1. Digital 3D Video: Chapter 8 Motion Estimation and Compensation
楊 家 輝
Jar-Ferr Yang
電腦與通信工程研究所 電機工程學系 國立成功大學
Institute of Computer and Communication Engineering
Department of Electrical Engineering
National Cheng Kung University, Tainan, Taiwan

2. Interframe Compression Methods
Video signal, which is a sequence of images, can be treated as a 3-D signal varying along x-axis, y-axis, and t-axis.
Image
Time x y
Interframe compression can be further improved than intraframe compression, since we can further use temporal information (i.e., previously-coded images)

3. Motion-Compensated Coding
MC-DPCM
Previously-coded Frame at t-1
f(x+△ x, y+△y, t-1) ^
Current Frame at t
Extra Coding Parameter, Motion vector: (Dx, Dy)
f(x, y, t)
Difference: d(x, y, t) = f(x, y, t) – f(x+Dx, y+Dy, t-1) ^
f(x,y,t)
Lossless
Encoder
PCM
d(x,y,t) ^
Image
Buffer
f(x,y, t)
f(x+Dx,y+Dy,t-1) + _
d(x,y,t) ^
c(x,y,t)
DPCM
Decoder
Image
Buffer
f(x,y,t) ^
f(x+Dx,y+Dy,t-1) +
Motion
Estimation
(Dx, Dy, t)
(Dx, Dy, t)

4. MC-Transform Hybrid Coding
World Video Standards: Motion Compensated-DCT Hybrid Coding Methods
1. H.261/H.262/H.263
ITU (CCITT) for Visual Telephony
2. MPEG-1/MPEG-2/MPEG-4
ISO Moving Picture Expert Group
To be discussed in the following chapters!
How to transmit motion information?
DCT-
based
Coder
d(x,y,t)
Image
Buffer
f(x,y, t) ^
f(x,y, t)
Lossless
Encoder
c(x, y, t)
DCT-
based
Decoder +
Motion
Estimation _ + + ^ +
f(x+Dx,y+Dy,t-1) + ^
f(x+Dx,y+Dy,t-1)
f(x,y,t) ^
Image
Buffer
(Dx, Dy,t)
(Dx, Dy,t)

5. Video Compression Concepts
Spatial Redundancies => DCT and Quantization
*Adjacent pixels with similar information
Spatial Redundancy
Temporal
axis
Time, T-1
Time, T
Conceptually, the temporal redundancy contains in consecutive frames. If we can properly estimate the motion of moving target, the subtraction of the target block in the current frame and the estimated block in the previously-encoded frame become very small.
On the other hand, the spatial redundancy contains in similar value in adjacent pixels. Due to energy compaction property, a few DCT coefficient can sufficient represent the spatial information.
Temporal Redundancies =>Motion Compensation
* Frame to frame redundancies

6. MC in Block-based Approach
If the motion parameters is transmitted on pixel bases, it might take too many information bits.
Block-base motion information is more reasonable!!
In current frame, the image is first divided into several target blocks!
Current Frame
In previously-coded frame, we need to find the best-matched block for each target block!
Encoded Previously-coded Frame

7. Block-Motion Models (1)
1. Bilinear Transformation
Bilinear
x’1 = a1x1 + a2x2 + a3x1x2 + a4
x’2 = a5x1 + a6x2 + a7x1x3 + a8
Eight Motion Parameters: a1, a2, a3, a4, a5, a6, a7, a8

8. Block-Motion Models (2)
2. Prospective Transformation
Prospective
a1x1 + a2x2 + a3
a7x1 + a8xa + 1
a4x1 + a5x2 + a6
a7x1 + a8xa + 1
x’1 =
x’2 =
Eight Motion Parameters: a1, a2, a3, a4, a5, a6, a7, a8

9. Block-Motion Models (3)
3. Affine Transformation
Rotation
&
Shift
x’1 = a1x1 + a2x2 + d1
x’2 = a3x1 + a4x2 + d2
Six Motion Parameters: a1, a2, a3, a4, d1, d2

10. Block-Motion Models (4)
4. Pixel Coordinate Transformation
Shift
x’1 = x1 + d1
x’2 = x2 + d2
Two Motion Parameters: d1, d2
The simplest block motion model, which is used for block-based motion compensation mostly!

11. Concept of Motion Estimation
Current block
Motion
Estimation
Motion Vectors
Previous block
(encoded)
Residual block
(px,py)
Current frame
(px,py)
(cx,cy)
Motion Vector
(Dx, Dy)
=(cx-px, cy-py)
The motion estimator with the input of the current frame and the previous encoded frame outputs the motion vectors and residual frame.
To search the best matched block, the motion estimation requires a search criterion and a search strategy.
Previous frame (encoded)
Motion Estimation needs a search criterion and a search strategy!

12. Block-Matching Algorithms
Definition of Best Match:
t-1
Initial Point M2 N1 M2 N2 N1 t
Current frame N2 M1 M1
Previously-encoded frame
Within a pre-defined search range and with a search criterion, we need to find the best match block!

13. Block Matching Criteria (1, 2)
1. Minimum Means Square Error(MSE)
MSE = 1
N1N2
(n1,n2)B
[ s(n1, n2, t) - s(n1+d1, n2+d2, t-1) ]2
[d1*, d2*] = Arg Minimum MSE
[d1 d2]
（Search one block by one block）
2. Minimum Mean Absolute Difference (MAD)
MAD = 1
N1N2
(n1,n2)B
 [ s(n1, n2, t) - s(n1+d1, n2+d2, t-1) ]
[d1*, d2*] = Arg Min MAD(d1,d2)
[d1 d2]
Mostly use: Sum of Absolute Difference (SAD) fixed block size

14. Block Matching Criteria (3, 4)
3. Maximum Matching Count (MMC)
1, if | s(n1,n2,t) - s(n1+d1,n2+d2,t-1) |≦C
T(n1,n2,d1,d2) =
0, others
MMC (d1,d2) =
(n1,n2)
 T(n1,n2,d1,d2)
[d1*, d2*] = Arg Max MMC (d1, d2)
[d1 d2]
4. Generalized Maximum Matching Count (MMC)
T(n1,n2,d1,d2) =

15. Block-Matching Search
Search Range :
-M1≦d1 ≦M1, -M2≦d2 ≦M2
-M1 M2
-M2 M1
(2M1 + 1)
(2M2 + 1)
Search Points:
(2M1 + 1) ╳ (2M2 + 1)
For MSE → (N1N2 Multiplications) × (2M1 + 1) × (2M2 + 1)
For MAD → (2 × Additions) × (2M1 + 1) × (2M2 + 1)
For MMC → (1 × Addition) × (2M1 + 1) × (2M2 + 1)
For GMC → (1 × Addition) × (2M1 + 1) × (2M2 + 1)

16. Block Matching Search Algorithms
Block Matching Algorithms
With Selected Block Matching Criteria
-- MSE -- MAD -- MMC
Block Match Search procedures
Full-search Method (Exhaustive search)
1. Full Search (FS)
---(2M1+1)*(2M2+1)
Example: M1=7﹐M2=7
(2M1+1) x (2M2+1) = 225 (Search Points)

17. Block Matching Search Algorithms
1. Full Search Method (FSM)
2. 1-D Row Column Search Method
3. Three Step Search (TSS) Method
4. Four Step Search (4SS) Method
5. Cross Search (CS) Method
6. Diamond Search Method
Novel TSS Method
Hexagonal Search Method
Other improved Algorithms

18. 2. Row-Column Search Algorithm15 -7 +7 15

19. 3. Three Step Search (TSS) Method
Step 1:”O”﹐9 search points  find min point (1st MSB)
(x, y) = (a 0 0, b 0 0)
a and b could be +1, 0, or -1
Step 2:””﹐8 search points  find min point (2nd MSB)
(x, y) = (-1 c 0, +1 d 0)
c and d could be +1, 0, or -1
Step 3: ”□” 8 points  find min point
TOTAL = ( ) = 25
The TSS method is called as the logarithmic search method!!

20. Third-step Search Points
3. Three Step Search (TSS) Method
First-step
Search
Points
Second-step
Third-step Search Points

21. Search Points of Three Step Search -7 -6 -5 -4 -3 -2 -1 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 1 2 3 4 5 6 7 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25

22. 4. Four Step Search (4SS)
Step 1. Search 9 points (Start from 5x5 : same as the TSS)
1. Corner:
2. Center:
3. Side:
Go to Step 2

23. 4. Four Step Search (4SS) Step 2/3 If Corner: (+5) If Side: (+3)
If Center: (8-point step)
Go to Step 3 if Not Center
Step 4 if Center

24. 4. Four Step Search (4SS)
Step 4. Search window reduced to 3x3 (same as Center case)
 1. Minimun search points = = 17
Step 1(Center) + Step 4 (Center)
 2. Maximun search points = = 27
Step 1(Center) + Step 2 (Corner) +
Step 3 (Corner) + Step 4 (Center)

25. Example for 4SS Step.1 =>Step.2 Side => Step.3 Corner -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7
Step.1 =>Step.2
Side
=> Step.3
Corner
=> Step.4
Center
Total Search
=25
Points

26. Worst Cases for 4SS Step.1 =>Step.2 Corner => Step.3 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7
Step.1 =>Step.2
Corner
=> Step.3
=> Step.4
Center
Total Search
=27 Points

27. Search Points of Four Step Search -7 -6 -5 -4 -3 -2 -1 27 26 27 27 27 27 27 25 23 25 27 27 25 22 20 22 25 26 23 20 17 20 23 26 1 2 3 4 5 6 7 25 22 20 22 25 27 27 27 25 23 25 27 27 17 27 27 26 27

28. 5. Cross Search Method The search pattern is cross shape:
Step 1:”+”search (5 points)
Step 2: (a) center (+8 points) stop
(b) side (+3 points)
Step N: center (+8 points) stop
Search Shapes (Change 9 points to 5 Points)

29. 5. Cross Search Method  1. Minimun search points = 5 + 8 = 132
 1. Minimun search points = = 13
Step 1(Center)
 2. Maximun search points = ???

30. N=4 for search range -7 to +7
5. Cross Search Example -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7
Step.1 => Step.2
Side
=> Step.3
=> Step.4
Center
Total Search
=19 Points
Normal Worse Case
N=4 for search range -7 to +7

31. Search Points of Cross Search -7 -6 -5 -4 -3 -2 -1 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 16 19 16 19 19 16 16 16 16 16 16 19 16 13 16 19 19 19 19 13 13 13 19 19 19 1 2 3 4 5 6 7 19 16 13 16 19 19 16 16 16 16 16 16 16 19 19 16 19 16 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19

32. 6. Diamond Search Step 1: 9 points ==> Three Cases 1. Center
2. Side
3. Corner

33. 6. Diamond Search
Step. 2/3
(1) Center
(2) Side : (3) Corner :

34. 6. Diamond Search
Final step: with shirked diamond (same as Center case)
 1. Minimun search points = = 13
Step 1(Center)
 2. Maximun search points = ? (33)

35. 6. Diamond Search Example -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7
Step.1 =>Step.2
Side
=> Step.3
=> Step.4
=> Step.5
Total Search
=29
Points

36. Search Points of Diamond Search -7 -6 -5 -4 -3 -2 -1 25 24 23 24 25 25 25 22 24 21 23 21 24 22 25 25 22 22 19 21 21 19 22 22 24 19 19 18 19 19 24 21 21 16 16 21 21 23 23 18 13 18 18 23 23 1 2 3 4 5 6 7 21 21 16 16 21 21 24 19 19 18 19 19 24 22 22 19 21 21 19 22 22 25 25 25 25 22 24 21 23 21 24 22 25 24 23 24 25

37. 7. Novel TSS Algorithm Step 1. TSS points (9 points)
+ around center points (8 points)
1. Center_center
2. Center_side
3. Center_corner
4. Outside
Center_center
Center_side
Center_corner
Outside point

38. 7. Novel TSS Algorithm Step. 2
(1) Center ==> (0,0) Stop! (17 points) minimum search
(2) Center-side  Perform 3 points search
(3) Center-corner  Perform 5 points search Stop!
(4) Outside point Perform regular TSS algorithm
Center_corner
Center-side
25+8=33(worst case)

39. Search Points for Novel 3-Step Search -7 -6 -5 -4 -3 -2 -1 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 22 22 20 22 22 33 33 33 33 22 22 20 22 22 33 33 33 33 33 20 20 17 20 20 33 33 33 1 2 3 4 5 6 7 33 33 22 22 20 22 16 33 33 33 33 22 22 20 16 22 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33

40. Search Points Comparison
Methods min max average speed-up FS
TSS
4SS
NTSS
Diamond
Method Ave criterion Ave distance (FS) Optimality
FS %
TSS %
4SS %
NTSS %
Diamond %

41. Hexagon-Based Search Algorithm
Step 1: The large hexagon with seven checking points is centered at (0,0), the center of a predefined search window in the motion field.
If the MBD point is found to be at the center of the hexagon,
go to Step3 (Ending);
otherwise,
go to Step 2 (Searching).
Step 2: With the MBD point in the previous search step as the center, a new large hexagon is formed. Three new candidate points are checked, and the MBD point is again identified.
If the MBD point is at the center point of the new hexagon,
otherwise, repeat this step continuously.
Step 3: Switch the search pattern from the large to the small size of the hexagon. The four points covered by the small hexagon are evaluated to compare with the current MBD point. The new MBD point is the final solution of the motion vector.

42. Hexagon Search Path
First step
Second step
Third step
Final step

43. Number of Search Points
Diamond Search:
Minimum search points =9+4=13
Number of search points NDS= 9+Mxn’+4, M=5 or n’: number of steps
Hexagon Search :
Minimum search points =7+4=11
Number of search points
NHEXBS=7+3xn+4 n: number of steps

44. One More Step Hexagon Search
Minimum Distortion

45. Search Points of HEXBS Method -7 -6 -5 -4 -3 -2 -1 20 17 17 17 20 20 20 17 17 17 17 17 17 17 20 20 20 17 17 17 14 17 17 17 20 17 17 14 14 14 14 14 17 17 17 17 14 14 11 14 14 17 17 17 17 14 14 11 11 11 14 14 17 17 1 2 3 4 5 6 7 17 17 14 14 11 14 14 17 17 17 17 14 14 14 14 14 17 17 20 17 17 17 14 17 17 17 20 20 20 20 20 17 17 17 17 17 17 17 20 17 17 17 20

46. Comparisons of HEXBS with DS
Block Size:16x16, Search Window:+/-7

47. Enhanced Hexagon Search
Enhanced Hexagon Search: Reduced inner search points by looking for the second and the third smallest points!
Compared with the conventional inner search in the original/ predictive HEXBS algorithm
The enhanced HEXBS will save two points for four sides and one point for another two sides. by uniformly averaging, 2x(4/6) + 1x(2/6) ~ 1.67 search points will be saved if we assume each side wins with equal probability.

48. New Search Mechanism: Prediction
Motion Vector Prediction:
a. Due to the spatial correlation, the motion vector of the current block is close to those in nearby blocks.
Previous coded blocks
Predictor Example 1:
vi -1,j-2
vi -1,j-1
vi -1,j
vi -1,j +1
vi,j-2
vi,j-1
Predictor Example 2:
vi,j of current block
Usage of Predictor:
Initial search point
DPCM coding of motion vector

49. Temporal Search Prediction
Motion Vector Prediction:
b. Due to the temporal correlation, the motion vector of the current block is close to those in nearby blocks in the previous frame.
Current Frame
Previous Frame
(all are coded)
Uncoded blocks
of current block
Predictor Example:

50. Early Stop Mechanism Early Stop Mechanism: Application:
During computation of motion estimation criterion (SAD), we don’t need to compute all Sub-SAD if the current accumulated SAD has larger than the minimum criterion
Once a certain SAD is reached, the quantization will removed all residual errors, the further search could be avoided.
Application:
Spiral full search
starting from
prediction
Prediction -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7
Starting from most possible points, you can find minimum SAD. So, the early stop can be applied.

51. Computation Reduction Method
DCT and Quantization:
DCT：
Quantization： Divided by 2Q for DCT coefficients
Quantized Results: Zero DCT coefficients after quantization if
where the dead zone parameter is DZ = Q/2
Now, let us to look the details of DCT and quantization processing.
After the DCT, we get the DCT coefficient F(u,v). However, if the DCT value is less than the quantization step, 2Q.

52. Computation Reduction for DCT
“Head-and-Shoulder” Sequences ：
Often used in video phone and video conference
Static background and small motion of body
High data correlation and low prediction residues
Low rate and large quantization parameter
Many all-zero blocks and zero motion macroblocks
Computation Reduction for DCT and Quantization
All-zero Block Condition
All-zero Macroblock Condition
Speed Up SAD Computation
Computation Reduction Algorithm
Just reverse of IDCT processor, if we can predict that the block after the DCT and quantization will be zero. We call this block as the all-zero block.
Professor Ming-Ting Sun found that once we calculate the SAD in the motion estimation in statistical ways. We then can predict the all zero block.
For some small SAD, actually, we don’t need to perform the DCT, IDCT, quantization, and inverse quantization.

53. All-Zero Block Condition
All-zero DCT Block Condition:
After quantization, if the magnitude of DCT coefficient F(u, v) is less than the quantization step, it will be quantized to zeros:
with dead zone DZ = Q/2 for
For DC term, , the zero condition is
Zero DC Condition:
All-Zero DCT Block Condition:
If we can prove that all F(u,v) are less than 2.5Q, then, the block is an all-zero DCT block.
For computation of the DC term of DCT, this equation shows that the DC value is less than one eighth of SAD. That means that once
the SAD is less then 20Q
The whole block will be detected as the all zero block, which will not need further computation of DCT, IDCT, and quantization, and inverse quantization.

54. Correct Rate and Detected Rate
All-zero DCT Block Condition:
TH=18Q
TH=20Q
TH=22Q
B = 9600
99.84 %
(68.12 %)
99.79 %
(72.44 %)
99.73 %
(76.09 %)
B= 12800
99.77 %
(60.68 %)
(65.93 %)
99.64 %
(70.28 %)
B= 19200
(50.56 %)
99.59 %
(56.91 %)
99.41 %
(62.77 %)
B= 28800
99.67 %
(42.46 %)
99.55 %
(49.11 %)
99.35 %
(54.91 %)
B= 33600
99.63 %
(41.19 %)
99.50 %
(47.84 %)
99.34 %
(53.62 %)
Correct Rate
(Detected Rate) B：
bit/sec
Akiyo
QCIF
By experimental simulations, the correct rate and detected rate by threshold of 18Q, 20Q, and 22Q are shown in this table.
For lower bit rate, we will use higher quantization, the detection rate will a higher. However, for larger bit rate, the lower quantization will make the detected rate smaller.
If the sum of absolute difference (SAD) is less than the quantization-dependent threshold, 20Q, we can correctly find the all-zero DCT blocks.

55. All-Zero Macroblock Condition for ME16
SAD
(MB) 8
SADB1
SADB4
SADB3
SADB2
All-Zero Macroblock Condition:
During the motion estimation, we can compute four SAD for each marcoblock. Once the SADs of four blocks are less than 20Q, we could treat the whole marcoblock as the all-zero marcoblock.
The all-zero marcoblock detection with four comparison operation does not increase any other computation.
if ( SADB1 < 20Q && SADB2 < 20Q &&
SADB3 < 20Q && SADB4 < 20Q ) {
All-zero Macroblock }

56. Computation Reduction ME Algorithm
Step 1：Compute SAD of initial search point (x0,y0)
If it satisfies all-zero MB condition, then
stop search and set MV = (x0, y0)
Else
go to Step 2
Step 2：
Continue search using any search algorithm A
and check each search point:
stop search and set MV = (x, y)
search until end of Algorithm A。
To link with any motion estimation algorithm, we propose the computation reduction algorithm for the motion estimation.
Basically, the proposed algorithm is completely the same as any other ME search algorithm.
Once the motion search reaches the all-zero marcoblock condition, we will stop the search.
With a good initial search points, we can dramatically reduce the search computation.

57. Computation Reduction (9600 bps)
PPMB
Irene
Miss_am
Akiyo
Claire
Carphone FS
787.88
787.88
787.88
787.88
787.88
FSZ
282.72
196.82
355.82
255.81
448.35
TMN8
17.07
18.25
13.91
18.78
21.11
TMN8Z
7.89
5.10
6.05
5.05
13.36
TSS
28.91
28.91
28.89
28.90
29.16
TSSZ
11.25
9.33
13.24
10.15
18.77
DS1
7.01
6.10
5.01
5.29
9.27
DS1Z
4.37
3.00
2.80
2.63
7.29
DS2
13.98
13.18
12.14
12.50
16.42
This table shows that all search algorithms with all zero-marcoblock detection reduces their search points about one half for all sequences.
DS2Z
6.72
4.79
5.97
4.83
10.88
BBGDS
14.93
13.92
12.55
12.92
17.86
BBGDSZ
7.41
5.26
6.03
4.99
12.31
The number of search Points Per MarcoBlocks (PPMB)

58. Performance Degradation (9600 bps)
PSNR(dB)
Irene
Miss_am
Akiyo
Claire
Car phone FS
29.13
35.50
33.33
34.24
27.88
FSZ
29.05
35.06
33.39
34.23
27.73
TMN8
29.18
35.56
33.32
34.22
27.78
TMN8Z
29.06
35.25
33.27
27.68
TSS
29.07
35.09
33.31
34.16
27.75
TSSZ
34.81
34.15
27.65
DS1
29.15
35.44
27.77
DS1Z
29.09
34.76
27.67
DS2
29.17
35.60
34.28
27.80
DS2Z
34.72
33.28
34.17
27.66
BBGDS
29.12
27.81
BBGDSZ
29.10
34.83
34.18
27.70
As to the performance degradation, this table demonstrates that the proposed algorithm works well for all search algorithms, specially for the low rate video.

59. Reduction for DS1 Algorithm
Sequences
Irene
Akiyo
Claire
Miss_am
Performance
PPMB
PSNR
DS1
7.01
31.95
5.01
38.07
5.29
38.96
6.10
39.51
DS1Z
5.22
31.92
3.97
3.51
4.43
39.49
Fixed
Threshold
Method
F600
5.60
31.93
2.30
2.39
38.94
3.38
39.44
F800
4.85
1.97
38.04
2.11
38.93
3.00
39.39
F1000
4.49
1.75
1.98
38.89
2.68
39.26
F1200
4.32
31.91
1.59
1.87
2.44
39.18
Referenced
R200
3.87
1.55
1.95
3.05
39.37
R400
3.61
31.89
1.43
38.09
1.76
2.72
39.29
R600
3.41
31.86
1.36
38.05
1.65
38.83
2.29
39.03
R800
3.29
31.84
1.32
38.01
1.56
38.80
2.09
38.90
Mixed
R200，F800
4.07
1.54
38.06
1.88
2.81
39.36
R200，F1000
3.80
1.52
1.85
38.92
2.67
39.31
R400，F800
3.58
1.40
1.73
2.54
39.22
R400，F1000
31.88
1.42
1.71
38.87
39.17
Original
All-Zero Block
By using the mixed threshold method, we can reduce diamond search method to about 1.4 – 4.07 search points without degradation of PSNR performances.
DS1= Diamond Search with Step Size 1, B= bps

60. Computation Reduction
All-zero macroblock detection can reduce the search point of any ME algorithm, it also can save all the computation of DCT, Q, Q-1, and IDCT.
Rate Control
Ordered
Source
Pictures
Residual
Motion
Estimator
DCT
Lossless
Coder Q
Buffer _ +
Q-1
Prediction
Coded Video
Bit Steam
MC Mode
Decision + +
Inverse
DCT
The all-zero marcoblock detection method can help to reduce the computation of motion estimation. It also can reduce all the computation required by the DCT, IDCT and quantization and inverse quantization. MV MC
Modes
Picture
Predictor
& Store
Decoded
Picture