CAMED: Complexity Adaptive Motion Estimation & Mode Decision for H.264 Video

Transcription

1 ICASSP 6 CAMED: Complexity Adaptive Motion Estimation & Mode Decision for H.264 Video Yong Wang Prof. Shih-Fu Chang Digital Video and Multimedia (DVMM) Lab, Columbia University

2 Outline Complexity aware H.264 coding CAMED Rate-Distortion-Complexity video coding Complexity cost function Complexity control Experiment results Conclusions & future work 2

3 H.264: Emerging Video Standard More efficient motion prediction Variable block size motion compensation (as small as 4 x 4) Quarter pixel interpolation Multiple reference frames (up to 5) Decoupling of referencing order from display order Directional spatial prediction for intra coding SKIP/DIRECT Mode Improved coding efficiency Small block-size transform (4 x 4 integer transform) Lossless inverse transform Context-based adaptive binary arithmetic coding (CABAC) Switching P (SP) and switching I (SI) pictures 3

4 Complexity Issue In H.264 Much better quality, with much higher complexity [Schäfer 3] [Lappalainen 3] 4

5 Complexity Aware H.264 Coding Important for mobile video solutions H.264: Emerging coding standard Much higher quality Much higher complexity Complexity constrained video coding ( P) ( P) ( P) min D, s.., t R R, C C T T P : QP, MV, Mode,? 5

6 Approaches for Comp. Reduction Hardware level Hardware architecture (MMX, SIMD [Chen 4]) Low power chip (Advanced voltage scaling [ARM 3]) High capacity battery [GIT] Encoder complexity Fast motion estimation (FME) [Li 5, Tourapis 3 ] Fast mode decision (FMD) [Zhu 5, Peng 5] Fast reference frame selection [Su 4] Adaptive I-B-P GOP structure [Ray 4] SAD (Sum of Absolute Difference) complexity modeling [He 4] Decoder complexity Smart inverse DCT [Ortega ] Interpolation approximation [Wang 5] Implementation optimization (buffer reusage, assembly coding) Complexity Adaptive ME & MD (CAMED) 6

7 CAMED: Philosophy Input Frames P(t) - d(t) Motion Compensation++ v v + P R (t) Transform Inverse Transform P( t 1) d T + (t) Frame Buffer+De-blocking (H.264) Quantization Inverse Quantization Pre-decoded Entropy Decoding d T (t) Entropy Encoding Entropy Decoding Video Decoder Decoded Frame Motion Estimation & Mode Decision Video Encoder v v Entropy Encoding Bit Strem Combining Encoded Bitstream 7

8 CAMED: Highlight Develop an encoding algorithm that achieves both high video quality and low decoding complexity Addressed the most significant module (Motion Compensation) in decoders, so that the complexity reduction is efficient. Nothing need to be done in decoders, and the extra cost in encoders is trivial Full standard compliant The proposed method can be a valuable complement for hardware-level and encoder-level solutions Very promising for mobile video applications. 8

9 R-D-C Optimization R-D-C Lagrange optimization V * RDC,, C = MOTION V sup{ V} ( BM, ) argmin J ( V BM, ) V sup{ V} * RDC,, C = MODE M sup{ M} M sup{ M} { RD, J } MOTION V BM γ MOTIONCMOTION V BM = arg min (, ) + (, ) ( ) M ( MBQP, ) argmin J M MBQP, ( ) γ MODE MODE ( ) { RD, J } MODE M MBQP C M MBQP = arg min, +, γ MOTION = γ MODE 9

10 Complexity Cost Function Pixel-based interpolation operation c P ( V) V is integer MV e 6 V is subpixel b,h e6 + e 2 V is subpixel a,c,d,n = 2e6 + e 2 V is subpixel e,g,p,r 7e 6 V is subpixel j 7e6 + e 2 V is subpixel i,f,k,q G a b c H d e f g h i j k m n p q r M s N Can be further simplified counting 7-tap interpolations. 1

11 Complexity Cost Function (cont.) Block-based interpolation operation Mode Integer 1 6-tap 2 6-tap 7 6-tap Skip/Direct x x x x x x x Intra 11

12 Complexity Cost Function (cont.) Complexity benchmark Real time AVC decoder in Dell Axim x5 PDA Decoding Time Per Pixel (1E-7s) Dell Axim x5 w/ 524MHz Xscale CPU, Foreman, QCIF, 15fps, baseline, 2Kbps M1 M2 M3 M4 M5 M6 M7 V1 V5 V9 V13 V2 V3 V4 V6 V8 V14 V16 V7 V1 V11 V12 V15 Different Subpixel Levels 12

13 Complexity Cost Function (cont.) Modeling based on complexity benchmark ( ) p v M b ( n c + n c c ) c +, = M 6, v 6 2, v 2 Content-independent: the complexity is mainly decided by the arithmetic calculations Can be summarized into look-up table to reduce encoding cost Reflects the actual computing complexity, including not only interpolation, but also other cost rather than interpolation such as memory access and logic flow. l 13

14 Complexity Control The procedure to adjust coding parameters so that the complexity level meets the target requirement. Similar to rate control Control parameter: Lagrange multiplier γ, γ MOTION MODE Complexity modeling ( 1 MODE ) ( γ ) ln ( γ ) C = D K + K γ MODE MODE () t ( ) KD( t) KDt () C t = exp 1 Compelxity (1K Interpolations) Mobile, 6kbps Each line is for one P frame Lagrange multiplier for block mode 14

15 Complexity Control (cont.) Model estimation Collect complexity stat using sliding window Estimate model parameters through regression Complexity C E Observed ( γ,c) Observed Point Increased frame complexity ( γ,c) Point Buffer management γ MODE Lagrange Multiplier Use complexity buffer to simulate the complexity usage status on the decoder side Estimate of available resource, adjust the parameters, and avoid issues of buffer overflow or underflow 15

16 Experiment Setup Environment Software: H.264 JM 8.2 Sequences: Akiyo, Foreman, Stefan, Mobile Resolution: QCIF/CIF Profile: Main/Baseline Bit rate: 1kbps~1Mbps Block mode: all on Direct mode: temporal Motion estimation search range: 32 pixels 16

17 R-D-C Optimization PSNR(dB) Foreman PSNR 33 H La mda=5 31 La mda=2 3 La mda=5 29 Lamda=2 Lamda= Bandwidth (kbps) Remaining Complexity(%) Foreman Complexity 2 La mda =5 La mda =2 1 La mda =5 Lamda=2 Lamda= Bandwidth (kbps) Up to 6% 6-tap interpolation can be saved while keeping the video quality almost intact (quality difference less than.2db) 17

18 Demonstration 18

19 Results: Complexity Benchmark 5. Foreman, QCIF, 15fps, baseline PSNR (db) Beat H.264 up to 1.8dB in terms of C-D performance H.264 L1. L2. L5. L.5 L1 4K Decoding Time (s) Dell Axim x5 19

20 Results: Compatibility w/ FME 2

21 Results: Complexity Control Comple xity (K inte rpolation) JM82 5K 1K 15K 2K Foreman, 1kbps GOP Index 21

22 Results: Complexity Control CIF, 3fps, 1kbps 22

23 Conclusions & Future Work Proposed CAMED for complexity-efficient H.264 coding Applied rigorous R-D-C optimization framework Defined several complexity cost functions Proposed the algorithm of complexity control Performance Validated through extensive simulation experiment Future work Theoretical analysis on the relationship between the complexity level and the control parameter (i.e., the Lagrange multiplier) Considering hardware acceleration (such as MMX, SSE) More efficient complexity modeling and complexity control 23

24 THANK YOU! Prof. Shih-Fu Chang Yong Wang