High Performance Architecture of Motion Estimation Algorithm for Video Compression

2016 ◽  
Vol 25 (08) ◽  
pp. 1650083
Author(s):  
P. Muralidhar ◽  
C. B. Rama Rao
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
High Performance ◽  
Estimation Algorithm ◽  
Block Matching ◽  
High Definition ◽  
Systolic Architecture ◽  
Hardware Architectures ◽  
Computationally Intensive ◽  
Full Search

Motion estimation (ME) is a highly computationally intensive operation in video compression. Efficient ME architectures are proposed in the literature. This paper presents an efficient low computational complexity systolic architecture for full search block matching ME (FSBME) algorithm. The proposed architecture is based on one-bit transform-based full search (FS) algorithm. The proposed ME hardware architectures perform FS ME for four macroblocks (MBs) in parallel. The proposed hardware architecture is implemented in VHDL. The FSBME hardware consumes 34% of the slices in a Xilinx Vertex XC6vlx240T FPGA device with a maximum frequency of 133[Formula: see text]MHz and is capable of processing full high definition (HD) ([Formula: see text]) frames at a rate of 60 frames per second.

HIGH PERFORMANCE, LOW-COMPLEXITY LINE-BASED MOTION ESTIMATION ALGORITHM WITH SMOOTHING AND PREPROCESSING

2009 ◽  
Vol 23 (01) ◽  
pp. 101-114 ◽  
Author(s):  
LI WERN CHEW ◽  
WAI CHONG CHIA ◽  
LI-MINN ANG ◽  
KAH PHOOI SENG
Keyword(s):  
Motion Estimation ◽  
High Performance ◽  
Reference Frames ◽  
Estimation Algorithm ◽  
Low Complexity ◽  
Block Matching ◽  
Absolute Difference ◽  
Current Frame ◽  
Block Matching Algorithm ◽  
The Difference

This paper introduces a smoothing and preprocessing (S+P) technique for a line-based one-bit-transform (1BT) motion estimation scheme. In the proposed algorithm, a smoothing threshold ( Threshold S) is incorporated into the 1BT convolutional kernel. By using the smoothing threshold, scattering noise which is a common problem in most 1BT images can be greatly reduced. After the transformation, the 1BT images for the current and reference frames are divided into a number of macroblocks. The macroblock in the current frame is first compared with the macroblock at the same position in the reference frame. If the Sum of Absolute Difference (SAD) is below a certain preprocessing threshold ( Threshold P), the macroblock in the current frame is considered to have negligible movement and motion search is not performed. Simulation results show that this technique achieves high performance and greatly reduces the number of search operations. By incorporating the S+P technique, the PSNR achieved by the 1BT is approaches the performance of the 8-bit Full Search Block Matching Algorithm (FSBMA), and the difference is as low as 0.08 dB. In addition, this technique outperforms current state-of-the-art 1BT motion estimation techniques. An improvement in PSNR performance by up to 0.6 dB and a reduction in the number of search operations by 60% to 93% is achieved using video conferencing sequences.

scholarly journals Literature survey on motion estimation techniques

2018 ◽  
Vol 7 (2.12) ◽  
pp. 394
Author(s):  
S Mahaboob Basha ◽  
M Kannan
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
Motion Compensation ◽  
Literature Survey ◽  
Block Matching ◽  
Estimation Techniques ◽  
Compression Performance ◽  
Diamond Search ◽  
Full Search

Motion Estimation (ME) is one of the most intensive computational operations in video compression techniques. Video compression algo-rithm utilizes numerous standards such as MPEG1, MPEG4 AND H.261, H.264. Compression performance can be increased drastically by efficient motion estimation techniques by which energy is reduced within the residual frames involved in motion compensation. In this paper literature survey of motion estimation especially considering block matching ME (Motion Estimation). In this paper, comparison is made between the already existing block matching algorithms and their limitations in motion estimation along with their applications. Many algorithms including Three Step Search (TSS), Improved Three Step Search (ITSS), New Three Step Search (NTSS), Four Step Search (4SS), Diamond Search (DS), Flexible Triangle Search(FTS), Full Search(FS), Modified Full Search(MDF) are compared and their per-formance measures are discussed in this paper.  

Adaptive Return Prediction Block Matching Algorithms for Video Coding

2011 ◽  
Vol 145 ◽  
pp. 277-281
Author(s):  
Vaci Istanda ◽  
Tsong Yi Chen ◽  
Wan Chun Lee ◽  
Yuan Chen Liu ◽  
Wen Yen Chen
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
Web Site ◽  
Motion Vector ◽  
Temporal Correlation ◽  
Block Matching ◽  
Asynchronous Learning ◽  
Learning And Teaching ◽  
Block Motion ◽  
Reconstruction Image

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.

An Improved Object-Based Video Segmentation Method

2012 ◽  
Vol 220-223 ◽  
pp. 2445-2449
Author(s):  
Wen Dan Xu ◽  
Xin Quan Lai ◽  
Dong Lai Xu
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
High Performance ◽  
Video Segmentation ◽  
Error Function ◽  
Least Square ◽  
Video Frame ◽  
Merging Process ◽  
Initial Segmentation ◽  
Two Stages

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.

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