1. Adjustable Partial Distortion Search Algorithm for Fast Block Motion Estimation Chun-Ho Cheung and Lai-Man Po Department of Electronic Engineering, City University of Hong Kong IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 13, NO. 1, JANUARY 2003 VOL. 13, NO. 1, JANUARY 2003

2. Outline Introduction to partial distortion search algorithm Progressive partial distortion  Preliminaries  Fast BMA and Searching Speed Limitation  Formation of PPD  PPDS Algorithm Adjustable partial distortion comparison Experimental results

3. Partial distortion search algorithms Alternating Subsampling Search Algorithm (ASSA) {Pixel-decimated (4 : 1)} Normalized Partial Distortion Search algorithm (NPDS) {early rejection, halfway-stop}

4. Partial distortion search algorithms Without limitation of checking points Lack the flexible adjustability between the prediction quality and searching speed  Adjustable Partial Distortion Search algorithm (APDS) Increase the searching speed of NPDS by introduction of progressive partial distortions (PPD) at very early stages. Adjustable partial distortion comparison method with enabling the quality/speed control.

5. Progressive Partial Distortion - Preliminaries The basic operations of computing SAE are absolute and addition operations, and require about (3N 2 -1) operations per BDM.

6. Progressive Partial Distortion - Fast BMA and Searching Speed Limitation For one second of K -Hz I x J video sequence with search windows ± W  K(I/N)(J/N)(3N 2 -1)(2W+1) 2 3SS  Minimizing the searching points in (2W+1) 2 ASSA and NPDS  Reduce the BDM’s (3N 2 -1)  ASSA => 4 times speedup  ASSA + subblock or subsampled motion field => 8 times speedup  NPDS => 12-13 times speedup

7. Conventional partial distance search algorithm Pixel-by pixel basis Obtains the optimal solution as in FS The basis for developing NPDS 19313 115157 414210 168126 S = 4 T = 4 P =16

8. NPDS Saving of multiples of 16-pixel matching- operations It limits the maximum possible speedup ratios to Num(block)/Num(d 1 ) or 16 times theoretically. PPDS are proposed to be used in the first few stages of NPDS for increasing the rejection rate. Number of pixel in the candidate block First partial distortion d 1 Also, wider range of quality control

9. Formation of PPD Theoretically, it is a combinational-nature problem to divided d 1 into smaller partitions. Regularity of the PPD patterns favors both hardware and software Implementations. P = 16 partitions

10. Proposed PPD patterns 614816 102124 715513 11391 3748 5162 4837 6251 2424 3131 2424 3131 1212 2121 1212 2121 4646 5253 4646 5351 3535 4142 3535 5241 1535 5454 3525 5454 2323 3131 2323 3131 (a)PPDS(v1) Group of 1 pixel (b)PPDS(v2) Group of 2 pixels (c)PPDS(v3) Group of 4 pixels (d)PPDS(v4) Group of 8 pixels (h)PPDS(v8) (4,4,8) (g)PPDS(v7) (1,1,2,4,8) (f)PPDS(v6) (2,2,4,4,4) (e)PPDS(v5) (1,1,2,4,4) Total G partial distortions G=H+P-1 H=16, G=31 H=8, G=23H=4, G=19H=2, G=17 H=6, G=21 H=5, G=20 H=3, G=18

11. An Example 2424 3131 2424 3131 (c)PPDS(v3) Group of 2 pixels Total G partial distortions G=H+P-1 H=4, G=19 For d g | 1≤g≤4, For d g | 5≤g≤19, same as the pervious PPD Formulation.

12. PPDS Algorithm f(n,k)=n, where n is the number of pixels cumulated in D g Normalized Distortion Comparison criteria (NDC): Adjustable function:

13. Experimental results COMPUTATIONS AND PSNR (dB) PERFORMANCE COMPARISON

14. COMPUTATIONS AND PSNR (dB) PERFORMANCE COMPARISON

16. Performance comparison of APDS(k) at several quality factor k PPDS(v3)

17. Performance comparison of APDS(k) at several quality factor k PPDS(v3)

18. Performance comparison of APDS(k) at several quality factor k PPDS(v3)

19. Average PSNR performance and speedup ratios SIF, “tennis”

20. Average distance from and probability of the true motion vector per block against the quality factor k SIF, “tennis”

21. Average PSNR performance and speedup ratios CCIR601, “tennis”

22. Average distance from and probability of the true motion vector per block against the quality factor k CCIR601, “tennis”

23. MSE performance

24. Operations

25. Distance per block

26. Probability per block

27. Operations Translation in RHS

28. Conclusions NPDS + different PPD  Computational reduction up to 61.54 times with less than 0.94-db degradation on PSNR compared to FS’s. The APDS is very suitable for a wide range of video applications such as low-bit-rate video conferencing and high-quality video coding.