MOTION ESTIMATION AND VIDEO COMPRESSION By, Jarjit Tandel Waseem Khatri Sidhesh Khapare

Outline Introduction Motion Estimation Motion Compensation Algorithm Block Estimation Algorithm Compression Results Conclusion References

Introduction Motivation Background Understand Motion Estimation Reconstruction of Video Using Motion Compensation Background A Video sequence consist of series of frames.

What is Motion Estimation Predict current frame from previous frame Determine the displacement of an object in the video sequence Types of Motion Estimation: Horn and Schunck Three Step Search Block Motion Method Hierarchical Block Motion

What is Motion Compensation Reconstruction of video file Reference frame is used to predict current frame using motion vectors.

Proposed Algorithm Input Color Video Extract frames ‘k’ and ‘k+1’ 3-step motion estimation Obtain motion vectors Forward motion estimation Predicted video frame - Predicted video frame Original frame ‘k+1’ + + + Reconstructed frame ‘k+1’ Quantized error Prediction error

Proposed Algorithm Input Color Video Extract frames ‘k’ and ‘k+1’ 3-step motion estimation Obtain motion vectors Forward motion estimation Predicted video frame - Predicted video frame Original frame ‘k+1’ + + + Reconstructed frame ‘k+1’ Quantized error Prediction error

Three Step Search Method Input RGB Video Extract Frames Divide each Frame into Blocks of size 16X16 Divide each block into 9 equal parts Calculate MSE Select block With lowest MSE/MAD Divide the selected Block into 9 equal parts Video Frame Draw line connecting Center of frame to this point Select block With lowest MSE/MAD Calculate MSE Divide the selected Block into 9 equal parts Select block With lowest MSE/MAD Calculate MSE 16 X 16 Block

Block Representation Input Color Video Extract frames ‘k’ and ‘k+1’ 3-step motion estimation Obtain motion vectors Forward motion estimation Predicted video frame - Predicted video frame Original frame ‘k+1’ + + + Reconstructed frame ‘k+1’ Quantized error Prediction error

Predicting Next Frame Frames ‘k’ and ‘k+1’ Motion Vectors Predicted Frame ‘k+1’

Block Representation Input Color Video Extract frames ‘k’ and ‘k+1’ 3-step motion estimation Obtain motion vectors Forward motion estimation Predicted video frame - Predicted video frame Original frame ‘k+1’ + + + Reconstructed frame ‘k+1’ Quantized error Prediction error

Prediction Error Calculation Frame 60 Frame 61 Prediction error + - Predicted Frame

Results Motion Vectors Predicted frame Color video 3-step motion estimation Forward motion estimation Motion Vectors Predicted frame Color video Extracted frames ‘k’ and ‘k+1’ - Predicted frame + Frame ‘k+1’ + + Quantized error Reconstructed video frame Prediction error

Conclusion Advantages: Simplicity: Simple geometric transformation of pixel co-ordinate. Easy to implement in hardware Limitations: Fails for zoom, rotational motion, and under local deformations.

References [1] H. Gharavi and M. Mills, “Block-matching motion estimation algorithms: New results,” IEEE Trans. Circ. and Syst., vol. 37, pp. 649-651, 1990. [2] V. Seferidis and M. Ghanbari, “General approach to block-matching motion estimation,” Optical Engineering, vol. 32, pp. 1464-1474, July 1993. [3] M. Bierling, “Displacement estimation by hierarchical block-matching,” Proc. Visual Comm. and Image Proc., SPIE vol. 1001, pp. 942-951, 1988. [4] B. K. P. Horn and B. G. Schunck, “Determining Optical Flow,” Artif. Intell., vol. 17, pp. 185-203, 1981. [5] S. V. Fogel, “Estimation of velocity vector fields from time varying image sequences,” CVGIP: Image Understanding, vol. 53, pp. 253-287, 1991. [6] T. S. Huang, ed., Image Sequence Analysis, Springer Verlag, 1981. [7] A. V. Oppenheim and R. W. Schafer, “Discrete - Time Signal Processing,” Prentice Hall Signal Processing Series, 1989. [8] A. M. Tekalp, “Digital Video Processing,” Prentice Hall Signal Processing Series, 1995. [9] D. E. Dudgeon, “Multidimensional Digital Signal Processing,” Prentice Hall Signal Processing Series, 1996. [10] K. Sayood, “Introduction to Data Compression,” Morgan Kaufmann Publishers, 2006.

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