A FAST SEARCH BLOCK-BASED MOTION ESTIMATION ALGORITHM FOR COMPRESSION OF MOTION PICTURES

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 7, July 2017, pp , Article ID: IJMET_08_07_075 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed A FAST SEARCH BLOCK-BASED MOTION ESTIMATION ALGORITHM FOR COMPRESSION OF MOTION PICTURES Md. Abdul Rawoof, D.Laxma Reddy, A.V.Paramkusam Department of Electronics and Communication Engineering MLR Institute of Technology, Hyderabad, Telangana, India Dr. R V Krishnaiah Institute of Aeronautical Engineering4, Hyderabad, India ABSTRACT The three-step search algorithm tracks the motion field efficiently for those blocks especially with large motion. On the other hand, it will be trapped into a local minimum for quasi-stationary blocks. In order to avoid this problem, many motion estimation algorithms have been proposed in the literature. However, these algorithms demand more number of search points to track the motion field of large motion blocks. In this paper, we propose a fast search block-based motion estimation algorithm which employs a five-point compact search pattern and the unrestricted search step is used in the first step to track the motion field. The experimental results show that the proposed algorithm outperform other well-known fast block-based motion estimation algorithms in terms of PSNR and requires less computation by up to 15% on average. Index Terms: Block Matching Algorithm, Motion Estimation, Motion Vector, Search Pattern. Cite this Article: Md. Abdul Rawoof, D.Laxma Reddy, A.V.Paramkusam and Dr. R V Krishnaiah, A Fast Search Block-Based Motion Estimation Algorithm for Compression of Motion Pictures. International Journal of Mechanical Engineering and Technology, 8(7), 2017, pp INTRODUCTION Full-Search (FS) searches all the candidate blocks or search points in the search window to find the optimal motion vectors. Due to the large number of candidate blocks in the search window, FS turn out to be the most computational intensive process in the motion estimation process. For the real-time video coding applications, the computational complexity of FS must be reduced. Many fast search motion estimation algorithms have been proposed to speed up the motion estimation process by reducing the number of search points editor@iaeme.com

2 Md. Abdul Rawoof, D.Laxma Reddy, A.V.Paramkusam and Dr. R V Krishnaiah These fast search motion estimation algorithms possibly classified into the following categories: reduction in search positions [1] [9], predictive search [10] [14], search pattern switching [15] [16], multi-resolution search [17] [20] and Fractional-Pixel Interpolation [21-24]. Existing fast search motion estimation algorithms use one or a combination of these categories. The Three-Step Search (TSS) [1] is a motion estimation technique which is apt at searching large motion video sequences, but it requires more number of search points. When the motion object has small motion i.e., for quasi-stationary blocks, TSS will be trapped into a local minimum The New Three-Step Search (NTSS) [2] motion estimation algorithm is appropriate for searching small motions by requiring less number of search points than TSS. The Diamond Search (DS) [3] is a center-biased motion estimation algorithm which is more suitable for searching small motions than NTSS. Hexagon-based search (HS) [4] algorithm can achieve substantial speed improvement over the DS algorithm with similar distortion performance. The enhanced versions of hexagonal search algorithm [6] [8] have been proposed for further reduction of the search points over HS algorithm. These algorithms mainly concentrate on the fast-inner search technique to improve the inner search speed. Normally the above-mentioned algorithms avoid the problem of being trapped into a local minimum for quasi-stationary blocks. However, they demand more number of search points to track the motion field of large motion blocks. In order to overcome this problem, we have proposed a fast search Block-Based Motion Estimation (BB-ME) algorithm. This algorithm requires less number of search points to track motion field accurately irrespective of the degree of motion content. The rest of this paper is organized as follows. In section II, the well-known block matching algorithms such as TSS, NTSSS, DS and HS have been discussed. Section III gives the details of the proposed algorithm. Section IV is devoted to the discussions of the experimental results for various sequences. Finally, section V concludes this paper. 2. BLOCK MATCHING ALGORITHMS A. Three Step Search (TSS) Block Matching Algorithm Koga et al [1] proposed a non-full search algorithm Three Step Search (TSS) in The TSS employs three steps for searching the best motion vectors. The motion vector search procedure for this algorithm is described as follows: Step 1: Choose the zero-motion vector as the centre point. Select the eight search points at a distance of step size equal to or slightly larger than half of the maximum search range from the centre. Check these eight search points and one search point at centre (Total nine search points) as shown in Fig.1. The search point with the minimum block distortion value is selected as the centre point for the second step. Figure 1 The nine search points of TSS in the first step with search range equal to editor@iaeme.com

3 A Fast Search Block-Based Motion Estimation Algorithm for Compression of Motion Pictures Step 2: Check another eight checking points surrounding the new centre with half the previous step size. Again, the search point with the minimum distortion value is chosen as the centre point for the third step. An example of step 2 in TSS is shown in Fig. 2. Figure 2 The example of step 2 in TSS. The red circle indicates the check point with the minimum block distortion in the step 1. Step 3: In this third step of the search procedure, the step size is again halved. The eight more search points surrounding the new centre are checked. The location of the point that gives the minimum distortion value in this step is the motion vector. The example of TSS path with three steps to locate a motion vector at (-1, -1) is shown in Fig. 3. The circle, square and triangle with red color indicate the minimum block distortion points in the step 1, step 2 and step 3 respectively. Figure 3 Example of three step search path to locate a motion vector at (-1, -1). The TSS requires 25 checking points for a maximum displacement window of 7. For a maximum displacement window of d, the number of checking points required equals to [1+8{log2 (d+1)}]. B. New Three Step Search (NTSS) Block Matching Algorithm The TSS algorithm assumes the uniform distribution in video sequences which becomes inefficient for small motion video sequences. The NTSS [2] algorithm is a modified version of the TSS algorithm for searching small motion estimation. The NTSS mainly emphasis on the use of centre-biased motion vector distribution which is one of real world image sequence s characteristics. The procedure for searching motion vectors in NTSS differs from TSS by firstly, using a centre-biased search pattern in its first step and next, including a halfway-stop technique for stationary or quasi-stationary blocks. The details of this NTSS algorithm are given below. Step 1: Choose the zero-motion vector as the centre point. As same as in the TSS, the eight search points at a distance of step size equal to or slightly larger than half of the maximum editor@iaeme.com

4 Md. Abdul Rawoof, D.Laxma Reddy, A.V.Paramkusam and Dr. R V Krishnaiah search range from the centre are selected. In addition to these search points used in TSS algorithm, eight extra search points are added, which are the eight neighbours of the search window centre. Check these sixteen search points and one search point at centre (Total 17 search points) as shown in Fig.4. Figure 4 The 17 search points of NTSS in the first step with search range equal to 7. Step 2: There are three cases where the minimum distortion point occurs in step 1. According to these cases the block is treated as stationary, quasi stationary or neither stationary nor quasi stationary blocks. A halfway-stop technique is used in first two cases for stationary and quasi - stationary blocks. Case 1: If the minimum distortion value in the first step occurs at the search window centre, the search is stopped. This is called the first step stop and the block is treated as stationary block. Case 2: If the minimum distortion point in the first step is one of the eight neighbours of the search window centre, then another eight neighbouring search points surrounding this minimum distortion point will be searched in the second step. The search is then stopped and is called the second-step-stop. In this case, the block is treated as quasi - stationary block. Depending on location of the minimum distortion point in the first step, either five or three new search points have to be checked. The number of checking points required is then either (17+3) =20 or (17+5) =22 as shown in Fig. 5. Case 3: If the minimum distortion point in the first step occurs at any one of the remaining eight search points then the second and the third step of TSS algorithm will be executed. In the worst case (i.e. there is no single stationary block) the NTSS checks 33 search points when compared to 25 search points in TSS. If a first step-stop occurs, then eight search points will be saved. As shown in Fig. 5 if a second step-stop occurs, five or three search points will be saved: if the minimum distortion point is one of the four neighbouring search points along the vertical or horizontal directions, three new search points will be searched and five search points will be saved; if the minimum distortion point is one of the four neighbouring search points along the two diagonal directions, five new search points will be searched and three search points will be saved. Figure 5 Circles are the search points in the first step of TSS, triangles are the 8 extra points added in the first step of NTSS, and squares are the new checking points (3 or 5) in the second step depending on minimum BDM point, in the first step, on the 8 neighbors of the window centre editor@iaeme.com

5 A Fast Search Block-Based Motion Estimation Algorithm for Compression of Motion Pictures The number of search points required in NTSS for finding a motion vector can be assessed by 17P1 + 20P2 + 22P2 ' + 33 (1-P1-P2- P2 ' ), where P1 indicates the probability of existence of stationary blocks in a video sequence while P2 and P2 ' indicate the probability of existence of a quasi - stationary blocks in a video sequence in the two cases mentioned above. C. Diamond Search (DS) Block Matching Algorithm The TSS and NTSS algorithms employ rectangular shaped search patterns. These search patterns are not efficient for centre biased motion vector distribution of real world video sequences. Instead of rectangular shaped search patterns used in TSS and NTSS algorithms, the DS [3] algorithm employs a diamond shaped search patterns. The DS attempts to behave as a circle-shaped search pattern by considering nearly all possible directions for searching a motion vector. The DS employs two search patterns which are Large Diamond Search Pattern (LDSP) and Small Diamond Search Pattern (SDSP) as shown in Fig. 6. These search patterns are derived from the search points within circle of radius of 2 pixels. The LDSP contains nine search points and forms a diamond shape. The SDSP consists five checking points and forms a smaller diamond shape. The search procedure of this algorithm is summarized as follows: Step 1: The LDSP is positioned at the search window centre and the nine search points of LDSP are checked. If the minimum distortion point found is positioned at the centre, go to Step 3. Otherwise, go to Step 2. Step 2: Form a new LDSP with a centre as the minimum distortion point found in the previous search step. If the new minimum distortion point obtained is positioned at the centre of new LDSP, go to Step 3. Otherwise, repeat this step. Step 3: Switch the search pattern from LDSP to SDSP. The minimum distortion point obtained in this step is the final motion vector. (a) (b) Figure 6 Search patterns of DS (a) LDSP with 9 search points (b) SDSP with 5 search points The search points are partially overlapped between adjacent steps; particularly, when LDSP is repeatedly used. For example, three cases of checking-point overlapping are presented in Fig. 7. When the previous minimum distortion point is placed at one of the corners or edge points of LDSP, only three or five new search points have to be tested as shown in Fig. 7(a) and (b), respectively. If the minimum distortion point occurs at centre of LDSP, the search pattern is changed from LDSP to SDSP in the final search. In this case, only four new search points have to be checked, as shown in Fig. 7(c) editor@iaeme.com

6 Md. Abdul Rawoof, D.Laxma Reddy, A.V.Paramkusam and Dr. R V Krishnaiah f (a) (b) (c) Figure 7 Examples of three cases of checking-point overlapping a) LDSP-LDSP (b) LDSP-LDSP (c) LDSP-SDSP D. Hexagon Based Search (HS) Block Matching Algorithm The diamond shaped search pattern in DS algorithm is more efficient than square shaped search patterns in TSS and NTSS. However, the diamond shaped search pattern is not more close to circle shaped search pattern, which is just a π/2 rotation of a square. The search points of diamond shaped search pattern have not been distributed uniformly; therefore each search point cannot be equally exploited with maximum efficiency. After an in-depth investigation on the effect of search pattern shape on search speed, a Hexagonal Search (HS) algorithm [4] has been proposed. In HS, a hexagon-based search pattern, a more circle-approximated search pattern, is used to attain considerable speed improvement against the DS algorithm with almost similar mean square error. The HS employs two search patterns large HS and small HS as shown in Fig 8(a) and 8(b). The large HS contains seven search points such that the search point at centre is enclosed by six endpoints of the hexagon. The two horizontal end points of the hexagon are located with distance 2 from the centre and the remaining four points are away from the centre with distance. It is very clear from the Fig. 8(a) that the six endpoints of the hexagon are almost uniformly distributed around the centre. The search procedure of this algorithm is summarized as follows: Step 1: The large HS is placed at the search window centre (0, 0) and the seven search points of large HS are checked. If the minimum distortion point found is located at the centre, go to Step 3. Otherwise, go to Step 2. Step 2: Form a new large HS with a centre as the minimum distortion point found in the previous search step. If the new minimum distortion point obtained is positioned at the centre of new large HS, go to Step 3. Otherwise, repeat this step. Step 3: Switch the search pattern from large HS to small HS. The four search points enclosed by the small HS are assessed to compare with the current minimum distortion point. The new minimum distortion point obtained is the final motion vector. When large HS is repeatedly used in step 2, the search points are partially overlapped between adjacent steps. In this case, only three new non-overlapped search points need to be checked each time. The total number of search points per block (N) required by the HS algorithm will be, N = 7 + (3 n) + 4. Where n specifies the number of executions of step 2. In equation (1), the number 7 indicates the number of search points checked in step 1, the number 3 indicates number of search points checked in each execution of step 2 and the number 4 indicates number of search points checked with small HS as a final stage editor@iaeme.com

7 A Fast Search Block-Based Motion Estimation Algorithm for Compression of Motion Pictures (a) (b) Figure 8 Search patterns of HS (a) Large HS (b) Small HS 3. THE PROPOSED ALGORITHM We will derive the proposed algorithm from the conventional TSS algorithm. As TSS uses a large search pattern in the first step it finds motion vectors accurately especially for those video sequences with large motion. However, it will be trapped into a local minimum if a video sequence contains small motion. Many centre-biased motion estimation algorithms have been proposed to track the motion field of small motion blocks accurately. But, these algorithms require more number of search points to track the motion field of large motion blocks. In this section, we propose a fast search block-based motion estimation algorithm that tracks the motion field of small motion blocks accurately while it requires less number of search points to find motion vectors of large motion blocks. Generally, the motion vector distribution in real-world video sequences is centre-biased motion vector distribution. In order to exploit this centre-biased motion vector distribution, a small five-point compact search pattern is utilized in the search window centre. The search pattern which is used in the first step of our proposed algorithm is shown Figure 9. The search range is set to ±7 as an example. Therefore, 13 search points will be checked in the first step which is four search points more than TSS and four search points less than NTSS. There are three cases where the minimum distortion point occurs in the first step. Case 1: If the winning point is the search window centre, the search is stopped. This is called the first step stop and the block is treated as stationary block i.e., final motion vector is the origin (0, 0). Case 2: If the minimum distortion point is one of the four points on the compact search pattern, the compact search pattern centre is set to the minimum distortion point and another three search points will be checked. The centre of the small compact search pattern is moved to the winning point each time until the minimum distortion point is found in the small compact search pattern centre. Case 3: If the minimum distortion point in the first step occurs at any one of the remaining eight search points then the following process will be the same as in TSS. Figure 9 Search pattern used in the first step of our proposed algorithm Our proposed algorithm differs from NTSS in that: (1) a small compact search pattern is used instead of a square search pattern in the central area and (2) unrestricted search step for the small compact search pattern rather than a single movement for the small square. The editor@iaeme.com

8 Md. Abdul Rawoof, D.Laxma Reddy, A.V.Paramkusam and Dr. R V Krishnaiah proposed algorithm can be summarized as follows: (The search range is set to ±7 as an example). Step 1: Select the origin (zero-motion vector) as the centre point. Select the eight search points at a distance of four pixels from the centre. In addition to these eight search points, four extra search points on a small compact search pattern in the centre are added. If the minimum distortion point occurs at search window centre, the search will be stopped. Otherwise, go to step 2. Step 2: If the minimum distortion point in step 1 occurs at one of the eight search points on the 9 9 grid, the rest of the search procedure is the same as in TSS. Otherwise, go to step 3. Step 3: Move the small compact search pattern so that its centre is the previous minimum distortion search point. Continue to search the other search points on the compact search pattern. There is no restriction on the search step unless the minimum distortion search point is the small compact search pattern centre or the small compact search pattern reaches the boundary of search window. The block diagram of the proposed algorithm in detail is shown in Fig.10. In Fig. 11, we show the example of a search path for finding motion vector (-3, -l) when the minimum distortion search point in the first step is (-1, 0). It is clear that when the minimum distortion search point is found in one of the compact search pattern points around centre (0, 0) in the first step, employing unrestricted search step can increase the probability of finding the true motion vector which is positioned near the central 5 5 area. In general, our proposed algorithm requires 13 search points for stationary blocks and 16 to 21 search points for quasi-stationary blocks (the blocks with small motion vectors within central 5 5 area) compared with 17, 20 or 22 points needed in NTSS respectively. For the worst case, =29 points are required while 25 and 33 search points are required in TSS and NTSS respectively. Figure 10 Block diagram of the proposed algorithm editor@iaeme.com

9 A Fast Search Block-Based Motion Estimation Algorithm for Compression of Motion Pictures Figure 11 A search path for finding motion vector (-3, -l) 4. SIMULATION RESULTS The experiments have been conducted by using the luminance components of the first 150 frames of six video sequences with different resolutions, Mobile ( ), Cricket ( ), Foreman ( ), Suzie ( ), Akiyo ( ) and Flower ( ). The first three video sequences cover medium to large motions while the other video sequences contain relatively small motion. The distortion measurement of Sum of Absolute Difference (SAD) is used as the block distortion measurement. The search range is set to both ±7 and ±15 for a block size of Two measures have been used for comparing our proposed algorithm against four other well-known fast Block-Based Motion Estimation (BB-ME) algorithms TSS, NTSS, DS and HS. They are Peak Signal to Noise Ratio (PSNR) and the Average number of Search Points per block (ASP). The experimental results for comparing our proposed algorithm against TSS, NTSS, DS and HS have been summarized in Tables 1 to 6. From Table 1, 2 and 3, it is obvious that for video sequences which contain medium to large motion objects and regardless of search range the proposed algorithm provides the better image prediction quality in terms of PSNR when compared to other algorithms while its computational complexity is less than TSS and NTSS and comparable to DS and HS. Table 3, 4 and 5 show that the proposed algorithm is also apt for tracking small motions. It achieves similar or larger PSNR than that of NTSS, DS and HS while it requires fewer search points than those of NTSS, DS and HS. On an average the proposed algorithm needs three to four search points fewer than the NTSS thus a 15% speed up ratio against NTSS can be achieved. Furthermore, it can be found that when using large search range, the search points required for TSS increased dramatically due to the increasing search range while the computational cost for the proposed algorithm, NTSS, DS and HS only changed slightly. Therefore, the proposed algorithm is more robust than the other fast BB-ME algorithms. Figure 12 (a) and 12 (b) show the frame by frame comparison of the proposed algorithm, TSS, NTSS, DS and HS in terms of PSNR and the ASP respectively for the Cricket video sequence using search range ±15. Figures 12 (a) and 12 (b) clearly evident the superiority of the proposed algorithm in contrast to TSS, NTSS, DS and HS algorithms in terms of PSNR and the ASP respectively editor@iaeme.com

10 Md. Abdul Rawoof, D.Laxma Reddy, A.V.Paramkusam and Dr. R V Krishnaiah 5. CONCLUSION In this paper, a fast search block-based motion estimation algorithm has been proposed. By using a small compact search pattern in the first step, the unrestricted search step has been used to search the center-biased motion vectors. For both large and small search ranges, experimental results clearly show that the proposed algorithm performs better than the TSS, NTSS, DS and HS in terms of PSNR with fewer or comparable number of search points for the video sequences which contain medium to large motion. Table 1 Performance comparisons PSNR in db and ASP for Mobile video sequence. BB-ME search range = ±7 search range = ±15 algorithms PSNR ASP PSNR ASP FS TSS NTSS DS HS proposed algorithm Table 2 Performance comparisons PSNR in db and ASP for Cricket video sequence. BB-ME search range = ±7 search range = ±15 algorithms PSNR ASP PSNR ASP FS TSS NTSS DS HS proposed algorithm Table 3 Performance comparisons PSNR in db and ASP for Foreman video sequence. BB-ME algorithms search range = ±7 search range = ±15 PSNR ASP PSNR ASP FS TSS NTSS DS HS proposed algorithm editor@iaeme.com

11 A Fast Search Block-Based Motion Estimation Algorithm for Compression of Motion Pictures Table 4 Performance comparisons PSNR in db and ASP for Suzie video sequence. BB-ME search range = ±7 search range = ±15 algorithms PSNR ASP PSNR ASP FS TSS NTSS DS HS proposed algorithm Table 5 Performance comparisons PSNR in db and ASP for Akiyo video sequence. BB-ME search range = ±7 search range = ±15 algorithms PSNR ASP PSNR ASP FS TSS NTSS DS HS proposed algorithm Table 6 Performance comparisons PSNR in db and ASP for Flower video sequence. BB-ME algorithms search range = ±7 search range = ±15 PSNR ASP PSNR ASP FS TSS NTSS DS HS proposed algorithm editor@iaeme.com

12 Md. Abdul Rawoof, D.Laxma Reddy, A.V.Paramkusam and Dr. R V Krishnaiah 12 (a) 12 (b) It also has similar performance compared with NTSS, DS and HS when searching for small motion vectors. Therefore, the proposed algorithm is fit for a wide range of video applications and particularly for video sequences which consists of complex and large motion like Cricket video sequence, etc. REFERENCES [1] T. Koga, K. linuma, A. Hirano, Y. Iijima, and T. Ishiguro, Motion compensated interframe coding for video conferencing, in Proc. Nat. Telecommun. Conf., 1981, pp. C9.6.1 C [2] R. Li, B. Zeng, and M.L. Liou, A New Three-step Search Algorithm for Block Motion Estimation, IEEE Trans. Circuits Syst. Video Technol., vol. 4, no. 4, pp , Aug [3] S. Zhu and K.K. Ma, A New Diamond Search Algorithm for Fast Block-matching Motion Estimation, IEEE Trans. Image Processing, vol. 9, no. 2, pp , [4] Ce Zhu, Xiao Lin, and Lap-Pui Chau, Hexagon-based Search Pattern for Fast Block Motion Estimation IEEE Trans. on Circuits and Systems for Video Technology, Vol. 12, No. 5, pp , may editor@iaeme.com

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