Motion estimation for content adaptive video compression

Author(s):  
Jiancong Luo ◽  
I. Ahmad ◽  
Yongfang Liang ◽  
Yu Sun
2008 ◽  
Vol 18 (7) ◽  
pp. 900-909 ◽  
Author(s):  
Jiancong Luo ◽  
I. Ahmad ◽  
Yongfang Liang ◽  
V. Swaminathan

2011 ◽  
Vol 145 ◽  
pp. 277-281
Author(s):  
Vaci Istanda ◽  
Tsong Yi Chen ◽  
Wan Chun Lee ◽  
Yuan Chen Liu ◽  
Wen Yen Chen

As the development of network learning, video compression is important for both data transmission and storage, especially in a digit channel. In this paper, we present the return prediction search (RPS) algorithm for block motion estimation. The proposed algorithm exploits the temporal correlation and characteristic of returning origin to obtain one or two predictive motion vector and selects one motion vector, which presents better result, to be the initial search center. In addition, we utilize the center-biased block matching algorithms to refine the final motion vector. Moreover, we used adaptive threshold technique to reduce the computational complexity in motion estimation. Experimental results show that RPS algorithm combined with 4SS, BBGDS, and UCBDS effectively improves the performance in terms of mean-square error measure with less average searching points. On the other hand, accelerated RPS (ARPS) algorithm takes only 38% of the searching computations than 3SS algorithm, and the reconstruction image quality of the ARPS algorithm is superior to 3SS algorithm about 0.30dB in average overall test sequences. In addition, we create an asynchronous learning environment which provides students and instructors flexibility in learning and teaching activities. The purpose of this web site is to teach and display our researchable results. Therefore, we believe this web site is one of the keys to help the modern student achieve mastery of complex Motion Estimation.


2012 ◽  
Vol 220-223 ◽  
pp. 2445-2449
Author(s):  
Wen Dan Xu ◽  
Xin Quan Lai ◽  
Dong Lai Xu

This paper presents an improved video segmentation scheme, which consists of two stages: initial segmentation and motion estimation. In the initial segmentation, the watershed transformation followed by a region adjacency graph guided region merging process is used to partition the first video frame into spatial homogenous regions. Then the motion of changed region is estimated. Based on the highly efficient quadratic motion model, the motion estimation is undertaken using Gauss-Newton Levenberg-Marquardt method to minimize the least-square error function. Experimental results show the proposed scheme provides high performance in terms of segmentation accuracy and video compression ratio.


2020 ◽  
Vol 14 (5) ◽  
pp. 935-943
Author(s):  
Mehdi Saeedi ◽  
Boris Ivanovic ◽  
Tomasz Stolarczyk ◽  
Ihab Amer ◽  
Gabor Sines

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