Volume No - 5, Issue No - 1, January, 2017 Enhanced Hexagon with Early Termination Algorithm for Motion estimation Neethu Susan Idiculay Assistant Professor, Department of Applied Electronics & Instrumentation, Mount Zion College of Engineering Kadammanitta, Kerala, India E-mail: susanidiculay@gmailcom Abstract: To achieve a high compression ratio in coding video data, a method known as Motion Estimation (ME) is often applied to reduce the temporal redundancy between successive frames of a video sequence One of the ME techniques, known as Block Matching Algorithm (BMA), has been widely used in various video coding standards In recent years, many of these BMAs have been developed with similar intention of reducing the computational costs while at the same time maintaining the video signal quality Here, an algorithm called Enhanced Hexagon with Early Termination Algorithm is proposed with either Small Cross Search Pattern (SCSP) or X-Shaped Pattern (XSP) in the initial step This is implemented in MATLAB and is tested with different standard video sequences Experimental results show that the proposed XSP based algorithm is better than Enhanced Hexagonal Search (EHS) in terms of average number of search points and computational complexity Keywords: Block Matching Algorithm, Enhanced Hexagon Search, Inner search, Motion Estimation, Small Cross Search Pattern, X-Shaped Pattern, Video compression I INTRODUCTION Video compression is the field in engineering that deals with representation of video data when transmitting or storing video sequences for both analog and digital video The biggest challenge is to reduce the size of the video data using video compression Since the early 1980s, the video compression exploits temporal redundancy by motion compensation and spatial redundancy by DCT transformation, and it has been widely adopted by H261, H262 and H263 standards [1], [2], since then much other advancement have been made to algorithms such as motion estimation (ME) Video coding research efforts have been concentrated towards the exploitation of the temporal redundancy using motion estimation Motion estimation is used to obtain the motion vectors (MV) that match the actual motion content Digital video coding has gradually increased in importance since the 90s when MPEG-1 first emerged It is required in video delivery, storage and presentation Motion estimation is a major part in such compression systems Compared to analog video, video coding achieves higher data compression rates without significant loss of subjective picture quality It is more convenient to view video as a sequence of correlated images Most of the video compression algorithm makes use of the temporal correlation to remove redundancy Block matching motion estimation (BMME) is the most widely used motion estimation method for video coding standards such as H26 series and MPEG series With the aim to find the best matched block in the reference frame, it is natural to utilize full search (FS) or exhaustive search (ES) comparing all candidate blocks within the search window, but results in high computational complexity In the last decade, several fast block matching algorithms (BMAs) have been proposed, which reduces the computation of full search To speed up searching process, many efficient approaches were proposed successively, such as 2D logarithmic search [3], three-step search (TSS ) [4], new three step search (NTSS) [5], four-step search (FSS) [6] and diamond search (DS) [7], [8], etc In TSS, NTSS, FSS, square-shaped search patterns of different sizes are adopted, while DS employs a diamondshaped search pattern Hexagon shaped (HS) [9] pattern adopts the same searching strategy as DS The purpose of turning to a hexagonal shape is that it is closer to the shape of a circle than a diamond and it also requires a lower number of search points Therefore further development was on hexagon shaped method (HS), which uses a more circle-approximated search pattern and is more computationally efficient than DS Fast inner search is highly desirable to further reduce search points An Enhanced Hexagonal Search (EHS) [10] was proposed to further speedup the original HS algorithm EHS improves the inner search speed This algorithm groups the inner points according to six sides of hexagon, and only checks a portion of inner points with smallest group error Later, an Enhanced Hexagonal based Search using Point Oriented Inner Search (EHS-POIS) [11] and Direction Oriented Inner search (EHS- DOIS) [12] were proposed In this paper, a new algorithm called Enhanced Hexagon with Early Termination Algorithm for motion estimation is proposed This algorithm employs Enhanced Hexagon Search (EHS) with Small Cross Search Pattern (SCSP)/X-Shaped Pattern (XSP) in the early stage This was proposed to speed up the original EHS algorithm This will further reduce the number of search points with the concept of early termination The rest of this paper is organized in the following manner Section II focuses on the fundamental concepts of motion estimation Section III describes an view of selected block matching algorithms Section IV introduces proposed Enhanced Hexagon with Early Termination Algorithm for motion estimation Section V describes experimental results and presents a comparison of the proposed search with other fast search block matching algorithms Finally, a brief conclusion is given in Section VI RES Publication 2012 Page 55 wwwijmeceorg
Volume No - 5, Issue No - 1, January, 2017 II MOTION ESTIMATION Motion estimation (ME) is a process to estimate the pels or pixels of the current frame from reference frame Block matching motion estimation is a technique to remove temporal redundancy between two or more successive frames The technique of block matching estimation is used in most video coding systems and standards such as H261 and MPEG series due to its efficiency calculates the best match using Mean Absolute Difference (MAD) as given in equation (1) MAD 1 N N 1 N 1 C 2 i 0 j 0 where i, j varies from 0 to N-1, N is the side of the macroblock, C ij and R ij are the pixels being compared in current macroblock and reference macroblock respectively ij R ij (1) III LITERATURE SURVEY ON SELECTED BLOCK MATCHING ALGORITHMS Figure 1 Video Encoder/Decoder Diagram Figure 1 shows the block diagram of a video encoder/decoder diagram [13] The encoding side estimates the motion in the current with respect to a previous frame A motion compensated image for the current frame is created The motion estimator then calculates a motion vector The encoded image that is sent is then decoded The decoding process can be thought of as the reverse of the encoding process and creates a full frame The received encoded data is Huffman/run-level decoded Motion vectors are passed from the data stream and fed to the motion compensator A Hexagonal Search (HS) This method [9] makes use of two search pattern One is large hexagon search (LHS) pattern, which consists of seven checking points with the center surrounded by six endpoints of the hexagon The hexagonal search pattern also contains two fewer checking points than the 9-point DS pattern In the search process, the hexagon-based search pattern keeps advancing with the center moving to any of the six endpoints Whichever endpoint the center of the search pattern moves to, there are always three new endpoints emerging, and the other three endpoints are being lapped Other one is a small hexagon search (SHS) pattern, a smaller shrunk hexagonal pattern cing four checking points in the motion field, which is finally used in the focused inner search as shown in Fig 3 The shrunken hexagonal pattern includes the same checking points as the shrunk diamond pattern For a stationary motion vector, the HS algorithm consists of 11 search points where as the DS algorithm consists of 13 points In short, the HS algorithm can find the same motion vector in the motion field with fewer search points than the DS algorithm Figure 3 Hexagonal Search pattern Figure 2 Block-matching Motion estimation Fig 2 illustrates the process of block matching motion estimation In block matching motion estimation, the current frame is initially divided into square blocks of pixels Then it calculates the displacement of the best matched block from the previous frame as the motion vector of the block in the current frame within the search window The best matched block is usually evaluated through a cost function based on Block Distortion Measure such as Mean Square Error (MSE), Mean Absolute Difference (MAD) or Sum of Absolute Difference (SAD) [1], [2] Block matching algorithms B Enhanced Hexagonal Search (EHS) On top of the hexagonal search method developed, an enhanced hexagonal search algorithm is proposed to further improve the performance in terms of reducing number of search points and distortion, where a fast inner search is employed by exploiting the distortion information of the evaluated points In EHS, the algorithm is divided into two parts One is a low resolution search which cs a maximum searching area and finds a small region where the motion vector lies The other one is the inner search which finds the best motion vector inside the small region RES Publication 2012 Page 56 wwwijmeceorg
Volume No - 5, Issue No - 1, January, 2017 The proposed EHS algorithm [10] can be summarized as follows Initially the six sides of the hexagon is considered as six groups or pairs For each group, we define a group distortion by summing the distortions of all the points within the group The minimum distortion is found in the group with which has the smallest group distortion Therefore we focus the inner search just in the region near to the group with the smallest group distortion For different groups in different locations, we have different number of inner search points, as shown in Fig 4a and Fig 4b used as the initial search pattern as shown in Fig 5a and Fig 5b Grouping the search points in six sides of the hexagon, resulting in six groups (pairs) of points is used as the Enhanced Hexagonal Search (EHS) pattern Figure 5a Small Cross Search Pattern (SCSP) Figure 5b X-Shaped Pattern (XSP) Figure 4a Three inner points nearest to Group 5 in EHS Figure 5 Search Patterns used in the initial step of the proposed algorithm B Proposed Algorithm The flow chart of proposed Enhanced Hexagon with Early Termination algorithm is shown in Fig 6 Figure 4b Two inner points nearest to Group 4 in EHS Figure 4 Search patterns used in EHS algorithm If the smallest distortion group is Group 2 or 5, three checking points nearest to the smallest distortion group will be used in the focused inner search If the smallest distortion group is Group 1, 3, 4, or 6, two inner points nearest to the smallest distortion group will be evaluated in the focused inner search Since in this proposed method the final inner search is constrained to one side of the hexagon with the minimum average distortion, only part of the inner points will be checked IV PROPOSED ENHANCED HEXAGON WITH EARLY TERMINATION ALGORITHM A Search Patterns Used in Proposed Algorithm Enhanced Hexagon with Early Termination algorithm employs Enhanced Hexagonal Search (EHS) [10] with Small Cross Search Pattern (SCSP)/X-Shaped Pattern (XSP) This algorithm is proposed to reduce the average number of search points per block, in which it has a significant effect on motion estimation time The search patterns used in the initial step of the proposed algorithm is shown in Fig 7 A small cross search pattern (SCSP)/X-Shaped Pattern (XSP) with five checking points centered at the origin of the search window is Figure 6 Flow chart of the proposed algorithm RES Publication 2012 Page 57 wwwijmeceorg
Volume No - 5, Issue No - 1, January, 2017 The proposed algorithm is summarized as follows Step 1: A Minimum Block Distortion (MBD) is found from the five search points of the Small Cross Search Pattern (SCSP)/X-Shaped Pattern (SCSP) located at the center of the search window If the minimum block distortion point occurs at the center of the SCSP/XSP, the search stops Otherwise go to step 2 Step 2: The search pattern is changed from SCSP/XSP to Hexagonal Search (HS) by repositioning the MBD point found in the previous step as the center of HS If the Minimum Block Distortion point occurs at the center of the new Hexagon Search pattern, go to step 3 Otherwise repeat step 2 Step 3: The search pattern is changed from HS pattern to Enhanced Hexagonal Search (EHS) pattern Now, the MBD point is found in the six groups of the hexagon The area near to the group with the smallest group distortion is considered as the region in which the minimum distortion is most likely to be found and the corresponding inner search is to be done The MBD found in this step is the final solution of the motion vector which points to the best matching block V SIMULATION RESULTS A Performance Measures A number of different criteria are used for evaluating the proposed algorithm for motion estimation They are 1 Average number of search points per block 2 Speed Improvement Rate To show the speed performance, these different comparisons are done The number of search points per block has a significant effect on motion estimation time The number of search points per block reflects the absolute speed of different algorithms, and the Speed Improvement Rate (SIR) reflects the speed up percentage relative to the original search The Speed Improvement Rate (SIR) [10] of method 1 method 2 is defined by SIR N N N 100% 2 1 / 2 (1) where N 1 is the number of search points used by method 1 and N 2 is that by method 2 B Experimental Setup Performance of the proposed Enhanced Hexagon with Early Termination algorithm has been evaluated using four different video sequences While performing the experiment, Mean Absolute Difference (MAD) [2] is used as block distortion measure (BDM) The experimental set up is as follows Four standard video sequences Akiyo,, Traffic and Miss were used which vary in motion content as well as frame size For all simulation work, the block size is fixed at 16 x 16 The maximum displacement allowed is set at ±7 in horizontal and vertical directions Several selected block matching algorithms (BMAs), namely Hexagonal Search (HS) and Enhanced Hexagonal Search (EHS) are first implemented onto standard video sequences using MATLAB Then the proposed algorithm was implemented Their performances are then compared and analyzed in terms of average number of search points per block and Speed Improvement Rate (SIR) in order to determine the suitability of the proposed algorithm for different motion content represented in those video sequences C Results and Discussion Table I shows the performance of the proposed Enhanced Hexagon with Early Termination algorithm several selected block matching algorithms (BMAs), such as Hexagonal Search (HS) and Enhanced Hexagonal Search (EHS) TABLE I AVERAGE NUMBER OF SEARCH POINTS HS 103047 107227 145859 191406 EHS 101250 106563 143906 190352 EH with SCSP EH with XSP 94102 95625 106992 119336 91602 92930 100195 101992 Table I shows the average number of search points taken for different block matching algorithms with four standard video sequences Results show that the proposed algorithm has the lowest average number of search points per block for each type of video sequence compared to EHS TABLE II PROPOSED ALGORITHM USING SCSP SPEED IMPROVEMENT RATE (%) OVER VARIOUS ALGORITHMS RES Publication 2012 Page 58 wwwijmeceorg HS EHS 868 1082 2665 3765 706 1026 2565 3731 Table II shows the Speed Improvement Rate (SIR) of the proposed algorithm using SCSP HS and EHS algorithms The Speed Improvement Rate (SIR) of the proposed algorithm using SCSP HS and EHS are around 8% to 37% and 7% to 37% respectively
Volume No - 5, Issue No - 1, January, 2017 TABLE III PROPOSED ALGORITHM USING XSP SPEED IMPROVEMENT RATE (%) OVER VARIOUS ALGORITHMS HS EHS Table III shows the Speed Improvement Rate (SIR) of the proposed algorithm using XSP HS and EHS algorithms The Speed Improvement Rate (SIR) of the proposed algorithm using XSP HS and EHS are around 11% to 46% and 9% to 46% respectively Table III show that the proposed approach can reduce the computational complexity significantly VI CONCLUSION The motion estimation of video processing was a popular research in recently decade years Many of the block matching algorithms have been developed with similar intention of reducing the computational cost while at the same time maintaining the video signal quality Enhanced Hexagon with Early Termination algorithm has been proposed here This is implemented onto several types of standard test video sequences in MATLAB The performances of the proposed algorithm are then compared with several selected block matching algorithms in terms of search points and speed improvement rate Simulation results shows that the proposed algorithm in terms of number of required search points is minimum compared to enhanced hexagonal search algorithms At the same time, the results show that proposed algorithm has maintained a close search quality performance to those selected block matching algorithms Experimental results show that the proposed Enhanced Hexagon with Early Termination algorithm approach can reduce the computation complexity compared to Enhanced Hexagonal Search REFERENCES 1111 1333 3131 4671 953 1279 3037 4641 [1] K R Rao and J J Hwang, Techniques and Standards for Image, Video and Audio Coding, Englewood Cliffs, NJ: Prentice Hall, 1996 [2] T Wiegand, G J Sullivan, G Bjontegaard, and A Luthr, Overview of the H264/AVC video coding standard, IEEE Trans Circuits Syst Video Technol, vol 13, no 7, pp 560-576, Jul 2003 [3] J R Jain and A K Jain, Displacement measurement and its application in interframe image coding, IEEE Trans Commun, vol 29, no 12, pp 1799-1808, Dec 1981 [4] T Koga, K Iinuma, A Hirano, Y Iijima, and T Ishiguro, Motion compensated interframe coding for video conferencing, in Proc Nat Telecommun Conf, New Orleans, LA, pp G531-G535, Nov-Dec 1981 [5] 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 438-443, Aug1994 [6] L M Po and W C Ma, A novel four-step search algorithm for fast block motion estimation, IEEE Trans Circuits Syst Video Technol, vol 6, no 3, pp 313-317, Jun1996 [7] J Y Tham, S Ranganath, M Ranganath and A A Kasim, A novel unrestricted center-biased diamond search algorithm for block motion estimation, IEEE Trans Circuits Syst Video Technol, vol 8, no 4, pp 369-377, Aug 1998 [8] S Zhu and K K Ma, A new diamond search algorithm for fast block matching motion estimation, IEEE Trans Image Process, vol 9, no 2, pp 287-290, Feb2000 [9] C Zhu, X Lin, and L P Chau, Hexagon-based search pattern for fast block motion estimation, IEEE Trans Circuits Syst Video Technol, vol 12, no 5, pp 349-355, May 2002 [10] C Zhu, X Lin, L P Chau and L M Po, Enhanced hexagonal search for fast block motion estimation, IEEE Trans Circuits Syst Video Technol, vol 14, no 10, pp 1210-1214, Oct 2004 [11] L M Po, C W Ting, K M Wong, and K H Ng, Novel point-oriented inner searches for fast block motion estimation, IEEE Trans on Multimedia, vol 9, no 1, pp 9-15, Jan 2007 [12] C Zhu, X Lin, and L P Chau, Enhanced hexagonal based search using direction oriented inner search for motion estimation, IEEE Trans Circuits System, Video Technology, vol 20, no 1, pp 156-160, Jan 2010 [13] Khalid Sayood, Introduction to Data Compression, 2nd edition, Morgan Kauffman Harcourt, India, 2000 RES Publication 2012 Page 59 wwwijmeceorg