Fast Block-Matching Motion Estimation Using Modified Diamond Search Algorithm

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1 Fast Block-Matching Motion Estimation Using Modified Diamond Search Algorithm Bichu Vijay 1, Ganapathi Hegde 2, Sanju S 3 Amrita School of Engineering, Bangalore, India vijaybichu.in@gmail.com 1, ganapathi_hegde@blr.amrita.edu 2, 0505sanjus@gmail.com 3 Abstract This paper propose a new block matching algorithm for motion estimation. Four different block matching algorithms using for motion estimation are evaluated where no of computation to find out best match and peak signal to noise ratio (PSNR) are used to find out most optimal algorithm. Four different block matching algorithm are implemented in MATLAB. Each algorithm is evaluated using PSNR and search point per macro block (computation time). The result shows that among the four algorithms evaluated proposed Modified diamond search (MDS) algorithm has the best based on PSNR and computation time. Index Terms Block matching, Motion estimation, Modified diamond search algorithm (MDS). I. INTRODUCTION Motion estimation is the technique that helps to reduces temporal correlation of successive video frames to make compression of video efficient. Motion estimation estimates the pair of motion vectors which map the previous frame to the current frame of a video Advantage of Motion estimation is instead of transmit whole frame every time only motion vectors need to be transmit. So compression can be achieved by coding the motion vectors. Availability of limited channel bandwidth and storage medium for real time video processing require an efficient coding scheme. So fast and efficient Motion estimation techniques are required for making encoding process is an efficient one. Hence considerable works are demanded in this field. Motion estimation has mainly two subcategory first one is block matching algorithm methods and second one is pixel recursion method. Block motion estimation seems to be the efficient and regular method. The search method, search range and block matching criteria are the some major factors that affect the performance of Block matching Algorithm. Full search method (FSBM) [5] and fast block matching algorithms are two methods are used to find out best match in motion estimation technique. In FBMAs the set of motion vector is computed by some fixed set of search patterns like, Three Step Search(TSS)[6], Four Step Search[7], Block Based Gradient Decent(BBGDS) and DS[9]. The bottleneck of motion estimation process is large number of computation. By reducing number of the search points are to be evaluated, computations in a Block matching algorithm such as addition, subtraction and absolute operations can be reduce. A fast motion estimation algorithm is needed consequently. In this paper a new algorithm for finding best match and motion vector is proposed. Modified Diamond Search (MDS) algorithm is a fast block matching algorithm. MDS algorithm use three pattern to find out the best match. It provides improved performance compared to previous methods in term of Peak Signal Noise Ratio (PSNR) and number of search points. II. BLOCK MATCHING MOTION ESTIMATION Block matching motion estimation (BMA) technique carried out by block-by-block basis. Block based motion estimation is used in many video coding standards which include MPEG-1, MPEG-3, H.261, H.263. In BMA image frames are divided into several non-overlapping macro blocks and best matches is searched within the search range by using all possible position for each macro block. In real video, motion of an object can be a complex combination of translation and rotation. Such type of motion require large amount of processing and motion estimation is also to be very difficult. Motion compensated coding utilize translational motion of the objects in the videos, because translational motion can be estimated by easily. Block matching estimation algorithm assumes the objects are rigid and move in the translational movement for at least a few frames and occlusion of one object by another and with an uncovered background which is neglected BMA is the block based search technique. BMA working on the principle such that present frame in the video sequence is divided in to several macro blocks. Each corresponding macro block compared with adjacent neighbor macro blocks in the previous frame. The best matching with macro block in the current frame with previous frame gives the motion vector of the macro block from one location to another location in between two frames. This displacement 1

2 calculation for an entire frame consists of n n macro block gives out the motion estimation of a frame. The frame is divided in to matrix of n n macro blocks in block matching motion estimation. Search window size constrained up to p pixel on all sides of the macro block in previous frame to get a best match for the macro block. The parameter p is known as the search parameter. The value parameter p is requiring larger for larger motion. Thus for large search parameter the motion estimation process require more number of computation. The matching of one macro block with another is determined in basis of the output of matching criteria. The macro block that results in the minimum cost is the block that matches the closest to current block. As the value of n is increased, in each frame the required number of blocks to be processed will decrease. Therefore corresponding to this computational complexity also reduces. Fig. 1: A sample candidate block search in search window. III. MATCHING CRITERIA The matching is to determine the comparison between the frames or portions of frames of video. The similarity measurement or correlation measurement is a very important element in the matching process. Instead finding the maximum similarity or correlation, finding the minimum dissimilarity or matching error is the best way in block matching. There are several matching criteria or cost functions have already been proposed, of which the most popular and less computation expensive is Sum of Absolute Difference (SAD). Another matching function is Mean Absolute Differences (MAD) and Peak Signal to Noise Ratio (PSNR) which are represented in Equations given below SAD = MAD = 1 N 2 i=0 i=0 Cij Rij (1) i=0 i=0 Cij Rij (2) In the Sum of Absolute Difference correspondingly pixels from each block are compared and their differences are summed, as in the equation (1). In the equation N is the size of the macro block, Cij is pixel value of current macro block and Rij is the pixels value of reference macro block, those values are compared. This SAD function is for two blocks C and R of size NxN. Cij is the value of pixel in the i th row and j th column of block C. The lower the SAD the better is the match and so the candidate block with minimum SAD should be chosen. PSNR = 10 log MSE Peak-Signal-to-Noise-Ratio (PSNR) showing the motion compensated image its calculation is by motion vectors and macro blocks from the reference frame. (3) IV. SEARCHING ALGORITHMS In the Block matching algorithm the most accurate algorithm is the full-search (FS) algorithm method which exhaustively computes all possible candidate block cost function within search window to find the optimum value. To find out motion vector for a given candidate block, candidate block that gives the minimum block distortion measure is chosen. Full search method has not been a popular choice due to the large computational cost involved in it. However, Full search method gives the best match, so it produces the highest PSNR compared to other BMA methods. There are total (2P+1) (2P+1) number of positions investigated in the Full search algorithm. Because of the large computation time, it is not suitable for real-time video coding. Many fast block matching motion estimation algorithms had been proposed to locate optimal motion vectors. Among them are well-known three-step search (TSS) [6], 4-step search (4SS) [7], diamond search (DS) [9] and hexagonal search. TSS technique uses a maximum of three steps to find out the best match. TSS algorithm steps starts and ends with 9 equal spaced search points, after each step search points get close to each other. The best matched candidate search point in the last step becomes the center of the next step. Hence, TSS has a fixed number of search points per each block; this gives a speedup of 9 over the FS. The TSS algorithm is one of the simple and efficient methods used for ME. TSS saving factor is 100 times greater when compared with Full search block matching Algorithm (FSBMA). The search step is fixed which is almost three search steps. TSS is inefficient for the estimation of small motion because it utilizes a uniformly allocated pattern in the initial step. Diamond Search (DS) Algorithm employed by two diamond- shaped search patterns of different sizes. The DS algorithm is based on the assumption such that motion vectors are center biased. The DS algorithm employs two search patterns Fig.2. A large diamond search pattern (LDSP) of 9 points and a small diamond search pattern (SDSP) of 5 points. The DS algorithm starts with LDSP pattern. LDSP is repeatedly applied until the step in which the minimum block distortion or cost function point occurs at the center of the diamond. The search pattern is then switched to SDSP. The block having nominal value of cost function among the 5 checking points in the SDSP provides the motion vector of the best matching block.

3 Fig. 2: LDSP and SDSP pattern. V. PROPOSED ALGORITHM Proposed MDS algorithm is based on a Modified Diamond search pattern. Diamond shape pattern is extremely efficient for finding true motion vectors. The most efficient shape is the diamond, with only slight loss in search efficiency. The diamond shape has high search efficiency because most video contains translation, zooming, pan and tilt motions, which easily fall on the diamond corners when block ME is employed. However, diamond search is still susceptible to finding local minima and missing the globally optimum motion vector. In order to further improve search efficiency, reduce number of computation a Modified Diamond Search pattern is proposed which is showed in the Fig 3 & 4. H/VDSP Finally among five checking point in SDSP, the minimum cost function position gives out best match. Proposed MDS Algorithm is summarized as follows. Step1: SDSP apply, if Minimum Distortion (MBD) at center of SDSP, algorithm terminated (Block stationary). Otherwise go to step 2. Step2: If Minimum Distortion block is on left or right position of SDSP applies HDSP. If Minimum Distortion point is on top or bottom position of SDSP applies VDSP. If MBD at center of H/VDSP goes to step 3, otherwise continue step 2. Step3: SDSP is apply, MBD obtain from SDSP gives best motion vector. a) Case1: SDSP to VDSP b) Case2: SDSP to HDSP Fig. 3: VDSP and HDSP pattern. c) Case 3: HDSP to VDSP Fig. 4: SDSP pattern. Modified Diamond Search algorithm uses 3 pattern to find out best match Small Diamond search pattern (SDSP) Vertical Diamond search pattern (VDSP), Horizontal Diamond search pattern (HDSP). The MDS algorithm intends to generate quality results close to those generated by Diamond Search with lower no of calculation. The MDS finds best match using 3 patterns. Each pattern consist of five search point, this will reduce number of SAD calculation. MDS algorithm is done in three steps search starting with SDSP followed by d) Case 4: VDSP to SDSP 3

4 Table 1: No of Search points needed for Full Search Algorithm for e) Case 5: HDSP to SDSP Fig. 7: PSNR value of Full search Algorithm for Fig. 5: Search path example which leads to the motion vector (5,-1) in six search steps initial one time SDSP three times of HDSP, one time VDSP and SDSP at the final step. There are17 search points in total taking five, three, three, three, three and one search points at each step, sequentially VI. EXPERIMENTAL RESULT The performance of the proposed motion estimation is evaluated with FS [5], TSS [6], DS [9], all the three BMAs follow the proposed BMA execution flow, but the search strategies and patterns vary between them. Experiments have been conducted to justify the performance of our MDS algorithm. The peak-signal-to-noise-ratio (PSNR) was used as a performance measure. The MV search is based on the luminance component with the search block of 16 16, and the search window was ±7 pixels. The simulation was performed with two sequences in QCIF (each with 33frames). Fig. 8: Number of Search points for TSS Algorithm for Table 2: Number of Search points needed for TSS Algorithm for caltrain sequence Fig. 9: PSNR for TSS Algorithm for Fig. 6: No of Search points for Full Search Algorithm for Fig. 10: Number of Search points for DS Algorithm for 4

5 Table 3: Number of Search points needed for DS Algorithm for Fig. 11: PSNR value of DS Algorithm for caltrain Table 4: PSNR values of DS Algorithm for caltrain Fig 12: No of Search points for MDS Algorithm for Table 5: Number of Search points needed for MDS Algorithm for Fig 13: PSNR value of MDS Algorithm for caltrain Table 2: PSNR value for MDS Algorithm for caltrain Sequence Algorithm Average Number of Speed up search point FS Caltrain TSS DS MDS FS Mobile TSS DS MDS Table 7: Average No of search points per motion vector estimation Sequence FS TSS DS MDS Caltrain Mobile Table 6: Average PSNR per pixel. Four algorithms have been implemented in MATLAB. The input video sequence with a distance of 2 between current frame and reference frame was used to generate the frame-by-frame results of the algorithms. The above figures show that, the plot of the average number of searches required per macro block for the input video sequence using the 3 fast block matching algorithms and FSBMA. As is shown by figures Fig 11 and 12 DS and MDS come pretty close to the PSNR results of FSBMA. While the ES takes on an average around ~207 searches per macro block, DS and MDS drop that number by more than an order of magnitude. TSS performance in terms of PSNR is not close to the results of FSBMA, but the number of computations required per macro block reduced by almost an order of magnitude. VII CONCLUSION In this paper, we proposed a Modified diamond search (MDS) algorithm for fast block-based motion estimation. Simulation results, both objective and subjective, were presented to show that MDS is more efficient, effective, and robust as compared to some other popular block-matching algorithms such as the full-search, the three-step search, and diamond search algorithm. Compared with other proposed BMA s such as full search and DS, our MDS algorithm works better in terms of PSNR values, reconstructed image quality, and average number of search points. ACKNOWLEDGMENT The authors acknowledge the help of Mr. Ganapathi 5

6 hegde Professor, Dept. of Electronics and Communication, Amrita School of Engineering, Bangalore for introducing the various aspects of multimedia processing during the course REFERENCES [1] Digital Image Processing Using MATLAB, 2nd edition, Rafael Gonzalez, Richard Woods, Steven Eddins, Tata McGraw-Hill Education Pvt Ltd. [2] Digital Image Processing, Kenneth Castleman, Pearson Education. [3] MATLAB help pages for tool related issues [4] Gonzalez and Woods, "Digital image processing", 2nd Edition, Prentice hall, [5] Yih-Chuan Lin and Shen-Chuan Tai, Fast Full search Block Matching Algorithm for Motion Compensated Video Compression ; IEEE Transactions on Communications, vol.45, no.5, pp , 1997 [6] Renxiang Li, Bing Zeng, and Ming L. Liou, A New Three-Step SearchAlgorithm for Block Motion Estimation, IEEE Trans. Circuits AndSystems For Video Technology, vol 4., no. 4, pp , August [7] Lai-Man Po, and Wing-Chung Ma, A Novel Four-Step Search Algorithm for Fast Block Motion Estimation, IEEE Trans. Circuits And Systems For Video Technology, vol 6, no. 3, pp , June [8] J. Y. Tham, S. Ranganath, M. Ranganath, and A. A.Kassim, A novel unrestricted center-biased diamond search algorithm for block motion estimation, IEEE Trans. Circuits Syst. Video Technol., vol. 8, pp , Aug [9] Shan Zhu and Kai-Kuang Ma, A new diamond search algorithm for fast blockmatching motion estimation, IEEE Trans. On Image process, Vol. 9, No. 2, Feb.,2000 M. Ghanbari, [10] Thomas W, Gary J S. Overview of the H.264/AVC Video Coding Standard[J]. IEEE Transaction on Circuits and System for Video Technology, 2003, 9(7): [11] Aroh Barjatya, Block matching algorithms for motion estimation, DIP 6620 Spring 2004R. [12] Yong Ding, Xiao-Lang Yan, Parallel Architecture of Motion Estimation for Video Format Conversion with Center-biased Diamond Search, 2009 IEEE international conference. [13] Obianuju Ndili & Tokunbo Ogunfunni, Algorithm and Architecture Co-design of Hardware-Oriented, Modified Diamond Search for Fast Motion Estimation in H.264/AVC, IEEE Trans. On circuits and systems for video technology, vol.21, No.9, September [14] Gustavo Sanchez, Felipe Sampaio, Marcelo Porto, Sergio Bampi & Luciano Agostini, DMPDS: A Fast Motion Estimation Algorithm Targeting High Resolution Videos and Its FPGA Implementation, International Journal of Reconfigurable Computing Volume