Search Algorithm For Motion Estimation Research Articles

Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

In the present situation, applications related to multimedia are discovered to be comfortable with the use of video. The number of end consumers who use video continues to rise every day. People are presently searching for videos with better quality among the ones that are currently available there. This results in the launch and dissemination of HD (high definition) videos. Ultra high-definition (UHD) videos are becoming more and more popular as a result of this advancement and need. However, as video communication keeps expanding, there is an upsurge in network traffic because of the limited bandwidth, especially among smart cities. Different advancement codecs have been suggested to deal with the data stream to overcome this hazardous circumstance. However, the fact that modern UHD videos have huge amounts of data makes the available codecs even more complicated. UHD videos can be processed with the latest improvement codec, H.265/High-efficiency video coding (HEVC). Nevertheless, it is impacted by increased power consumption and intricate calculations. Limitations in the codec's functionality confine its use to specific applications, preventing its application in wireless, mobile, or portable settings. Hence, this research concentrates on implementing frame-level quality enhancement through a deep learning network known as FQE-Net. The deep learning convolutional neural network (DLCNN) is specifically crafted to manage films with resolutions up to 16K. Its primary objectives include reducing complexity, minimizing artifacts, enhancing the efficiency of the HEVC codec, and compacting energy consumption. To achieve superior efficiency, it is imperative to replace the DWT transforms within the HEVC codec with a DLCNN model. Additionally, incorporating the Content Block Search Algorithm for Motion Estimation and Compensation, alongside filtering techniques like Sample Adaptive Filter and Deblocking Filter, becomes essential. The simulation results showed that the suggested FQE-Net performed better than the conventional techniques.

Half-Way Stop Adaptive Pattern Search Algorithm for Motion Estimation and Dedicated VLSI Architecture

Half-Way Stop Adaptive Pattern Search Algorithm for Motion Estimation and Dedicated VLSI Architecture

A novel diamond–hexagon search algorithm for motion estimation

Motion estimation (ME) is initial way for video compression and associated to the compression efficacy by dipping temporal redundancies. ME is the most significant measure of a video encoder and half of coding intricacy or computational time depends on it. There were numerous ME algorithms suggested and realized to minimalize the computational time. H.264/AVC codec deals various coding method for realizing high compression gains as relate to other criterions. These methods affectedly rise the computational intricacy of the block based ME, which consumes up to 80% of the complete encoder’s computations. In this paper, we proposed diamond–hexagon search algorithm for block matching ME method. This proposed method delivers lessening in computational intricacy and encoding time without conceding the quality of the video sequence.

Compressed domain zoom motion classification using local tetra patterns

In this paper, a novel compressed domain method for classifying zooming motion is presented. Camera zoom motion classification is an important problem in video analysis wherein the task is to recognize and separate zooming-in camera from zooming-out camera. In our study, we address this problem utilizing local tetra patterns which has earlier found applications in image texture analysis and content-based image retrieval. Towards this goal we model the motion vector orientation and magnitude using local tetra patterns followed by histogram formation. Since the feature dimension is large, uniform pattern-based feature reduction is applied on the histograms to form the feature vector which is fed to the C-SVM classifier for training/testing purposes. Experimental testing utilizing standard video sequences with block motion vectors coming from exhaustive search motion estimation algorithm as well as H.264 obtained block motion vectors along with comparative analysis carried out with existing techniques shows superior performance for the proposed method.

A novel parallel implementation of partial distortion search algorithm based on template search

This paper proposes a new early terminating partial distortion search algorithm based on template search in fast motion estimation. Our algorithm accomplishes a parallel implementation technique by considering both the idea of partial distortion search and the advantage of the template search. With this technique, the proposed algorithm achieves a better performance by faster updating the current minimum distortion. The proposed parallel implementation that reduces a large amount of distortion computations between pixels outperforms the standard partial distortion search method by faster rejecting the unlikely candidate blocks. Our algorithm is embedded into the diamond search algorithm, star diamond search algorithm and three-step search algorithm respectively. According to the experiment results, it is proved that about 30% reduction of computational complexity has been obtained compared with the standard partial distortion search. Moreover, there is no degradation of the peak signal-to-noise ratio(PSNR) for motion estimation compared with the embedded block-based template search motion estimation algorithm.

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.

Enhanced zonal search algorithm for motion estimation in depth-map coding

This paper presents a novel motion estimation scheme for depth-map coding. Depth map has a couple of different characteristics compared with the common color scene, and therefore, careful investigation of the conventional fast motion search schemes is required. In this paper, I first provide the necessity of the depth-oriented motion search scheme based on experiments and then analyze the problems of the conventional methods. On the basis of the analysis, I propose to use the initial position refinement step during a motion search. In detail, the modification of the one-at-a-time search scheme is proposed for improvement of the prediction accuracy, and an initial direction selection and a 1-bit transform scheme follow for complexity reduction. The experimental results show that the proposed scheme can achieve coding gains of more than 3% on average over the state-of-the-art zonal search methods while keeping the overall complexity increase low.

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.

Adaptive Prediction based Diamond Search Algorithm (APDS) for Video Encoder of Solar Powered UAV

Aerial Vehicle (UAV) provide bird's eye view over an intersection or a large area, and provide real-time surveillance of area under observation. UAVs have been playing a vital role in disaster management due to the increased sensing and processing capabilities. This paper proposes a fast adaptive prediction based diamond search Motion Estimation (ME) algorithm for Sun Falcon 2, a solar powered UAV's video encoder to cope the computational complexity, low power and increased quality of ME process requirement. Results show that the proposed Adaptive Predict Diamond Search (APDS) ME algorithm performs best in the term of PSNR, MSE and number of Search Points (SP), for approximately all the video sequences. Moreover, performance of APDS is decreased a little bit in term of number of SP when compared to Hexagon search algorithm but its PSNR is still considerably high for those video sequences. The average PSNR improvement rate of APDS is 0.62, 2.67, 0.82, 0.83 and 2.31 for Diamond Search (DS), HexBS, FHS, FSS and MDS respectively, while the average SIR is 25.4404, 6.3374, 48.274 and 205.55 for DS, FHS, FSS and MDS respectively.

Efficient VLSI design of adaptive rood pattern search algorithm for motion estimation of high definition videos

Block-based motion estimation plays a significant role in video codecs by exploiting and reducing the temporal redundancies that exist between consecutive frames in a video sequence. Adaptive Rood Pattern Search (ARPS) is one of the most popular fast motion estimation algorithms. In this paper, VLSI design for the ARPS algorithm is proposed that involves reasonably limited hardware resource without compromising the real-time speed for transmitting HD videos. To tackle the adaptive nature of the algorithm, the proposed design avoids systolic arrays and introduces novel pattern generation methodology that can tackle the adaptive nature of the algorithm. Further, the design incorporates interleaved memory organization with a well–defined sharing strategy to re-use data and ensures high throughput. Working at a frequency of 112MHz, the present design can process 30 Full HD 1080p (1920×1080) frames using only 47.15K gates. Hence, the proposed VLSI architecture can be incorporated in video codecs that can be suitably used in devices like camcorders, tablets and smart phones.

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.

Content-Aware Fast Motion Estimation Algorithm for IoT Based Multimedia Service

Various applications based on IoT real-time multimedia are under the spotlight. To implement real-time multimedia service, motion estimation in video compression service has a high computational complexity. In this paper, an efficient motion search method based on content awareness is proposed consisting of three steps. The first step is motion classification using the center position cost distribution. The second step is calculation of a predictor based motion classification. The third step is setting the arm size of the search pattern based on adaptive use of the distance between the predictor and the center position. Experimental results show that the proposed algorithm achieves speed-up factors of up to 48.57% and 16.03%, on average, with good bitrate performance, compared with fast integer-pel and fractional-pel motion estimation for H.264/AVC (UMHexagonS), and an enhanced predictive zonal search for single and multiple frame motion estimation (EPZS) methods using JM 18.5, respectively. In addition, the proposed algorithm achieves a speed-up factor of up to 42.61%, on average, with negligible bitrate degradation, compared with the TZ search motion estimation algorithm for the multiview video coding (TZS) method on HM 10.0.

A fast partial distortion search algorithm for motion estimation based on the multi-traps assumption

Full search has the best matching accuracy but costs the most time, while fast search algorithms can achieve a high speed but are easy to be trapped in local minimums. To compensate the shortcomings of the existing algorithms, this paper proposes a fast partial distortion motion estimation algorithm based on the multi-traps assumption (MT-PDS). It mainly consists of three steps: (1) estimate the number of traps in the search area, (2) obtain the positions of the traps by using the k-th (k=0,1,2,…15) partial distortion, which contributes most to the true sum of absolute difference (SAD), to perform the coarse search, and (3) get the positions of the deepest traps and search around them to get the global minimum. Besides, the proposed algorithm also introduces an adaptive search method and a sparse search pattern, which further reduce the computations. Experimental results show that the proposed MT-PDS is about 160 times faster than the full search on average; and the speed-up can achieve over 180 times for the low motion contents. What is more, it only degrades the quality by −0.0178dB and slightly increases the bit rate by 0.735%, which can be considered ignorable. Those advantages make the MT-PDS a very useful tool in real time applications, such as video compression, pattern recognition, target tracking, etc.

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.

Hardware-friendly HEVC motion estimation: new algorithms and efficient VLSI designs targeting high definition videos

In this article the spread and iterative search (S&IS) and the low density and iterative search (LD&IS) motion estimation algorithms are presented. The proposed algorithms are hardware-friendly because they have a regular number of cycles to encode a block, they have a regular memory access and the parallelism can be better explored when compared to other published solutions. The proposed algorithms were evaluated under the high efficiency video coding reference software and compared with the state-of-art enhanced predictive zonal search (EPZS) algorithm and with the well know diamond search (DS) algorithm. The designed algorithms presented better BD-rate results than DS but they presented some losses when compared to EPZS. Since EPZS is not focused in a hardware design, it presents a lot of data dependencies, a irregular memory access, a non-deterministic number of operations to encode a block and it must store motion vectors of previously encoded frames. All these features are undesirable for many applications, including those ones focused on battery powered devices. To show the efficiency of the proposed algorithms, two architectures were designed targeting these algorithms and they were synthesized for Altera Stratix 4 and for ASIC using TSMC 90 nm standard-cells technology. Synthesis results show that the architectures are capable to process HD 1080p videos, at real time, by obtaining good results in terms of hardware resources use, power consumption and processing rate when compared to related works. The ASIC implementations are able to guarantee real-time performance for high definition videos consuming 12.5 and 13.5 mW, respectively. These results are possible because S&IS and LD&IS were developed focusing on hardware design.

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%.

Parallelization of Full Search Motion Estimation Algorithm for Parallel and Distributed Platforms

This work presents an efficient method to map the Full Search algorithm for Motion Estimation (ME) onto General Purpose Graphic Processing Unit (GPGPU) architectures using Compute Unified Device Ar...

Iterative random search: a new local minima resistant algorithm for motion estimation in high-definition videos

Motion estimation (ME) is the most important component of current video encoders, however, it presents a very high computational complexity. To deal with this complexity, fast ME search algorithms are widely used, since they can greatly speed up this process. Fast search algorithms are vulnerable to choose local minima, producing quality losses, and these losses are more significant when high-definition videos are considered. This work presents a new fast search algorithm for motion estimation, focusing on high-definition videos, named Iterative Random Search (IRS). The IRS algorithm randomly chooses candidate blocks from the reference frame, and, for the best candidates, an iterative refinement is done. The central position is also evaluated through an iterative process. By using this combination of strategies, the IRS becomes less susceptible to local minima falls. Achieved results show that, for 1080p sequences, IRS generates the highest quality results when compared to well known fast algorithms, such as Diamond Search, Four Step Search and Three Step Search. The quality gains can be higher than 4 dB, while the number of evaluated candidate blocks may increase, at most, 2.6 times. Additionally, the average quality loss against Full Search is 1.45 dB, while the number of evaluated candidate blocks can achieve a reduction higher than 200 times.

Comparative Analysis of Exhaustive Search algorithm with ARPS algorithm for Motion Estimation

Accurate motion estimation is a key factor for achieving enhanced compression ratio. It is the process of determining an offset to a suitable reference area in previously coded frame and has a significant effect on performance of coders and decoders (CODEC). This paper compares the performance of Adaptive Rood Pattern Search (ARPS) motion estimation algorithm with Exhaustive search (ES) algorithm in terms of computational complexity being applied on MATLAB platform for different dimensional images. Several significant parameters such as number of computations and cost to determine the motion vectors are determined by applying to different dimension images. General Terms Compression, Multimedia, Spatial domain, Temporal domain,