/HEVC Encoder

The high definition (HD) and ultra HD videos can be widely applied in broadcasting applications. However, with the increased resolution of video, the volume of the raw HD visual information data increases significantly, which becomes a challenge for storage, processing, and transmitting the HD visual data. The state-of-the-art video compression standard-H.265/High Efficiency Video Coding (HEVC) compresses the raw HD visual data efficiently, while the high compression rate comes at the cost of heavy computation load. Hence, reducing the encoding complexity becomes vital for the H.265/HEVC encoder to be used in broadcasting applications. In this paper, based on the best motion vector selection correlation among the different size prediction modes, we propose a fast motion estimation (ME) method to reduce the encoding complexity of the H.265/HEVC encoder. First, according to the prediction unit (PU) partition type, all PUs are classified into two classes, parent PU and children PUs, respectively. Then, based on the best motion vector selection correlation between the parent PU and children PUs, the block matching search process of the children PUs is adaptively skipped if their parent PU chooses the initial search point as its final optimal motion vector in the ME process. Experimental results show that the proposed method achieves an average of 20% ME time saving as compared with the original HM-TZSearch. Meanwhile, the rate distortion performance degradation is negligible.

Data reuse-based fast subpixel motion estimation for high efficiency video coding

A data reuse-based fast subpixel motion estimation (SME) method for high efficiency video coding (HEVC) is proposed. Since SME is one of the most computation-intensive tools in the encoder process, conventional research on SME focused on the reduction of the computational complexity. The applied data-reuse architecture for the design of fast SME substantially reduces computational complexity at the cost of a reasonable increase in memory bandwidth. The core of the proposed data-reuse method is the replacement of redundant computations in SME with the memory access operations of previously computed values. The proposed method was tested in the latest video coding standard, HEVC, with experimental results showing a reduction in operational complexity of ∼64.14%, and a reduction in encoding time of ∼56.13%, compared to the SME in the HEVC reference encoder.

Fast motion estimation based on enhanced constrained one‐bit transform

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.

적응적 탐색 영역을 이용한 HEVC 고속 움직임 탐색 방법

본 논문에서는 HEVC 부호화의 연산 복잡도를 줄이기 위한 고속 움직임 탐색 방법을 제안한다. 고속움직임 탐색을 위한 종래의 방법은 현재 영상과 참조 영상 간의 거리에 따라 탐색 영역의 크기를 조절하는 반면, 제안 방법은 예측 단위 간의 움직임 벡터 차의 유사성을 이용하여 예측 단위에 따라 움직임 벡터 탐색 영역을 적응적으로 조절한다. 실험을 통해 제안 방법은 random access 실험 조건에서 움직임 예측의 연산 시간을 약 10.7% 감소시키는 반면, 약 0.1%의 낮은 부호화 성능 하락을 가지는 것을 확인하였다. This paper proposes a fast motion estimation method which can reduce the computational complexity of HEVC encoding process. While the previous method determines its search range based on a distance between a current and a reference pictures to accelerate the time-consuming motion estimation, the proposed method adaptively sets the search range according to motion vector difference between prediction units. Experimental results show that the proposed method achieves about 10.7% of reduction in processing time of motion estimation under the random access configuration whereas its coding efficiency loss is less than 0.1%.

Directional Prediction CamShift algorithm based on Adaptive Search Pattern for moving object tracking

Moving object tracking is a fundamental task on smart video surveillance systems, because it provides a focus of attention for further investigation. Continuously Adaptive MeanShift (CamShift) algorithm is an adaptation of the MeanShift algorithm for moving objects tracking significantly, and it has been attracting increasing interests in recent years. In this work, a new CamShift approach, Directional Prediction CamShift (DP-CamShift) algorithm, is proposed to improve the tracking accuracy rate. According to the characteristic of the center-based motion vector distribution for the real-world video sequence, this work employs an Adaptive Search Pattern (ASP) to refine the central area search. The proposed approach is more robust because it adapts the optimal search pattern methods for the most adequate direction of the moving target. Since the fast Motion Estimation (ME) method has its own moving direction feature, we can adaptively use the most proper fast ME method to the certain moving object to have the best performance. Furthermore for estimation in large motion situations, the strategy of the DP-CamShift can preserve good performance. For the test video sequences with frame size of 320 × 240, the experimental results indicate that the proposed algorithm can have an accuracy rate of 99 % and achieve 23 frames per second (FPS) processing speed.

Model Parameters Estimation for Image Motion Compensation

Fast and optimal motion estimation method is proposed for electronic image stabilization. First, an approach for macro-block judgment is presented. Before motion vectors calculation, gradient information is analyzed, only useful reference blocks that are indispensable for accurate motion estimation are selected, by which the number of macro-blocks for subsequent calculation is reduced. Second, in the block matching, an improved SSDA is used to reduce computing cost. Finally, the affine transformation model and similarity transformation model of image motion are created and using least squares method for solving the optimal estimation of model parameters. Experimental results show the accuracy and fast computing speed of the proposed method.

다시점 비디오 부호화를 위한 고속 계층적 탐색 기법

Motion estimation (ME) that limits the performance of image quality and encoding speed has been developed to reduce temporal redundancy in video sequences and plays an important role in digital video compression. But it is computational demanding part of the encoder. Multi-view video is obtained by capturing one three-dimensional scene with many cameras at different positions. ME for Multi-view video requires high computational complexity. To reduce computational complexity and maintain the image quality, a fast motion estimation method is proposed in this paper. The proposed method uses a hierarchical search strategy. This strategy method consists of modified diamond search patten, multi gird diamond search pattern, and raster search pattern. These search patterns place search points symmetrically and evenly that can cover the overall search area not to fall into the local minimum or exploits the characteristics of the distribution of motion vectors to place the search points. Experiment results show that the speedup improvement of the proposed method over TZ search method (JMVC) can be up to 1.2 ~3 times faster while maintaining similar video quality and bit rates.

Motion Model Parameters Estimation for Electronic Image Stabilization Instrument

Fast and optimal motion estimation method is proposed for electronic image stabilization. First, an approach for macro-block judgment is presented. Before motion vectors calculation, gradient information is analyzed, only useful reference blocks that are indispensable for accurate motion estimation are selected, by which the number of macro-blocks for subsequent calculation is reduced. Second, in the block matching, an improved SSDA is used to reduce computing cost. Finally, the affine transformation model and similarity transformation model of image motion are created and using least squares method for solving the optimal estimation of model parameters. Experimental results show the accuracy and fast computing speed of the proposed method.

A fine-grain distortion and complexity aware parameter tuning model for the H.264/AVC encoder

Most existing video encoders currently used in mobile applications are unable to gracefully degrade their output quality as the battery life nears its end. In other words, they cannot manage power consumption to efficiently utilize the available power resources. To be able to effectively adapt to changes in the encoder′s software and hardware platforms, especially due to the power limitations of mobile devices, the effect of encoder parameters on the encoding quality and power consumption has to be represented using a Rate–Distortion–Complexity (R–D–C) model. Most existing R–D–C models only consider macroblock level parameters, and overlook other higher level parameters that may have a more significant impact on complexity. In this paper, the distortion and complexity of the H.264/AVC encoder is controlled considering a subset of higher level encoding parameters consisting of search range, number of reference frames, and motion vector resolution. First, the complexity of full and fast motion estimation methods is modeled in an implementation and platform independent manner. Then, using this complexity model, a common encoding parameter setting table is derived, which leads to the least amount of distortion for each complexity condition. Finally, a complexity control mechanism is proposed which tunes the encoding parameters in a real-time manner. The proposed model can be combined with other existing macroblock level models in order to design a two-phase fine grain complexity controller. Simulation results indicate that when our method is integrated with the direct resource allocation (DRA) approach, performance increases by an average of 1.02dB and 1.06dB for full and fast motion estimation approaches, respectively.

Single-Dimension Perturbation Glowworm Swarm Optimization Algorithm for Block Motion Estimation

In view of the fact that the classical fast motion estimation methods are easy to fall into local optimum and suffer the high computational cost, the convergence of the motion estimation method based on the swarm intelligence algorithm is very slow. A new block motion estimation method based on single-dimension perturbation glowworm swarm optimization algorithm is proposed. Single-dimension perturbation is a local search strategy which can improve the ability of local optimization. The proposed method not only has overcome the defect of falling into local optimum easily by taking use of both the global search ability of glowworm swarm optimization algorithm and the local optimization ability of single-dimension perturbation but also has reduced the computation complexity by using motion vector predictor and terminating strategies in view of the characteristic of video images. The experimental results show that the performance of the proposed method is better than that of other motion estimation methods for most video sequences, specifically for those video sequences with violent motion, and the searching precision has been improved obviously. Although the computational complexity of the proposed method is slightly higher than that of the classical methods, it is still far lower than that of full search method.

Fast Motion Estimation Algorithm Based on Complex Wavelet Transform

In this paper, we introduce an algorithm for motion estimation. It combines complex wavelet decomposition and a fast motion estimation method based on affine model. The principle of wavelet transform is to decompose hierarchically the input image into a series of successively lower resolution reference images and detail images which contain the information needed to be reconstructed back to the next higher resolution level. The motion estimation determines the velocity field between two successive images. This phase can be extracted from this measure descriptive information of the sequence. Motion Estimation (ME) is an important part of any video compression system, since it can achieve significant compression by exploiting the temporal redundancy existing in a video sequence. This paper described a method from calculating the optical flow of an image sequence based on complex wavelet transform. It consists to project the optical flow vectors on a basis of complex-valued wavelets. Thus, we add an additional assumption on the shape of the velocity field that we want to find, which is the affinity of the optical flow. The two-dimensional affine motion model is used to formulate the optical flow problem by coarse resolution simultaneously coarse-and-fine, beside the traditional approach by coarse-to-fine, to avoid the error propagation during the decomposition of coarse level to fine level. This method opens the way for a quick and low-cost computing optical flow.

An SAD-Based Selective Bi-prediction Method for Fast Motion Estimation in High Efficiency Video Coding

As the next-generation video coding standard, High Efficiency Video Coding (HEVC) has adopted advanced coding tools despite the increase in computational complexity. In this paper, we propose a selective bi-prediction method to reduce the encoding complexity of HEVC. The proposed method evaluates the statistical property of the sum of absolute differences in the motion estimation process and determines whether bi-prediction is performed. A performance comparison of the complexity reduction is provided to show the effectiveness of the proposed method compared to the HEVC test model version 4.0. On average, 50% of the bi-prediction time can be reduced by the proposed method, while maintaining a negligible bit increment and a minimal loss of image quality.

움직임벡터차에 기반한 고속 움직임 추정 방법

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.

Adaptive search range motion estimation using neighboring motion vector differences

This paper presents a new fast motion estimation method where search ranges are determined by the probabilities of motion vector differences (MVDs). The MVDs' distribution is investigated and its parameter is estimated by the maximum likelihood estimator. With the estimated distribution, the search ranges are efficiently restricted by a prefixed probability for MVDs. Experimental results show 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.

Gradient-based edge detection for motion estimation in H.264/AVC

This study introduces a block-based motion estimation algorithm based on projection with adaptive window-size selection. Using one-dimensional (1D) projection, the original 2D block-matching problem is translated to a simpler 1D matching problem which eliminates the majority of potential pixel participation. The authors propose an efficient fast-motion estimation method using an intensity gradient algorithm to reduce the encoding time. The proposed algorithm estimates the texture direction of the block, and then a horizontal or vertical projection is employed for the sum of absolute difference calculation. Experimental results show that the proposed algorithm achieves a greater than 60% time reduction with negligible peak signal to noise ratio (PSNR) loss and BR increments.

Region similarity based edge detection for motion estimation in H.264/AVC

This paper introduces a block-based motion estimation algorithm based on projection with adaptive window size selection. By using one-dimensional (1-D) projection the original two-dimensional (2-D) block-matching problem is translated to a simpler 1D matching, which eliminates majority of potential pixel participation. In this paper, we propose an efficient fast motion estimation method by using region similarity detection (RSD) algorithm to reduce the encoding time. The proposed RSD algorithm effectively estimates the texture direction of the block to find the direction and then horizontal, vertical or diagonal projection is employed for SAD calculation. Experimental results show that the proposed algorithm achieves more than a 60% time reduction with negligible PSNR loss and bitrate increments.