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Ai-Mei Huang and Truong Nguyen Video Processing Lab ECE Dept, UCSD, La Jolla, CA 92093 This paper appears in: Image Processing, 2007. ICIP 2007. IEEE International Conference on. A NOVEL MULTI-STAGE MOTION VECTOR PROCESSING METHOD FOR MOTION COMPENSATED FRAME INTERPOLATION. Overview .
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Ai-Mei Huang and Truong Nguyen Video Processing Lab ECE Dept, UCSD, La Jolla, CA 92093 This paper appears in: Image Processing, 2007. ICIP 2007. IEEE International Conference on A NOVEL MULTI-STAGE MOTION VECTOR PROCESSING METHOD FOR MOTIONCOMPENSATED FRAME INTERPOLATION
Overview • Introduction • Block diagram of the proposed algorithms • Prediction residual energy analysis • The proposed multi-stage motion vector processing method • Simulation results • Conclusions
Introduction • Motion-compensated frame interpolation (MCFI) improves temporal quality by increasing the frame rate at the decoder. • Frame interpolation for compressed video remains a problem due to the use of improper MVsare often generated. • The proposed algorithms preserve the object structure informationbut also produce a smoother motion vector field (MVF).
Prediction residual energy analysis(1/5) • In [4], we have discussed that there exists a strong correlation between MV reliability and its associated residual energy. • These high residual energies regions are distributed over where object edges are located. • Let vm,n denote the MV of each 8×8 block. We classify vm,ninto three different reliability levels, reliable, possibly reliable.
Prediction residual energy analysis(2/5) • For a MB (16×16) with only one MV, we simply assign the same MV to all four 8×8 blocks(bm,n): • If Em,n≧ ε1 , it will be considered as unreliable(L1). • Consider intra-coded MBs as unreliable(L1). • The neighboring of L1MBs or MVs in the same MB will be classified as possibly reliable (L2). • Other MBs will be classified as reliable (L3).
Prediction residual energy analysis(3/5) Motion Vector Reliability Map
Prediction residual energy analysis(4/5) • Analyze the connectivityof the unreliable MVs in MVRM and create a MB merging map. • If a MB that has unreliable MVs connecting to other unreliableMVs in vertical, horizontal or diagonal directionsin adjacent MBs, these MBs will be merged. • The merging process is performed on a MB basis using MVRM, and all MBs will be examined in a raster scan order. • The 32×32 block size is the maximumfor merging.
Prediction residual energy analysis(5/5) • The diagonal direction is not considered for intra-intra MB merging, because the possibility for two diagonal intra-coded MBs belonging to the same object is lower. (c) MV reliability classification map. Unreliable and reliable MVs are marked in yellow and white colors, respectively. Intra-coded MBs are marked in cyan color. (d) MB merging map.
The proposed multi-stage motion vector processing method(1/4) • Find the best MV for each merged group: • If the ABPD of v*bis less than a threshold ε2 ,assignv*bto the merged MBs in Cu. • Otherwise, drop the selected MV(v*b) and wait until a proper MV propagates to its neighborhood in next iteration. • Process stops until all merged groups have been assigned new MVs. S denotes the reliable MVs in merge group & adjacent blocks. Cu denotes the merged group.
The proposed multi-stage motion vector processing method(2/4) • Reclassify MV reliability based on BPD resulted from the selected MV. • BPD(m, n) of each 8×8 block is obtained by simply summing up difference error like Eq(1). • If BPD(m, n) is higher thanε3,vm,n is unreliable(L1). • Otherwise, other MVs will be classified as reliable(L2). • If the MB consists of multiple motion, those unreliable MV can be easily detected by BPD.
The proposed multi-stage motion vector processing method(3/4) • For those unreliable MVs of 8×8 blocksin the updated MVRM, correct them by using a reliability and similarity constrained vector median filter: Scontains the neighboring MVs centered at vm,n di,jdenotes the distance between vi,j and vm,n Vm,n
The proposed multi-stage motion vector processing method(3/4) • For those unreliable MVs of 8×8 blocks in the updated MVRM, correct them by using a reliability and similarity constrained vector median filter: • Two MVs are considered to be similar if the angle distance(di,j) is below a threshold, ε4. • Before updating v*m,nin MVF2, check on BPD of v∗m,n to ensure that error energy is descended. • If the energy check fail, correct it in next iteration. Scontains the neighboring MVs centered at vm,n di,jdenotes the distance between vi,j and vm,n
The proposed multi-stage motion vector processing method(4/4) • MV smoothing process in [3] to reduce visual artifacts due to high BPD. • On the frame boundary, using unidirectional interpolation based on the directions of MVs:
CONCLUSIONS • We propose a novel algorithm based on the received information for MCFI . • Accomplishing the concept of object motion without complex motion estimation. • The method outperforms other conventional methods on both objective and subjective video quality.