Browsing by Author "Yang, SW"

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A hierarchical decimation lattice based on N-queen with an application for motion estimation
(2003-08-01)
Wang, CN
;
Yang, SW
;
Liu, CM
;
Chiang, TH
;
資訊科學與工程研究所
We present a novel technique, N-queen lattice, to spatially subsample a block of pixels. Although this lattice is pertinent to many applications, we present an application to speed up motion estimation with minimal loss of coding efficiency. The N-queen lattice is constructed to characterize spatial features in all directions. It can be hierarchically organized for motion estimation With variable nonsquare block size. Despite the randomized lattice structure, we demonstrate that it is possible to achieve compact data storage architecture for efficient memory access and simple hardware implementation. Our simulations show that the N-queen lattice is superior to several existing sampling techniques with improvement in speed by about N times and small loss. in peak SNR.
A hierarchical N-queen decimation lattice and hardware architecture for motion estimation
(2004-04-01)
Wang, CN
;
Yang, SW
;
Liu, CM
;
Chiang, TH
;
資訊工程學系
A subsampling structure, an N-Queen lattice, for spatially decimating a block of pixels is presented. Despite its use for many applications, we demonstrate that the N-Queen lattice can be used to speed up motion estimation with nominal loss of coding efficiency. With a simple construction, the N-Queen lattice characterizes the spatial features in the vertical, horizontal, and diagonal directions for both texture and edge areas. Especially in the 4-Queen case, every skipped pixel has the minimal and equal distance of unity to the selected pixel. It can be hierarchically organized for variable nonsquare block-size motion estimation. Despite the randomized lattice, we design compact data storage architecture for efficient memory access and simple hardware implementation. Our simulations show that the N-Queen lattice is superior to several existing sampling techniques with improvement in speed by about N times and small loss in peak SNR (PSNR). The loss in PSNR is negligible for slow-motion video sequences and is less than 0.45 M at worst for high-motion estimation sequences.
Fast motion estimation using N-queen pixel decimation
(2001)
Yang, SW
;
Wang, CN
;
Liu, CM
;
Chiang, TH
;
資訊科學與工程研究所
We present a technique to improve the speed of block motion estimation using only a subset of pixels from a block to evaluate the distortion with minimal loss of coding efficiency. To select such a subset we use a special sub-sampling structure, N-queen pattern. The N-queen pattern can characterize the spatial information in the vertical, horizontal and diagonal directions for both texture and edge features. In the 4-queen case, it has a special property that every skipped pixel has the minimal and equal distance of one to the selected pixel. Despite of the randomized pattern, our technique has compact data storage architecture. Our results show that the pixel decimation of N-queen patterns improves the speed by about N times with small loss in PSNR. The loss in PSNR is negligible for slow motion video sequence and has 0.45 dB loss in PSNR at worst for high motion video sequence.