1 / 19

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

Lucy
Download Presentation

Fast Motion Estimation Algorithms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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?

More Related