An Effective Implementation of Configurable Motion Estimation Architecture for Block Matching Algorithm
0 likes222 views
This document presents a configurable motion estimation architecture designed to optimize block-matching algorithms (BMAs) used in video compression systems. It details a fast full search algorithm that reduces computational requirements while maintaining performance, resulting in efficient motion vector determination. Experimental results indicate improved speed and performance compared to traditional methods, supporting various operating modes in BMAs.
![An Effective Implementation of Configurable Motion Estimation Architecture for Block Matching Algorithm
(IJSRD/Vol. 1/Issue 7/2013/0022)
All rights reserved by www.ijsrd.com 1471
IV. PROPOSED ALGORITHM
Fast full search is an algorithm that significantly reduces the
number of computations required to carry out template
matching and yields exactly the same result as the full
search algorithm. The algorithm relies on the concept of
bounding the matching function. Finding an efficiently
computable upper bound of candidates, that can provide a
better score with respect to the current best match. In this
framework, we apply a succession of increasingly tighter
upper bounding functions. Moreover, by including a
parameter prediction step, we obtain a parameter free
algorithm that, in most cases, affords computational
advantages very similar to those attainable by optimal
parameter tuning. Experimental results show that the
proposed algorithm can significantly accelerate a full-search
equivalent template matching process and performs state-of-
the-art methods.
V. SYNTHESIS RESULTS
According to the above analysis, FS is well suited. Hence,
the proposed BMA framework is configured to support
TDL, BS, and TSS operating modes. The area and timing
results based on logic synthesis as well as other
characteristics. The proposed architecture outperforms the
reference architectures in terms of performance because of
its efficient memory system MAD and SAD unit.
VI. CONCLUSION
During the searching procedure, the checking point yielding
the minimum block distortion (MBD) determines the
displacement of the best matching block. This project
introduces configurable motion estimation architecture for a
wide range of fast block-matching algorithms (BMAs). The
simulation results shows that the time permitting for
performing of searching within a block is less when
compare to the full search. So we are achieved both speed
and performance factor for searching the block without any
trade off factor.
REFERENCES
[1] Jarno vane, Eero aho, Kimmo kussilinna, Timo D.
Hamalainen “A Configurable motion estimation
architecture for block matching algorithms”, IEEE
Transactions on circuits and systems for video
technology, VOL.19, NO.4, APRIL 2009.
[2] P. Kuhn, Algorithms, Complexity Analysis and VLSI
Architectures for MPEG-4 Motion Estimation.
Boston, MA: Kluwer, 1999, p. 239.
[3] Y. W. Huang, C. Y. Chen, C. H. Tsai, C. F. Shen, and
L. G. Chen, “Survey on block matching motion
estimation algorithms and architectures with new
results,” J. VLSI Signal Process., vol. 42, no. 3, pp.
297–320, Mar. 2006.
[4] W. Li and E. Salari, “Successive elimination
algorithm for motion estimation,” IEEE Trans. Image
Process., vol. 4, no. 1, pp. 105–107, Jan. 1995.
[5] X. Q. Gao, C. J. Duanmu, and C. R. Zou, “A
multilevel successive elimination algorithm for block
matching motion estimation,” IEEE Trans. Image
Process., vol. 9, no. 3, pp. 501–504, Mar. 2000.
[6] B. Liu and A. Zaccarin, “New fast algorithms for the
estimation of block motion vectors,” IEEE Trans.
Circuits Syst. Video Technol., vol. 3, no. 2,pp. 148–
157, Apr. 1993.
[7] T. Koga, K. Iinuma, A. Hirano, Y. Iijima, and T.
Ishiguro, Motion compensated Interframe coding for
video conferencing,” in Proc. Nat.
Telecommunication Conf., New Orleans, LA, 1981,
pp. G5.3.1–5.3.5.
[8] L. K. Liu and E. Feig, “A block-based gradient
descent search algorithm for block motion estimation
in video coding,” IEEE Trans. Circuits Syst. Video
Technol., vol. 6, no. 4, pp. 419–422, Aug. 1996.](https://image.slidesharecdn.com/ijsrdv1i7022-140806063137-phpapp02/85/An-Effective-Implementation-of-Configurable-Motion-Estimation-Architecture-for-Block-Matching-Algorithm-2-320.jpg)
An Effective Implementation of Configurable Motion Estimation Architecture for Block Matching Algorithm
- 1. IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 7, 2013 | ISSN (online): 2321-0613 All rights reserved by www.ijsrd.com 1470 Abstract— Abstract - This project introduces configurable motion estimation architecture for a wide range of fast block-matching algorithms (BMAs). Contemporary motion estimation architectures are either too rigid for multiple BMAs or the flexibility in them is implemented at the cost of reduced performance. .In block-based motion estimation, a block-matching algorithm (BMA) searches the best matching block for the current macro block from the reference frame. During the searching procedure, the checking point yielding the minimum block distortion (MBD) determines the displacement of the best matching block. Keywords: Block-matching algorithms (BMA’s), BMA framework, motion estimation. I. INTRODUCTION BLOCK-BASED motion estimation has been widely adopted by the current video compression standards such as MPEG-1/2/4 and H.261/263/264. In block-based motion estimation, a block-matching algorithm (BMA) searches for the best matching block for the current macro block from the reference frame. During the searching procedure, the checking point yielding the minimum block distortion (MBD) determines the displacement of the best matching block. For the block distortion computation, the sum of absolute differences (SAD) is one of the most frequently employed criteria. After finding the MBD point, motion estimation delivers a motion vector (MV) of the current block and prediction residues. The MV of the current block equals the displacement of the best matching block. II. LITERAUTRE SURVEY The pixel based motion estimation approach seeks to determine motion vectors for every pixel in the image. This is also referred to as the optical flow method, which works on the fundamental assumption of brightness constancy that is the intensity of a pixel remains constant, when it is displaced. However, no unique match for a pixel in the reference frame is found in the direction normal to the intensity gradient. It is for this reason that an additional constraint is also introduced in terms of the smoothness of velocity (or displacement) vectors in the neighbourhood. The smoothness constraint makes the algorithm interactive and requires excessively large computation time, making it unsuitable for practical and real time implementation for this reason I go for BMA. In BMA a single motion vector is computed for the entire block, whereby we make an inherent assumption that the entire block undergoes translational motion. This assumption is reasonably valid, except for the object boundaries and smaller block size leads to better motion estimation and Compression. Block based motion estimation is accepted in all the video coding standards proposed till date. By observing all these below algorithm like TSS, BS, FSS, and TDL, introducing fast full search algorithm method. III. ABOUT BMA In a typical Block Matching Algorithm, each frame is divided into blocks, each of which consists of luminance and chrominance blocks. Usually, for coding efficiency, motion estimation is performed only on the luminance block. Each luminance block in the present frame is matched against candidate blocks in a search area on the reference frame. These candidate blocks are just the displaced versions of original block. The best candidate block is found and its displacement (motion vector) is recorded. In a typical inter frame coder the input frame is subtracted from the prediction of the reference frame. Consequently the motion vector and the resulting error can be transmitted instead of the original luminance block thus inter frame redundancy is removed and data compression is achieved. A Block Matching Algorithm (BMA) is a way of locating matching blocks in a sequence of digital video frames for the purposes of motion estimation. The purpose of a block matching algorithm is to find a matching block from a frame i in some other frame j, which may appear before or after i. This can be used to discover temporal redundancy in the video sequence, increasing the effectiveness of interface video compression. Block matching algorithms make use of criteria to determine whether a given block in frame j, matches the search block in frame i. Motion estimation is the process of determining motion vectors. Fig. 2: Diagram of Block-Matching motion estimation process An Effective Implementation of Configurable Motion Estimation Architecture for Block Matching Algorithm C. Mohan1 V. Viswanadha2 1 M. Tech, 2 Associate Professor 1,2 SIETK, Puttur, A.P.
- 2. An Effective Implementation of Configurable Motion Estimation Architecture for Block Matching Algorithm (IJSRD/Vol. 1/Issue 7/2013/0022) All rights reserved by www.ijsrd.com 1471 IV. PROPOSED ALGORITHM Fast full search is an algorithm that significantly reduces the number of computations required to carry out template matching and yields exactly the same result as the full search algorithm. The algorithm relies on the concept of bounding the matching function. Finding an efficiently computable upper bound of candidates, that can provide a better score with respect to the current best match. In this framework, we apply a succession of increasingly tighter upper bounding functions. Moreover, by including a parameter prediction step, we obtain a parameter free algorithm that, in most cases, affords computational advantages very similar to those attainable by optimal parameter tuning. Experimental results show that the proposed algorithm can significantly accelerate a full-search equivalent template matching process and performs state-of- the-art methods. V. SYNTHESIS RESULTS According to the above analysis, FS is well suited. Hence, the proposed BMA framework is configured to support TDL, BS, and TSS operating modes. The area and timing results based on logic synthesis as well as other characteristics. The proposed architecture outperforms the reference architectures in terms of performance because of its efficient memory system MAD and SAD unit. VI. CONCLUSION During the searching procedure, the checking point yielding the minimum block distortion (MBD) determines the displacement of the best matching block. This project introduces configurable motion estimation architecture for a wide range of fast block-matching algorithms (BMAs). The simulation results shows that the time permitting for performing of searching within a block is less when compare to the full search. So we are achieved both speed and performance factor for searching the block without any trade off factor. REFERENCES [1] Jarno vane, Eero aho, Kimmo kussilinna, Timo D. Hamalainen “A Configurable motion estimation architecture for block matching algorithms”, IEEE Transactions on circuits and systems for video technology, VOL.19, NO.4, APRIL 2009. [2] P. Kuhn, Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation. Boston, MA: Kluwer, 1999, p. 239. [3] Y. W. Huang, C. Y. Chen, C. H. Tsai, C. F. Shen, and L. G. Chen, “Survey on block matching motion estimation algorithms and architectures with new results,” J. VLSI Signal Process., vol. 42, no. 3, pp. 297–320, Mar. 2006. [4] W. Li and E. Salari, “Successive elimination algorithm for motion estimation,” IEEE Trans. Image Process., vol. 4, no. 1, pp. 105–107, Jan. 1995. [5] X. Q. Gao, C. J. Duanmu, and C. R. Zou, “A multilevel successive elimination algorithm for block matching motion estimation,” IEEE Trans. Image Process., vol. 9, no. 3, pp. 501–504, Mar. 2000. [6] B. Liu and A. Zaccarin, “New fast algorithms for the estimation of block motion vectors,” IEEE Trans. Circuits Syst. Video Technol., vol. 3, no. 2,pp. 148– 157, Apr. 1993. [7] T. Koga, K. Iinuma, A. Hirano, Y. Iijima, and T. Ishiguro, Motion compensated Interframe coding for video conferencing,” in Proc. Nat. Telecommunication Conf., New Orleans, LA, 1981, pp. G5.3.1–5.3.5. [8] L. K. Liu and E. Feig, “A block-based gradient descent search algorithm for block motion estimation in video coding,” IEEE Trans. Circuits Syst. Video Technol., vol. 6, no. 4, pp. 419–422, Aug. 1996.