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Motion Estimation for Video Coding Motion Estimation for Video Coding . Motion-Compensated Prediction . Bit Allocation . Motion Models . Motion Estimation . Efficiency of Motion Compensation Techniques T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 1 Hybrid Video Encoder Coder Control Control Data s[x, y,t] u[x, y,t] Intra-Frame DCT DCT Coder - Coefficients Decoder Intra-Frame Decoder u'[x, y,t] s'[x, y,t] 0 Motion- Compensated Intra/Inter Predictor sˆ[x, y,t] Motion Data Motion Estimator T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 2 Motion-Compensated Prediction Previous Stationary frame background Moving t object Current frame x y time t d x Displaced d y object Prediction for the luminance signal s[x,y,t] within the moving object: sˆ[x, y,t] s(x dx , y d y ,t t) T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 3 Motion-Compensated Prediction: Example Frame 1 s[x,y,t-1] (previous) Frame 2 s[x,y,t] (current) Partition of frame 2 into blocks (schematic) Size of Blocks Accuracy of Motion Vectors Referenced blocks in frame 1 Frame 2 with Difference between motion- displacement vectors compensated prediction and current frame u[x,y,t] T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 4 Motion Models . Motion in 3-D space corresponds to displacements in the image plane . Motion compensation in the image plane is conducted to provide a prediction signal for efficient video compression . Efficient motion-compensated prediction often uses side information to transmit the displacements . Displacements must be efficiently represented for video compression . Motion models relate 3-D motion to displacements assuming reasonable restrictions of the motion and objects in the 3-D world Motion Model d x x x f x (a, x, y), d y y y f y (b, x, y) x, y : location in previous image x, y : location in current image a,b : vector of motion coefficients : displacements d x ,d y T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 5 Representation of Video Signal Decoded video signal is given as s[x, y,t] sˆ[x, y,t]u[x, y,t] Motion-compensated Prediction residual signal prediction signal M 1 sˆ[x, y,t] s(x d , y d ,t t) x y u (x, y) c j j (x, y) j0 N 1 N 1 dx ai i (x, y), d y bi i (x, y) i0 i0 Transmitted residual parameters Transmitted motion parameters Ru f (c), c (c0 ,...) Rm f (a,b), a (a0 ,...), b (b0 ,...) T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 6 Rate-Constrained Motion Estimation Bit-rate Motion vector rate D Rm Prediction error rate Ru Displacement error variance dD dD . Optimum trade-off: , R Rm Ru dRm dRu . Displacement error variance can be influenced via • Block-size, quantization of motion parameters • Choice of motion model T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 7 Lagrangian Optimization in Video Coding . A number of interactions are often neglected • Temporal dependency due to DPCM loop • Spatial dependency of coding decisions • Conditional entropy coding . Rate-Constrained Motion Estimation [Sullivan, Baker 1991]: min Dm mRm Distortion after Lagrange Number of bits motion compensation parameter for motion vector . Rate-Constrained Mode Decision [Wiegand, et al. 1996]: min D R Distortion after Lagrange Number of bits reconstruction parameter for coding mode T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 8 Motion Models . Translational motion model dx a0 , d y b0 . 4-Parameter motion model: translation, zoom (isotropic Scaling), rotation in image plane d x a0a1x a2y d y b0 a2x a1y N 1 N 1 . Affine motion model: d x a0a1x a2y dx ai i (x, y), d y bi i (x, y) i0 i0 d y b0 b1x b2 y 2 2 . Parabolic motion model dx a0a1x a3y a2x a6y a5xy 2 2 dx b0 b1x b3y b2 x b6 y b5 xy T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 9 Impact of the Affine Parameters 150 100 dx a0 translation 50 y 0 -150 -100 -50 0 50 100 150 -50 -100 -150 x 150 150 100 100 50 50 dx a1x scaling y y 0 0 -150 -100 -50 0 50 100 150 -150 -100 -50 0 50 100 150 -50 -50 -100 -100 -150 -150 x x 150 100 dx a3 y sheering 50 y 0 -150 -100 -50 0 50 100 150 -50 -100 -150 x T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 10 Impact of the Parabolic Parameters 150 2 100 dx a2 x 50 y 0 -150 -100 -50 0 50 100 150 -50 -100 -150 x 150 150 100 2 100 50 dx a6 y 50 y y 0 0 -150 -100 -50 0 50 100 150 -150 -100 -50 0 50 100 150 -50 -50 -100 -100 -150 -150 x x 150 100 50 dx a5 xy y 0 -150 -100 -50 0 50 100 150 -50 -100 -150 x T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 11 Differential Motion Estimation . Assume small displacements dx,dy: u(x, y,t) s(x, y,t) sˆ(x, y,t,d x ,d y ) s(x, y,t t) s(x, y,t t) s(x, y,t) s(x, y,t t) d d x x y y Displace frame difference Horizontal and vertical gradient of image signal S . Aperture problem: several observations required . Inaccurate for displacements > 0.5 pel multigrid methods, iteration . Minimize By Bx min u 2 (x, y,t) y1 x1 T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 12 Gradient-Based Affine Refinement . Displacement vector field is represented as x a1 a2 x a3 y . Combination y b1 b2 x b3 y s s u(x, y,t) s(x, y,t) s(x, y,t t) (a a x a y) (b b x b y) x 1 2 3 y 1 2 3 a yields a system of linear equations: 1 a2 s s s s s s a3 u s s , x, y, , x, y, x x x y y y b1 b2 b3 . System can be solved using, e.g., pseudo-inverse, By Bx by minimizing 2 arg min u (x, y,t) a1 ,a2 ,a3 ,b1 ,b2 ,b3 y1 x1 T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 13 Block-matching Algorithm search range in • Subdivide current frame previous frame into blocks. Sk1 • Find one displacement vector for each block. • Within a search range, find a “best match” that minimizes an error measure. • Intelligent search strategies can reduce computation. block of current frame Sk T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 14 Block-matching Algorithm Previous Frame Current Frame Measurement window is Block of pixels is selected compared with a shifted block as a measurement window of pixels in the other image, to determine the best match T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 15 Block-matching Algorithm Previous Frame Current Frame . process repeated for another block. T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 16 Error Measures for Block-matching . Mean squared error (sum of squared errors) By Bx 2 SSD(dx ,d y ) [s(x, y,t) s (x dx , y d y ,t t)] y1 x1 . Sum of absolute differences By Bx SAD(dx ,d y ) | s(x, y,t) s (x dx , y d y ,t t) | y1 x1 . Approximately same performance SAD less complex for some architectures T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 17 Block-matching: Search Strategies Full search . All possible displacements within the search range are compared. dx dy . Computationally expensive . Highly regular, parallelizable T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 18 Speedup of Block-matching Complexity of block-matching: evaluation of complex error measure for many candidates Reduce complexity Reduce number • Approximations • Cover likely search areas • Early terminations • Unequal steps between • Exclude candidates searched candidates Combine both approaches: Choose starting point and search order that maximizes likelihood for efficient approximations, early terminations and excluding of candidates T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 19 Approximations • Stop search, if match is “good enough” (SSD, SAD < threshold or J=D+R is small enough) • Practical method in video conferencing for static background: test zero-vector first and stop search if match is good enough static background T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 20 Early Termination . Partial distortion measure: DK (dx ,d y ), K N...By K Bx p DK (dx ,d y ) [s(x, y,t) s (x dx , y d y ,t t)] y1 x1 . Previously computed minimum: J min . Early termination without loss: Stop, if DK (dx ,d y ) m R(dx ,d y ) Jmin . Early termination with possible loss, but higher speedup: Stop, if DK (dx ,d y ) m R(dx ,d y ) (K) Jmin 1/ 2 e.g., (K) (K / By ) T. Wiegand / B. Girod: EE398A Image and Video Compression Motion estimation no. 21 Block Comparison Speed-Ups . Triangle inequality for samples in block B (here SAD) | Sk Sk1 | | Sk | | Sk1 | B B . Strategy: 1. Compute partial sums for blocks in current and previous frame 2. Compare blocks based on partial sums 3.
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