Search Algorithm For Fast Motion Estimation Research Articles

Adaptive Search Range for HEVC Motion Estimation Based on Depth Information

High Efficiency Video Coding achieves twofold coding efficiency improvement compared with its predecessor H.264/MPEG-4 Advanced Video Coding. However, it suffers from high computational complexity due to its quad-tree structure in motion estimation (ME). This paper exposes the use of depth maps in the multiview video plus depth format for relieving the computational burden. The depth map provides an intimation of the objects' distance from the projected screen in a 3D scene, which is explored in adaptive search range determination in this paper. The proposed algorithm exploits the high temporal correlation between the depth map and the motion in texture. By utilizing this correlation, a depth/motion relationship map is built for a mapping process. For each block, this forms a tailor-made search range with a motion-aware asymmetric shape to skip unnecessary search points in ME. The obtained search range can be further adjusted by taking the influence of 3D-to-2D projection into consideration. Simulation results reveal that, compared to the full search approach, the proposed algorithm can reduce the complexity by 93% on average, whereas the coding efficiency can be maintained. Besides, the proposed search range determination can work well with other fast search ME algorithms in the literature.

Motion estimation using learning automata

Block-matching algorithms (BMAs) are widely employed for motion estimation. BMAs divide input frames into several blocks and minimize an error function for each block to calculate motion vectors. Afterward, each motion vector is applicable for all of the pixels within the block. Since computing the error functions is resource intensive, many fast-search motion estimation algorithms have been suggested to reduce the computational cost. These fast algorithms provide a significant reduction in computation but often converge to a local minimum. A learning automaton is an adaptive decision-making unit that learns the optimal action through repeated interactions with its environment. Learning automata (LA) have been applied successfully to a wide range of applications including pattern recognition, dynamic channel assignment, and social network analysis. In this paper, we apply LA to motion estimation problem, which is one of the basic problems in computer vision. We compare the accuracy and performance of the suggested algorithms with other well-known BMAs. Interestingly, the obtained results indicate high efficiency and accuracy of the proposed methods. The results suggest that simplicity, efficiency, parallel nature, and accuracy of LA-based methods make them a good candidate to solve computer vision problems.

A Frequency Based Hierarchical Fast Search Block Matching Algorithm for Fast Video Communication

Numerous fast-search block motion estimation algorithms have been developed to circumvent the high computational cost required by the full-search algorithm. These techniques however often converge to a local minimum, which makes them subject to noise and matching errors. Hence, many spatial domain block matching algorithms have been developed in literature. These algorithms exploit the high correlation that exists between pixels inside each frame block. However, with the block transformed frequencies, block matching can be used to test the similarities between a subset of selected frequencies that correctly identify each block uniquely; therefore fewer comparisons are performed resulting in a considerable reduction in complexity. In this work, a two-level hierarchical fast search motion estimation algorithm is proposed in the frequency domain. This algorithm incorporates a novel search pattern at the top level of the hierarchy. The proposed hierarchical method for motion estimation not only produces consistent motion vectors within each large object, but also accurately estimates the motion of small objects with a substantial reduction in complexity when compared to other benchmark algorithms.

A novel fast search motion estimation boosted by multilayer concept

Most of the fast search motion estimation algorithms reduce the computational cost of motion estimation (ME) greatly by checking only a few search points inside the search area by using full distortion measure. This paper proposes multi-layer motion estimation (MME) which employs partial distortion as its distortion measure to reduce the number of computations involved in each search point instead of reducing the number of search points. The MME, first, constructs the layers from the reference frame so as to facilitate the calculation of partial distortion measures on the layers. Later, it searches motion vectors by computing the partial distortion measures on the layers. A layer is an image which is derived from the reference frame such that the summation of a block of pixels in the reference frame determines the point of a layer. It has been noticed on different video sequences that many motion vectors on the layers are the same as those searched on the reference frame. Experimental results on a wide variety of video sequences show that the proposed algorithm outperforms the other popular conventional fast search motion estimation algorithms computationally while maintaining the motion prediction quality very close to the full-search algorithm.

An efficient multi-layer reference frame motion estimation for video coding

Most of the fast search motion estimation algorithms reduce the computational complexity of motion estimation (ME) greatly by checking only a few search points inside the search area. In this paper, we propose a new algorithm--multi-layer motion estimation (MME) which reduces the computational complexity of each distortion measure instead of reducing the number of search points. The conventional fast search motion estimation algorithms perform ME on the reference frame with full distortion measure; on the contrary, the MME performs ME on the layers with partial distortion measures to enhance the computational speed of ME. A layer is an image which is derived from the reference frame; each macro-pixel value in the layer represents the sum of the values of the corresponding pixels in the reference frame. A hierarchical quad-tree structure is employed in this paper to construct multiple layers from the reference frame. Experimental results on different video sequences show evidence that many motion vectors have been found similar both in the reference frame and the layers. The effectiveness of the proposed MME algorithm is compared with that of some state-of-the-art fast block matching algorithms with respect to speed and motion prediction quality. Experimental results on a wide variety of video sequences show that the proposed algorithm outperforms the other popular conventional fast search motion estimation algorithms computationally while maintaining the motion prediction quality very close to the full-search algorithm. Moreover, the proposed algorithm can achieve a maximum of 97.99 % speed-improvement rate against the fast full-search motion estimation algorithms which are based on hierarchical block matching process. The proposed MME performs the motion estimation on the layers by using three types of search patterns. The derivation of these search patterns exploits the characteristic of the center-biased motion vector distribution and that of less intensive block distortion measurement of the layers.

Employing a novel cross-diamond search in a modified hierarchical search motion estimation algorithm for video compression

The large amount of bandwidth that is required for the transmission or storage of digital videos is the main incentive for researchers to develop algorithms that aim at compressing video data (digital images) whilst keeping their quality as high as possible. Motion estimation algorithms are used for video compression as they reduce the memory requirements of any video file while maintaining its high quality. Block matching has been extensively utilized in compression algorithms for motion estimation. One of the main components of block matching techniques is search methods for block movements between consecutive video frames whose aim is to reduce the number of comparisons. One of the most effective searching methods that yield accurate results but is computationally very expensive is the Full Search algorithm. Researchers try to develop fast search motion estimation algorithms to reduce the computational cost required by full-search algorithms. In this research, the authors present a new fast search algorithm based on the hierarchical search approach, where the number of searched locations is reduced compared to the Full Search. The original image is sub-sampled into additional two levels. The Full Search is performed on the highest level where the complexity is relatively low. The Enhanced Three-Step Search Algorithm and a new proposed searching algorithm are used in the consecutive two levels. The results show that by using the standard accuracy measurements and the standard set of video sequences, the performance of the proposed hierarchal search algorithm is close to the Full Search with 83.4% reduction in complexity and with a matching quality over 98%.

Hierarchical stochastic fast search motion estimation algorithm

Many fast search motion estimation algorithms have been developed to reduce the computational cost required by full-search algorithms. Fast search motion estimation techniques often converge to a local minimum, providing a significant reduction in computational cost. The motion vector measurement process in fast search algorithms is subject to noise and matching errors. Therefore researchers have investigated the use of Kalman filtering in order to seek optimal estimates. In this work, the authors propose a new fast stochastic motion estimation technique that requires 5% of the total computations required by the full-search algorithm, and results in a quality that outperforms most of the well-known fast searching algorithms. The measured motion vectors are obtained using a simplified hierarchical search block-matching algorithm, and are used as the measurement part of the Kalman filter. As for the prediction part of the filter, it is assumed that the motion vector of a current block can be predicted from its four neighbouring blocks. Using the predicted and measured motion vectors, the best estimates for motion vectors are obtained. Using standard methods of accuracy measurements, results show that the performance of the proposed technique approaches that of the full-search algorithm.

Dynamically variable step search motion estimation algorithm and a dynamically reconfigurable hardware for its implementation

Motion Estimation (ME) is the most computationally intensive part of video compression and video enhancement systems. For the recently available High Definition (HD) video formats, the computational complexity of full search (FS) ME algorithm is prohibitively high, whereas the PSNR obtained by fast search ME algorithms is low. Therefore, in this paper, we present Dynamically Variable Step Search (DVSS) ME algorithm for processing high definition video formats and a dynamically reconfigurable hardware architecture for efficiently implementing DVSS algorithm. The simulation results showed that DVSS algorithm performs very close to FS algorithm by searching much fewer search locations than FS algorithm and it outperforms successful fast search ME algorithms by searching more search locations than these algorithms. The proposed hardware is implemented in VHDL and is capable of processing high definition video formats in real time. Therefore, it can be used in consumer electronics products for video compression, frame rate up-conversion and de-interlacing.

New fast full search algorithms using DCT coefficients

This paper presents new fast full search motion estimation (ME) algorithms using discrete cosine transform (DCT) coefficients as a gradient magnitude. The proposed algorithms use intra DCT coefficients to arrange the matching scan order. The proposed algorithm 1 uses some of DCT coefficients based on the relationship between gradient magnitude and DCT coefficients, and the proposed algorithm 2 uses all of DCT alternating current (AC) coefficients in order to predict the fluctuation of a block. The proposed algorithms significantly reduce the computational complexity of the conventional algorithm while maintaining the visual quality of video.

Variable step search fast motion estimation for H.264/AVC video coder

In this paper, we propose a variable step search fast motion estimation algorithm using local statistics of neighboring motion vectors. Using the degree of correlation between neighboring motion vectors, motion search range of each macroblock is adaptively adjusted to reduce unnecessary search points. Based on the determined search range, motion vector is obtained by variable step search step. Experimental results show that comparing to fast full spiral search, fixed step, and fast variable block size motion estimation algorithms, the motion search points and computing time of proposed algorithm are dramatically reduced with acceptable loss in coding performance.

Search centre prediction-based partial distortion search motion estimation algorithm

A fast partial distortion search motion estimation algorithm based on search centre prediction is proposed. A search centre prediction scheme is first exploited and, based on it, two schemes of reducing computation complexity are presented, including early search termination and halfway search stop. Experimental results show that the proposed algorithm can achieve from 14 to 161 times speedup compared to the full search algorithm, while maintaining similar visual quality.

Bit Rate Distribution for Motion Estimation in H.264 Coding

In this paper, we analyze the bit rate distribution of the full search and the fast search motion estimation algorithms in H.264. It shows both experimentally and theoretically that a well-designed fast block-matching algorithm not only speeds up motion estimation process, but also improves the rate-distortion performance. This is in contrast to the common belief that full search always yields optimal rate-distortion performance under the same test condition.

Fast full search motion estimation algorithm using early detection of impossible candidate vectors

To reduce the amount of computations for a full search (FS) algorithm for fast motion estimation, we propose a new and fast FS motion estimation algorithm. The computational reduction of our FS motion estimation algorithm comes from fast elimination of impossible motion vectors. We obtain faster elimination of inappropriate motion vectors using efficient matching units from localization of a complex area in image data. In this paper, we show three properties in block matching of motion estimation. We suggest two fast matching scan algorithms: one from adaptive matching scan and the other from fixed dithering order. Experimentally, we remove the unnecessary computations by about 30% with our proposed algorithm compared with the conventional fast FS algorithms.

A fast full search motion estimation algorithm using sequential rejection of candidates from hierarchical decision structure

We propose a new as well as fast full search (FS) motion estimation algorithm for video coding. The computational reduction comes from sequential rejection of impossible candidates with derived formula and subblock norms. Our algorithm reduces more the computations than the FS motion estimation algorithms previously developed.

Partial norm based search algorithm for fast motionestimation

A fast block motion estimation algorithm is presented which uses the sum of partial norms that can obtain the same motion vectors as the exhaustive full search algorithm but with a reduced computational load. Experimental results show that the proposed algorithm is ~3–4 times faster than the original exhaustive full search method, and is almost twice as fast as the successive elimination algorithm (SEA).