# High Frame Rate Up Conversion Ana Bertran. Problem Statement Original Frame (30 fps) Dwnsmpld Frame (5 fps) Recovered Frame (30 fps) S & H LI MCLI MCwA.

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High Frame Rate Up Conversion Ana Bertran

Problem Statement Original Frame (30 fps) Dwnsmpld Frame (5 fps) Recovered Frame (30 fps) S & H LI MCLI MCwA Hypothesis: if we have acceleration MCwA should provide better results X=a+b*sin(  *t), v x =b*  *cos(  *t), a x =-(b*  2 *sin(  *t)

Motivation Uses of frame rate up conversion: Converting between standards (PAL to NTSC). Using MCwA less critical since frame rates not too far from each other. Low bit rate compression for video-confrncng, video-phone and video games. MCwA becomes more critical (from 10 fps to 30 fps) Problems with traditional methods: S&H motion looks jerky, not smooth, very choppy Linear interpolation without motion: image looks blurry where motion has occurred, we will see ghosts due to avg. btwn. frames.

Motivation Original Linear InterpolationSample & Hold

MCLI vs. MCwA MECatalog Occsns LMC AMC

ME For forward and backward MVs Overlapping BM 16x16 blcks, move by 8 Finer MV Selection Top Left block use its MV 1st row blocks min SAD (L, current or next frame) 1st colmn blocks min SAD (T, current or next frame) others min SAD (T, L, current or next frame) ½ pixel MV zoom in Take out illegal MVs

Cataloging Occlusions 1 2 3 4 For covered pixels we need to use frame 2 as the predicted image, one whose blocks we serch for. For uncovered pixels we need to use frame 3 as the predicted image, one whose blocks we serch for. Need to track block from 3 to 2 Criteria: compare SADs depending which one minimum classify.

Cataloging Occlusions Results blue=uncov red=cov Green=no occsn blue=uncov red=no occsn

LMC 3 cases No occlusions Uncovered pixels Covered pixels No occlusions Occlusions For each block along motion trajectory: MV x_est =w*MV x_3_2 MV y_est =w*MV y_3_2 Motion will be non-integer: interpolate Assumed motion in x Depending on +ve/-ve x mtn, unvov/cov: 32

AMC To solve distance x =v x *t+0.5*a x *t 2 you need two MVs but you can get more accurate motion trajectories by solving a LMS problem on 3 MVs. For each MV between two frames take the previous and next MVs to estimate the trajectory. Need to solve:

Frame rate up conversion a difficult problem Need to use true motion vector fields but block matching does poorly with this, specially across object boundaries and if the moving object is too small. Can´t cope with discontinuities in the velocity plane. Some motion can fall in between pixels Dealing with occlusions Motion can change dramatically between frames and we won´t realize it. (Critical vels) Not to talk about scene changes! – we no longer will have enough MVs to track motion trajectory.

Results/Demo  Will have the demo in the website by the end of the week. For now you can see the demo for the other types of FR up-conversion

Current temporary results

Future Work Need better MV estimation of the true MVs Implement ½ pixel resoltn or Use hierarchical block matching to find true motion MVs or Object based interpretation of the video to smooth out MVs Use 3D Recursive Search Block Matching Solve occlusion problem for both x and y movement

References Using Motion-Compensated Frame-Rate Conversion for the Correction of 3:2 Pulldown Artifacts in lman, Video Sequences, Kevin Hilman, Hyuon Wook Park, and Yongmin Kim True-Motion Estimation with 3-D Recursive Search Block Matching Gerard de Haan, Paul W.A.C. Biezen, Henk Huijgen and Olukayode A. Ojo Digital Video Standards Conversion in the presence of accelerated motion Amdrew J. Patti, M. Ibrahim Sezan and M.Murat Tekalp Framee Rate Up- Conversion using transmitted true motion vectors Yen-Kuang Chen, Anthony Vetro, Huifang Sun, and S.Y. Kung

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