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LSH-based Motion Estimation. Alex Giladi. Motion Estimation. Motivation Utilize temporal redundancy for better video compression Improve video quality in MPEG-1 / MPEG-2 / MPEG-4 / H.264 AVC Definition For each block b s I(t), find closest b r I(t-1)

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## LSH-based Motion Estimation

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**LSH-based Motion Estimation**Alex Giladi**Motion Estimation**Motivation • Utilize temporal redundancy for better video compression • Improve video quality in MPEG-1 / MPEG-2 / MPEG-4 / H.264 AVC Definition • For each block bs I(t), find closest br I(t-1) • Objective: minimize the residue, bs – br • Search ranges: ± 64 is common for NTSC broadcast (720x480) • Assume: 16x16 blocks, done in MSE sense Algorithms • Full search (brute force): • 400K MSE computations for ± 16 on CIF (352x288) • 132M MSE computations for ± 64 on 1080i (1920x1080) • Real time: only 33.36ms per picture • Fast algorithms exist • Speedups to full search • Variants of logarithmic search • Hierarchical motion estimation LSH-based motion estimation**Motion estimation and LSH**Re-definition: • 16x16 block is represented as a vector in 256. • Similarity measure: L2 norm • Hash functions: dot product with a random vector in 256 Algorithm • Hash similar blocks to same buckets. • Pick blocks that hashed to the same bucket • Find best match among these LSH-based motion estimation**Motion estimation and LSH**Why LSH? • We don’t need exact answer • An approximation is enough; • We can allow longer pre-processing time • We don’t need an answer where blocks are dissimilar • These would not be coded using temporal prediction • Very large search ranges can be supported Problems • Search radius • Large radius – too many candidate blocks to be considered • Small radius – too many blocks have no pairs • Requires a large amount of additional memory (vs. none in the regular algorithms) • Requires several dot product computations per pixel LSH-based motion estimation**Results**• Answers are sufficiently close to the true values • Complexity is reduced • No answer for several blocks LSH-based motion estimation**Conclusion**Using LSH • Needs tuning and speedups • Can potentially reduce ME complexity, when large search range is required. Extensions: • Prefer closer vectors closer pictures • represent x,y,t vector components in 259 • Multiple references • Fast candidate elimination techniques LSH-based motion estimation

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