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Interframe Coding

Interframe Coding. Heejune AHN Embedded Communications Laboratory Seoul National Univ. of Technology Fall 200 8 Last updated 2008. 10. 12. Agenda . Interframe Coding Concept Block Matching Algorithm Fast Block Matching Algorithms Block Matching Algorithm Variations

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Interframe Coding

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  1. Interframe Coding Heejune AHN Embedded Communications Laboratory Seoul National Univ. of Technology Fall 2008 Last updated 2008. 10. 12

  2. Agenda • Interframe Coding Concept • Block Matching Algorithm • Fast Block Matching Algorithms • Block Matching Algorithm Variations • Enhanced Motion Models • Implementation Cases

  3. 1. Interframe Coding • Motivation • Video has High Temporal Correlation between frames. • Var[ X(t+1) – X(t) ] << Var[ X(t+1) ] Two successive video frames DFD (displaced Frame Difference)

  4. Motion Estimation and compensation • Motion estimation • Find the best parameters of current frame from reference frames • Motion compensation • Subtracts (Add) the predicted values from current frame (to DFD frame) Current frame MC Encode Residual MC Recon. Texture Info Reference frames ME Motion parameters Reference frames Recon.

  5. Performance Criteria • Coding performance • Residual signal has low energy (variance measure) • Complexity • Computational and implementation complexity • Storage and Delay • Number of required frames • Side Information • Size and complexity of motion parameters • Error resilience • When data is partially lost. • Some factors are trade off • Coding perf. against complexity, storage, side info, error resilience.

  6. 2D Motion previous frame stationary background current frame x time t y moving object „Displacement vector“ shifted object Prediction for the luminance signal S(x,y,t) within the moving object:

  7. X(t+1) X(t) Real Motion MV 2. Block Matching Algorithm • BMA(Block matching algorithm) • Segment frame into same rectangular Blocks • 2-D linear motion (mvx, mvy) per each block

  8. Difference Measure • MSE • MAE and SAE • CCF (Cross Correlation Function)

  9. Full Search Algorithm • “Full Search” does Not means the whole frame, but whole position in limited Search Window • Method • Raster order or Spiral order (Figure. 6.6)

  10. Full Search Complexity • (2w+1) x (2w+1) points (for search window [-w, w]) • NxN size Block computation int SAE(uchar *f, uchar *g, int mvx, int mvy){ for ( x=0; x< N; x++){ for ( y=0; y< N; y++){ sae += ABS(*(f + (y+mvy)*width +(x+mvx), *(g + y*width+x)); • } • mvx_min = mvy_min = 0; • min = SAE(f, g, 0, 0); • for(mvy=-w, mvy<=w, mvy++) • for(mvx=-w, mvx<=w, mvx++){ • sae = SAE(pre, cur, mv, mv) • if(min >sae) • mvx_min = mvx, mvy_min = mvy, min = sae; • }

  11. -w +w 0 3. Fast BMAs • Complexity Reduction Approaches • Reduce test points • Monotonic variation assumption • The closer to the optimal point, the smaller difference • Change the test-point order (more like first) • Binary Search than Linear Search • Benefit from Early Stop of block difference calculation • Reduce the computation at one point • Sub-sampled value • Note • Trade-off!

  12. TSS (3-Step Search) • Step 0: Search center (0,0), n = w • Step 1: n = floor[ n / 2 ] • Step 2: Search 8 points and find the min values • Step 3: if n == 1 stop, o.w. Go to Step 1 • Properties • Logarithmic/Binary search (only 3 step when p = 8) • Search decreasing distance • w/2 => w/4 => w/8 . . . . until 1 • Complexity : O(log2w)

  13. 2D Logarithmic Search • Step 0: Search center (0,0) • Step 1: Search 4 points with s step size • Step 2: find min, if center S = S/2, ow. move center to the min locaiton • Step 3: if S = 1, go to step 4, else go to Step 1 • Step 4: search the 8 neighbors, and decide min. • Properties • Similar to TSS, but more accurate • Complexity ~ O(log2w) but not fixed loop count 3 4 5 5 5 1 4 2 5 5 3 5 5 5 1 1 2 2 2 1

  14. Examples • TSS (Tree Step Search) • Logarithmic Search • Cross Search • One-at-a-time Search • Nearest Neighbors Search • From Other Source. • TSS (Three Step search) • TDL (Two Dim. Logarithmic) • CDS (Conjugate Direction Search) • CSA (Cross Search Algorithm) • OSA (Orthogonal Search Algorithm)

  15. Fast BMA Performance • Complexity

  16. Estimation Performance

  17. Issues in Fast MC Algorithm • Local Minimum Error • Fast MC calculates only few of positions • Many cases are not “monotonic” curves, single hill. • Possibly can conclude with local minimum. • See Figure 6.15 1 1 1 2 3 2 3

  18. Hierarchical MC • Reduced image • Sub-sampled, filtered • N levels with half resolution • Search top (N) level fully • reduced search window range (w/2N-1) • Search lower N-1 level • only 9(8?) neighbor positions only

  19. Benefits of hierarchical search • Escape Local minimum • Complexity Reduction • e.g) Window = 16 full search (2 × 32 + 1)2 = 4225 operations HBMA with N =4, (2 × 4 + 1)^2 + 3 × 9 = 108 operations Sub-sampled signal Original signal

  20. 4. Variations of BMA: Multi-frame MC • Multiple Frame MC • “Forward pred” starts from H.261 • “backward, bidirectional” starts from MPEG-1 • “multiple reference (each MB takes its own ref picture) starts from H.264 forward forward backward bidirectional: average

  21. 4. Variations of BMA: Multi-frame MC • Multiple Frame distance • Search Range = frame difference x window • Since displacement = velocity x time • eg) w = 8, 64 points (1 frame diff), 256 points (2 frame diff) • Practice • search only [-w, w] of (mvx1, mvy1) for (mvx2, mvy2) -2w mvx2,mvy2 -w mvx1,mvy1 +w +2w t -1 t -2 t

  22. MV at Boundary • Restriction on MV range • Should inside of reference pictures • In H.261/MPEG-1, MPEG-2, MPEG-4 • Unrestricted MV • Extrapolates (extends with same boundary pixel value) • In H263 Annex D,H.264 -w -w +w +w t -1 t Extrapolated t -1

  23. Sub-pixel Motion Estimation • Note • Object cannot happens to move integer pixels • We have only integer pixel samples • Sub-pixel estimation • Get the fractional pel values in reference frame • Normally using linear interpolation • Half-pel/quarter-pel

  24. 5. Enhanced Motion Models • More Motion Estimation Model • Rigid 2D Translation (BMA) • + Transformation • Global Motion • + Illumination variation • + zoom-in/out • Object Model • + overlapping of objects • + 3D Rotation • + Non rigid objects (deformation) • Some are from computer vision area • But at present most tools are too complex for application to video coding area • Some are included in MPEG-4 Part 2’s Object Oriented Coding

  25. Examples • Region based motion compensation • How to get/describe shape and motion • Global motion (picture warping) • Called Camera motion • Mesh-based Deformation

  26. 6. Implementation • Video Encoder and Decoder Complexity Profiling

  27. SW Optimization • Algorithm level optimization : independent of CPU • Data structure design (most modern CPU, RISC) • Memory Cache optimization • Current blocks into cache • Loop unrolling (See Fig. 6.21) • Reduce the pointer operation and jump prediction (pipelining) • CPU-specifics Optimization • SIMD (Single Instruction with Multiple Data) • Packed Instruction (See Fig. 6.22) • TI DSP, Intel MMX etc • MIMD (MuParalell Processing Core) • VLIW (Very Long Instruction Word) of TI DSP • GPU • DMA utilization • Coprocessor Utilization • DCT, ME, Post/Pre Processing

  28. SAE SAE SAE SAE Comparator HW Optimization • Criteria • Performance, cycle count, gate-count, data flow • Example #1: Full Search • Parallelization • M function block, then M Speed up Search Window Memory (DRAM/SRAM) Current MB (SRAM)

  29. STEP1 (+/-4 Step 2 (+/-2) Step3 (+/-1) Step 4 (+/-1/2) • Example #2: Fast Search • TSS and Hierachical search (has fixed clock property) • Pipelining blocks for speed up Search Window Memory (DRAM/SRAM) Current MB (SRAM) t =1 block 1 t=2 block 2 block 1 t= 3 block 3 block 2 block 1 t=4 block4 block 3 block 2 block 1

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