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An Inter-Frame Prediction Technique Combining Template Matching Prediction and Block Motion Compensation for High Efciency Video Coding Wen-Hsiao Peng Chun-Chi Chen Circuits and Systems for Video Technology, 2013 IEEE Transactions on

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Outline Introduction Background Bi-prediction Combining TMP and BMC Analysis LS and LMS Experiment Results Conclusion

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Introduction Inter prediction combines MVs from – TMP – BMC for Overlapped Block Motion Compensation. Prediction performance of OBMC close to that of bi-prediction. – without having to signal the template MV

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Introduction TMP generally outperforms SKIP prediction. TMP is inferior to block-based motion compensation. Another MV is required to best complement the template MV.

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Introduction A key issue in video coders with motion-compensated prediction is how to trade off effectively between – accuracy of the motion field representation – required overhead Based on HEVC version 6.0 Achieve the bitrate reduction.

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Outline Introduction Background – Template Matching Prediction – Block Motion Compensation – SKIP and Merge-SKIP – Signal Model – Prediction Error Surface Bi-prediction Combining TMP and BMC Analysis LS and LMS Experiment Results Conclusion

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Template Matching Prediction Obtains the MV at a current pixel by finding, in the reference frames, the best match for a template region composed of its surrounding reconstructed pixels.

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Block Motion Compensation The frames are partitioned in blocks of pixels and each block is predicted from a block of equal size in the reference frame.

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Comparsion True motionBMCTMP

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SKIP and Merge-SKIP SKIP – H.264/AVC Merge-SKIP – Weighted sum

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Signal Model Tao et al [19] –. Zheng et al [24] –. [19] B. Tao and M. T. Orchard, A parametric solution for optimal overlapped block motion compensation, IEEE Trans. on Image Processing, vol. 10, no. 3, pp. 341–350, Mar [24] W. Zheng, Y. Shishikui, M. Naemura, Y. Kanatsugu, and S. Itoh,Analysis of space-dependent characteristics of motion- compensated frame differences based on a statistical motion distribution model, IEEE Trans. on Image Processing, vol. 11, no. 4, pp. 377–386, Apr

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Signal Model Mean-sqaured prediction error –. Tao et al [19] –. Zheng et al [24] –. [19] B. Tao and M. T. Orchard, A parametric solution for optimal overlapped block motion compensation, IEEE Trans. on Image Processing, vol. 10, no. 3, pp. 341–350, Mar [24] W. Zheng, Y. Shishikui, M. Naemura, Y. Kanatsugu, and S. Itoh,Analysis of space-dependent characteristics of motion- compensated frame differences based on a statistical motion distribution model, IEEE Trans. on Image Processing, vol. 11, no. 4, pp. 377–386, Apr

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Signal Model Block MV, v b, and block center, s c – v b = v(s c ) –. Template MV, v t, and template center, s t – v t = v(s t ) –.

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Signal Model Taos modelZhengs model

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Prediction Error Surface

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Prediction Performance Comparsion Encoding 50 frames

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Outline Introduction Background Bi-prediction Combining TMP and BMC – Overlapped Block Motion Compensation – Least Square Solution – Least Mean-Square Solution Analysis LS and LMS Experiment Results Conclusion

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Bi-prediction Combining TMP and BMC Predictor is computed as a weighted average of two reference blocks. – Template MV, v t – Block MV, v b TMP can better compensate for the movement of the top-left area of a prediction block. BMC is thus aimed at reducing further the prediction residual in the remaining area.

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Overlapped Block Motion Compensation The weighting can be pixel adaptive. –. – ω is indicating their likelihood The problem is to determine the OBMC weights so that the resulting predictor would produce a minimal residual. –.

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Overlapped Block Motion Compensation How to minimize the prediction residual by a suitable choice of the block MV and OBMC weights. –. The approaches to solve the problem – Least Squares Approach – Least Mean-Square Approach

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Least Square Solution Rely on an iterative algorithm to solve for the optimal weights. 1.Estimating Block MVs :. 2.Adapting OBMC Weights :. Its convergence to a possibly local minimum is usually between 5 to 10 iterations.

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Least Mean-Square Solution Introduce statistical signal models. Given that every block is to be predicted using OBMC based on two MVs – defaulting to the true MV – MV sampling the motion field at some point s b – determine a set of OBMC weights

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Least Mean-Square Solution Transform the problem of minimizing ξ into that of minimizing its expected value E[ξ]. –. 1.Fixing s b determine the :. 2.Find the optimal s b that yields the global minimum :.

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Outline Introduction Background Bi-prediction Combining TMP and BMC Analysis LS and LMS Experiment Results Conclusion

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Analysis LS and LMS. indicates the likelihood of v t being the true motion of a pixel at s relative to the other hypothesis v b. Template MV is not as reliable for compensating pixels in the upper-left area as predicted by the theoretical results. Taos modelZhengs modelLS solution

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Analysis LS and LMS So, we would expect to drop to zero (or, equivalently, to increase to unity) without amendmentwith amendmentMultiple reference frames

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Results Reductions in mean-square error

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Outline Introduction Background Bi-prediction Combining TMP and BMC Analysis LS and LMS Experiment Results Conclusion

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Experiment Results Random Access High Efficiency Random Access Main Low-Delay B High Efficiency Low-Delay B Main

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Experiment Results

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Outline Introduction Background Bi-prediction Combining TMP and BMC Analysis LS and LMS Experiment Results Conclusion

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We proposed a bi-prediction scheme that combines BMC and TMP predictors through OBMC. TMP is inferior to BMC, but is, in general, superior to SKIP prediction. The data dependency complicates the pipeline design and hinders parallel processing. The proposed method restricted the use of TB-mode to 2Nx2N PUs only.

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