Hierarchical stochastic fast search motion estimation algorithm

Tedmori, Sara; Al-Najdawi, Nijad

doi:10.1049/iet-cvi.2010.0188

Cited by 14 publications

(10 citation statements)

References 20 publications

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“…When compared with the proposed work, using the same set of test videos, the average PSNR results show that the current proposed algorithm outperforms the work in [35] [36], and [37] with 13.49%, 4% and 3% average enhancement respectively. The results of the PSNR values of the proposed work can be improved if the Kalman filter is applied as a stochastic predictor/ corrector estimator.…”

Section: Resultsmentioning

confidence: 93%

“…In addition to the above standard algorithms, the enhanced Three-Step-Search algorithm [35], the Kalman simplified hierarchical search algorithm [36], and the Cross-Diamond Modified Hierarchical Search Algorithm [37] are chosen as the state-of-the-art benchmarks in the field of hierarchical search algorithms. When compared with the proposed work, using the same set of test videos, the average PSNR results show that the current proposed algorithm outperforms the work in [35] [36], and [37] with 13.49%, 4% and 3% average enhancement respectively.…”

Section: Resultsmentioning

confidence: 99%

“…This can be summarized with a total Number of Operations Per Block (NOPB), where the proposed algorithm requires only 0.69% of the total NOPB required by the FSA (i.e., 99.31% reduction in complexity). When compared to the rest of the algorithms, the algorithm require 4.1% of the total complexity required by the Cross-Diamond Modified Hierarchical Search algorithm [37], 12% compared to Kalman Simplified HSA [36], 6.8% compared to the TSS algorithm [32], and 10.4% compared to the 2DLS algorithms [33]. This enormous reduction in complexity is due the substantial reduction in the total number of operations required in the proposed matching criterion.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

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

Al-Najdawi¹,

Tedmori²,

Alzubi³

et al. 2016

ijacsa

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Abstract-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.

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Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Al-Najdawi¹,

Tedmori²,

Alzubi³

et al. 2016

ijacsa

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“…Hierarchical Motion Estimation (HME) is a good choice in fast ME algorithm, whose degree of parallelism is close to that of full-search method. HME in Kuhn et al [21]; Tedmori and Al-Najdawi [22]; Nijad [23]; Nam et al [24] algorithm combines the advantages of large blocks in a high resolution frame with small blocks in a low resolution frame. In HME, the original frame is subsampled into multiple low resolution frames, and the fullsearch method is performed on all the resolution frames from low to high.…”

Section: Related Workmentioning

confidence: 99%

A Highly Parallel and Scalable Motion Estimation Algorithm with GPU for HEVC

Xue

Ren

et al. 2017

Scientific Programming

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We propose a highly parallel and scalable motion estimation algorithm, named multilevel resolution motion estimation (MLRME for short), by combining the advantages of local full search and downsampling. By subsampling a video frame, a large amount of computation is saved. While using the local full-search method, it can exploit massive parallelism and make full use of the powerful modern many-core accelerators, such as GPU and Intel Xeon Phi. We implanted the proposed MLRME into HM12.0, and the experimental results showed that the encoding quality of the MLRME method is close to that of the fast motion estimation in HEVC, which declines by less than 1.5%. We also implemented the MLRME with CUDA, which obtained 30-60x speed-up compared to the serial algorithm on single CPU. Specifically, the parallel implementation of MLRME on a GTX 460 GPU can meet the real-time coding requirement with about 25 fps for the 2560 × 1600 video format, while, for 832 × 480, the performance is more than 100 fps.

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“…PSNR is a well-known parameter and can be computed from Equation 5 [10]. The PSNR results in an undefined value under one condition only; i.e., when the original image is compared to itself.…”

Section: Psnrmentioning

confidence: 99%