Motion estimation algorithm using interblock correlation

Hsieh, Chaur‐Heh; Lu, Pei-Hsuan; Shyn, J.-S.; Lu, Enshun

doi:10.1049/el:19900183

Cited by 73 publications

(16 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Algorithms belonging to decimation of search positions are often trapped in local minima of distortion, thus resulting in poor ME accuracy. Predictive motion estimation [39][40][41][42][43], which utilizes the motion information in the spatial and/or temporal neighboring blocks to form an initial estimate of current MV, can effectively reduce the search area as well as the computation. The reduced motion search area also provides an additional compression since the overhead information of MV is less.…”

Section: Predictive Searchmentioning

confidence: 99%

Survey on Block Matching Motion Estimation Algorithms and Architectures with New Results

Huang

Chen

Tsai

et al. 2006

J VLSI Sign Process Syst Sign Image Video Technol

139

View full text Add to dashboard Cite

Abstract. Block matching motion estimation is the heart of video coding systems. During the last two decades, hundreds of fast algorithms and VLSI architectures have been proposed. In this paper, we try to provide an extensive exploration of motion estimation with our new developments. The main concepts of fast algorithms can be classified into six categories: reduction in search positions, simplification of matching criterion, bitwidth reduction, predictive search, hierarchical search, and fast full search. Comparisons of various algorithms in terms of video quality and computational complexity are given as useful guidelines for software applications. As for hardware implementations, full search architectures derived from systolic mapping are first introduced. The systolic arrays can be divided into inter-type and intra-type with 1-D, 2-D, and tree structures. Hexagonal plots are presented for system designers to clearly evaluate the architectures in six aspects including gate count, required frequency, hardware utilization, memory bandwidth, memory bitwidth, and latency. Next, architectures supporting fast algorithms are also reviewed. Finally, we propose our algorithmic and architectural co-development. The main idea is quick checking of the entire search range with simplified matching criterion to globally eliminate impossible candidates, followed by finer selection among potential best matched candidates. The operations of the two stages are mapped to the same hardware for resource sharing. Simulation results show that our design is ten times more area-speed efficient than full search architectures while the video quality is competitively the same.

show abstract

Section: Predictive Searchmentioning

confidence: 99%

Survey on Block Matching Motion Estimation Algorithms and Architectures with New Results

Huang

Chen

Tsai

et al. 2006

J VLSI Sign Process Syst Sign Image Video Technol

139

View full text Add to dashboard Cite

show abstract

“…The motion vectors used in our analysis have been computed using a single resolution block matching algorithm [34]. The method measures the similarity of motion blocks using spatial correlation and uses predictive search to e ciently compute block-correspondences in di erent frames.…”

Section: Motion Segmentationmentioning

confidence: 99%

A probabilistic spectral framework for grouping and segmentation

ROBLESKELLY¹

2004

Pattern Recognition

View full text Add to dashboard Cite

This paper presents an iterative spectral framework for pairwise clustering and perceptual grouping. Our model is expressed in terms of two sets of parameters. Firstly, there are cluster memberships which represent the a nity of objects to clusters. Secondly, there is a matrix of link weights for pairs of tokens. We adopt a model in which these two sets of variables are governed by a Bernoulli model. We show how the likelihood function resulting from this model may be maximised with respect to both the elements of link-weight matrix and the cluster membership variables. We establish the link between the maximisation of the log-likelihood function and the eigenvectors of the link-weight matrix. This leads us to an algorithm in which we iteratively update the link-weight matrix by repeatedly reÿning its modal structure. Each iteration of the algorithm is a three-step process. First, we compute a link-weight matrix for each cluster by taking the outer-product of the vectors of current cluster-membership indicators for that cluster. Second, we extract the leading eigenvector from each modal link-weight matrix. Third, we compute a revised link weight matrix by taking the sum of the outer products of the leading eigenvectors of the modal link-weight matrices.

show abstract

“…The predicted MV is obtained by calculating the statistical average (such as the mean, the median, the weighted mean/median, etc.) of the neighboring MVs [7], [8] or selecting one of the neighboring MVs according to certain criteria [9]- [12]. After the prediction, the search window's size and the search center are re-defined accordingly, and FS is then performed within this new search window [9]- [12].…”

Section: B Fast Bma Based On Inter-block Correlationmentioning

confidence: 99%

“…of the neighboring MVs [7], [8] or selecting one of the neighboring MVs according to certain criteria [9]- [12]. After the prediction, the search window's size and the search center are re-defined accordingly, and FS is then performed within this new search window [9]- [12]. In other methods, fast BMAs instead of FS are performed around the predicted MV without re-defining the search window [7], [8], [11].…”

Section: B Fast Bma Based On Inter-block Correlationmentioning

confidence: 99%

Adaptive rood pattern search for fast block-matching motion estimation

Nie

2002

IEEE Trans. on Image Process.

467

View full text Add to dashboard Cite

Abstract-In this paper, we propose a novel and simple fast block-matching algorithm (BMA), called adaptive rood pattern search (ARPS), which consists of two sequential search stages: 1) initial search and 2) refined local search. For each macroblock (MB), the initial search is performed only once at the beginning in order to find a good starting point for the follow-up refined local search. By doing so, unnecessary intermediate search and the risk of being trapped into local minimum matching error points could be greatly reduced in long search case. For the initial search stage, an adaptive rood pattern (ARP) is proposed, and the ARP's size is dynamically determined for each MB, based on the available motion vectors (MVs) of the neighboring MBs. In the refined local search stage, a unit-size rood pattern (URP) is exploited repeatedly, and unrestrictedly, until the final MV is found. To further speed up the search, zero-motion prejudgment (ZMP) is incorporated in our method, which is particularly beneficial to those video sequences containing small motion contents. Extensive experiments conducted based on the MPEG-4 Verification Model (VM) encoding platform show that the search speed of our proposed ARPS-ZMP is about two to three times faster than that of the diamond search (DS), and our method even achieves higher peak signal-to-noise ratio (PSNR) particularly for those video sequences containing large and/or complex motion contents.Index Terms-Adaptive search pattern, block-matching algorithm, diamond search, fast motion estimation, rood pattern, spatial correlation, video compression, zero-motion prejudgment.

show abstract

Motion estimation algorithm using interblock correlation

Cited by 73 publications

References 2 publications

Survey on Block Matching Motion Estimation Algorithms and Architectures with New Results

Survey on Block Matching Motion Estimation Algorithms and Architectures with New Results

A probabilistic spectral framework for grouping and segmentation

Adaptive rood pattern search for fast block-matching motion estimation

Contact Info

Product

Resources

About