1. MultiHypothesis Pictures For H.26L Markus Flierl Telecommunications Laboratory University of Erlangen-Nuremberg Erlangen, Germany mflierl@stanford.edu Thomas Wiegand Image Processing Department Heinrich Hertz Institute Berlin, Germany wiegand@hhi.de Bernd Girod Information Systems Laboratory Stanford University Stanford, CA girod@ee.stanford.edu To be published in Proc. ICIP, Thessaloniki, Greece, Oct. 2001

2. Outline Motion Estimation in H.26L Introdution to MultiHypothesis Pictures Coding of MultiHypothesis Pictures Experimental Results

3. Motion Estimation in H.26L Low Complexity –MC_Range for 16x16 Macro Blocks –Range = ½ MC_Range for Other Sub Blocks –Range = ½ Range for Search in the older ref. pictures –MVs must be inside of the ref pictures –Choose the results which SA(T)D is minimal.

4. Motion Estimation in H.26L High Complexity –One MC_Range for all Inter Mode and Ref. Pictures –Sub Block Matching starts at the result of 16x16 Macro Block Matching results –Not Forced to contain the MV (0,0) –MVs could be out of the boundaries of the Ref. Picture –Choose the results which much matching the RD constrain.

5. MultiHypothesis Pictures

6. An extension of P pictures. Each macroblock can be compensated by a linear combi-nation of two motion-compensated macroblocks.

7. Modified in TML 6 Add a individual ref. parameter for each block. It ’ s efficient for H.263, but not for H.26L –In H.263, 16x16 and 8x8

9. MacroHypothesis Types

10. MacroHypotheses Coding In the multihypothesis mode –Two macrohypotheses –For Each macrohypothesis Picture ref. Parameter A Macrohhypothesis type The corresponding motion vector for each sub-block

11. Encoder A Lagrangian cost function is used for coding mode decisions. –D: Multihypothesis prediction error two picture ref. Parameterstwo macrohypotheses typesthe associated motion vectors –R: Bit rate for two picture ref. Parameters, two macrohypotheses types, and the associated motion vectors. For multihypothesis mode –Rate-Constrained multihypothesis motion estimation. –Performed by the macrohypothesis selection algo.

12. Iterative algo. for conditional rate- contrained motion estimation Initial macrohypothesis –The best macroblock type for long-term memory motion-compensated prediction. Steps 1. One macrohypothesis is fixed and conditional rate-constrained motion estimation is applied to the complementary macrohypothesis such that the multihypothesis costs are minimized. 2. The complementary macrohypothesis is fixed and the first macrohypothesis is optimized. 3. To repeat step 1, 2 until convergence.

13. Rate-Constrained Multi-Hypothesis Motion-Compensated Prediction for Video Coding M. Flierl, T. Wiegand, and B. Girod, “ Rate-Constrained Multi-Hypothesis Motion- Compensated Prediction for Video Coding, ” in Proceedings of the IEEE International Conference on Image Processing, Vancouver, Canada, Sept. 2000, vol. III, pp. 150 – 153.

14. Conditional rate-constrained motion estimation conditional optimal picture reference parameter macrohypothesis typeassociated motion vectorsFor the current macrohypothesis, conditional rate-constrained motion estimation determines the conditional optimal picture reference parameter, macrohypothesis type, and associated motion vectors. For the conditional motion vectors, an integer-pel accurate estimate is refined to half-pel and quarter-pel accuracy.

16. Additional MBtype A joint optimization of multihypothesis motion estimation and prediction error coding is far too demanding. But multihypothesis motion estimation independent of prediction error encoding is an efficient and practical solution.

18. Experimental Results Mobile & Calendar Tempete30 fpsOur coder is based on the H.26L test model TML-6. For our experiments, the CIF sequences Mobile & Calendar and Tempete are coded at 30 fps. We investigate the rate-distortion performance of multihypothesis pictures with respect to H.26L P pictures for various long-term memory buffer sizes.