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Fast Motion Estimation Algorithms

Fast Motion Estimation Algorithms. Master: Prof. Hashemi By: Amir Shahrokhi. Progress. What has been done: Download Papers about ME Specially Block Matching Survey Papers and Their References What will be done: Simulate Some of Proposed Algorithms Using MATLAB

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Fast Motion Estimation Algorithms

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  1. Fast Motion Estimation Algorithms Master: Prof. Hashemi By: Amir Shahrokhi

  2. Progress • What has been done: • Download Papers about ME Specially Block Matching • Survey Papers and Their References • What will be done: • Simulate Some of Proposed Algorithms Using MATLAB • Comparison of different algorithms

  3. Outline • Fast Motion Estimation Algorithms: • Introduction • Block Matching Algorithms • Full Search Method • 2-D Logarithmic Search • 3-Step Search • Conjugate Direction Search • Diamond Search

  4. Introduction • Temporal correlation between successive frames • An easy way to remove this redundancy: • Frame Replenishment: • A threshold for difference in pixel intensity from previous frame • If less than threshold  Send nothing • If more than threshold  Send position & intensity difference • Advantage: Easy to implement, Low complexity • Disadvantage: Low compression when rapid changes  In low bit-rates may cause scene cuts • Better way: Motion Compensated Coding

  5. Introduction (Continue) • Motion Compensated Coding • Frame changes are considered as object movements so: • Estimate motion of current frame respect to previous • Calculate difference between motion compensated & current frames • Code motion vectors and differences as this frame • To have better compression we need to good motion estimation • Different algorithms for motion estimation • Block Matching (Our Discussion) • Pel Recursive Technique • Optical Flow

  6. Outline • Fast Motion Estimation Algorithms: • Introduction • Block Matching Algorithms • Full Search Method • 2-D Logarithmic Search • 3-Step Search • Conjugate Direction Search • Diamond Search

  7. Block Matching • Divide current frame to small rectangular blocks • Motion of each block is assumed to be uniform • Find the best match for each block in previous frame • Calculate motion vector (MV) between current block and its counterpart in previous frame • Typical size for blocks: 16x16 pixels • Maximum movement: w: typically 8, 16 or 32 • Matching Criteria: • Mean Absolute Error (MAE) • Mean Square Error (MSE) • Sum of the Squared Error (SSE) • MAE is preferred due to its simplicity

  8. Block Matching • Search Window (in previous frame) • Rectangle with the same coordinate as current block in current frame, extended by w pixels in each directions q+2w w p+2w q w w p w

  9. Block Matching • Full Search • All candidates within search window are examined • (2w+1)2 positions should be examined • Advantage: Good accuracy, Finds best match • Disadvantage: Large amount of computation: (2w+1)2 matches, 16x16 MAE for each match  Impractical for real-time applications • In order to avoid this complexity, we should reduce search positions  Fast Block Matching Algorithms

  10. Block Matching (Fast Algorithms) • Improvements to Full Search • Successive Elimination Algorithm (SEA) • Li, Salari • Using SAD as matching criterion • Idea: excluding many search positions while finding still best match • Let f(x,y) be the intensity of each pixel in current block and f’(x’,y’) be intensity of each pixel in a current search position, and (x,y) be the counterpart of (x’,y’), so from triangular inequality: • |S f(x,y) – S f’(x,y)| <= S |f(x,y) – f’(x’,y’)| = SAD • So difference between sum of intensity values can be used as a lower bound for SAD value. This bound is used to exclude search points • Cost is to calculate sum of intensities, search points differ from each other in 2 rows or columns and they have a great overlap  Sum of intensities can be calculated efficiently

  11. Block Matching (Fast Algorithms) • 2-D Logarithmic Search • Jain and Jain • Examine central point & its four surroundings • Distance from center: w/2 • Find best match • If best match is in center or on boundaries, half distance from center • Examine five new points centering previous best • When distance is 1, use all 9 matches, find best. Stop 2 1 2 1 1 1 4 4 4 3 4 2 4 1 4 4 4 3

  12. Three-Step Search (3SS) Koga et al 9 Points: Central point & its 8 surroundings Distance: w/2 Find the best match Use previous best as center Half distance, select 8 new Repeat algorithm 3 times Examines 25 points Assumes a uniform distribution of MV’s Block Matching (Fast Algorithms) 1 1 1 1 1 1 2 2 2 3 3 3 3 2 3 1 2 1 1 3 3 3 2 2 2

  13. Block Matching (Fast Algorithms) • Improvements to 3SS • New Three-Step Search (N3SS) • Li, Zeng, Liou • Experiments show that most of best matches are near center • More points in center: 8 neighbors of center of search area (33 Search points) • More robust & Smaller error in motion compensation • Four-Step Search (4SS) • Po, Ma • The idea is same as N3SS, a hybrid of 3SS and 2DL, (For w=8) • Central point and 8 surrounding with distance = 2 • If best match on center, distance = 1 select 8 new points and end else select best match as new center with previous distance (2) • If following step repeated 3 times, distance=1, select 8 new points and end • 2 to 4 steps  17-27 search points, Less search points on average • Better MSE than 3SS & less search points than N3SS, Better prediction

  14. Conjugate Direction Search Srinivasan, Rao Start from center point, find best match between this point & its 2 horizontal neighbors Search 1 point in the direction of best match & Examine If no change in best match in horizontal, repeat for vertical When no change in vertical, select the point as best match worst case 2w+3 points Block Matching (Fast Algorithms) 7 6 5 4 3 2 1 1 1 5

  15. Block Matching (Fast Algorithms) • Improvement to CDS • Fast One-Step Search (FOSS) • Srinivasan, Ramachandran • 5 macro-blocks : center of search area and 4 points between center and 4 borders of search area • Calculate number of search points for these macro-blocks in eight directions • Direction with least number of search points is used as optimal for CDS • This is done for first P-frame after I-frame • This direction is unique for all P-frames in a GOP • Reduces search points by 2, with the cost of 0.4% overhead of overall compute • Better direction select and faster than CDS

  16. Block Matching (Fast Algorithms) • Diamond Search (DS) • Zhu, Ma • Start with 9 points creating a large diamond with center of search window in center • Find best match, repeat with this best match as center • If best match is in center, use 5 points creating small diamond, find best match, this is target • Better performance than 3SS • Less complex than N3SS and 4SS, with same performance

  17. Block Matching (Fast Algorithms) • DS Example 2 3 1 2 4 3 1 1 4 2 4 3 1 1 1 4 3 1 1 3 1

  18. References • “FPGA Implementation of a Novel, Fast Motion Estimation Algorithm for Real-Time Video Compression”, FPGA 2001, CA. USA, S. Ramachandran and S. Srinivasan, Feb. 2001 • “Image & Video Compression for Multimedia Engineering”, Y.Q. Shi and H. Sun, 2000 • “A New Diamond Search Algorithm for Fast Block-Matching Motion Estimation”, IEEE Trans. Image Processing, S. Zhu and K. K. Ma, Feb. 2000 • “A Novel Four-Step Search Algorithm for Fast Block Motion Estimation”, IEEE Trans. Circuits System, Video Technology, L. M. Po and W. C. Ma, June 1996 • “Successive Elimination Algorithm for Motion Estimation” W. Li and E. Salari IEEE Trans. , Jan. 1995 • “A New Three-Step Search Algorithm for Block Motion Estimation”, IEEE Trans. Circuits System, Video Technology, R. Li, B. Zeng, and M.L. Liou, Aug. 1994 • “Predictive Coding Based on Efficient Motion Estimation”, IEEE Trans. on communications, R. Srinivasan, K.R. Rao, Aug. 1985 • “Motion Compensated Inter-Frame Coding for Video-Conferencing”, T. Koga, K. Iinuma, A. Hirano, Y. Iijima, and T. Ishiguro, Proc. NTC81, Nov. 1981 • “Displacement Measurement and its Applications”, IEEE Trans. on communications, J.R. Jain and A.K Jain, Dec. 1981

  19. Any Question?

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