 Research Article
 Open Access
A ContentMotionAware Motion Estimation for QualityStationary Video Coding
 MengChun Lin^{1}Email author and
 LanRong Dung^{1}
https://doi.org/10.1155/2010/403634
© M.C. Lin and L.R. Dung. 2010
 Received: 31 March 2010
 Accepted: 1 August 2010
 Published: 18 August 2010
Abstract
The blockmatching motion estimation has been aggressively developed for years. Many papers have presented fast blockmatching algorithms (FBMAs) for the reduction of computation complexity. Nevertheless, their results, in terms of video quality and bitrate, are rather contentvarying. Very few FBMAs can result in stationary or quasistationary video quality for different motion types of video content. Instead of using multiple search algorithms, this paper proposes a qualitystationary motion estimation with a unified search mechanism. This paper presents a contentmotionaware motion estimation for qualitystationary video coding. Under the rate control mechanism, the proposed motion estimation, based on subsample approach, adaptively adjusts the subsample ratio with the motionlevel of video sequence to keep the degradation of video quality low. The proposed approach is a companion for all kinds of FBMAs in H.264/AVC. As shown in experimental results, the proposed approach can produce stationary quality. Comparing with the fullsearch blockmatching algorithm, the quality degradation is less than 0.36 dB while the average saving of power consumption is 69.6%. When applying the proposed approach for the fast motion estimation (FME) algorithm in H.264/AVC JM reference software, the proposed approach can save 62.2% of the power consumption while the quality degradation is less than 0.27 dB.
Keywords
 Video Sequence
 Quality Degradation
 Speedup Ratio
 Motion Estimation Algorithm
 Aliasing Problem
1. Introduction
Motion Estimation (ME) has been proven to be effective to exploit the temporal redundancy of video sequences and, therefore, becomes a key component of multimedia standards, such as MPEG standards and H.26X [1–7]. The most popular algorithm for the VLSI implementation of motion estimation is the blockbased full search algorithm [8–11]. The blockbased full search algorithm has high degree of modularity and requires low control overhead. However, the full search algorithm notoriously needs high computation load and large memory size [12–14]. The highly computational cost has become a major problem on the implementation of motion estimation.
To reduce the computational complexity of the fullsearch blockmatching (FSBM) algorithm, researchers have proposed various fast algorithms. They either reduce search steps [12, 15–22] or simplify calculations of error criterion [8, 23–25]. Some researchers combined both stepreduction and criterionsimplifying to significantly reduce computational load with little degradation. By combining stepreduction and criterionsimplifying, some researchers proposed twophase algorithms to balance the performance between complexity and quality [26–28]. These fast algorithms have been shown that they can significantly reduce the computational load while the average quality degradation is little. However, a real video sequence may have different types of content, such as slowmotion, moderatemotion, and fastmotion, and little quality degradation in average does not imply the quality is acceptable all the time. The fast blockmatching algorithms (FBMAs) mentioned above are all independent of the motion type of video content, and their quality degradation may considerably vary within a real video sequence.
Few papers present qualitystationary motion estimation algorithms for video sequences with mixed fastmotion, moderatemotion, and slowmotion content. Huang et al. [29] propose an adaptive, multiplesearchpattern FBMA, called the ATDB algorithm, to solve the contentdependent problem. Motivated by the characteristics of threestep search (TSS), diamond search (DS), and blockbased gradient descent search (BBGDS), the ATDB algorithm dynamically switches search patterns according to the motion type of video content. Ng et al. [30] propose an adaptive search patterns switching (SPS) algorithm by using an efficient motion content classifier based on error descent rate (EDR) to reduce the complexity of the classification process of the ATDB algorithm. Other multiple search algorithms have been proposed [31, 32]. They showed that using multiple search patterns in ME can outperform standalone ME techniques.
Instead of using multiple search algorithms, this paper intends to propose a qualitystationary motion estimation with a unified search mechanism. The qualitystationary motion estimation can appropriately adjust the computational load to deliver stationary video quality for a given bitrate. Herein, we used the subsample or pixeldecimation approach for the motionvector (MV) search. The use of subsample approach is twofolded. First, the subsample approach can be applied for all kinds of FBMAs and provide high degree of flexibility for adaptively adjusting the computational load. Secondly, the subsample approach is feasible and scalable for either hardware or software implementation. The proposed approach is not limited for FSBM, but valid for all kinds of FBMAs. The proposed approach is a companion for all kinds of FBMAs in H.264/AVC.
Articles in [33–38] present the subsample approaches for motion estimation. The subsample approaches are used to reduce the computational cost of the blockmatching criterion evaluation. Because the subsample approaches always desolate some pixels, the accuracy of the estimated MVs becomes the key issue to be solved. As per the fundamental of sampling, downsampling a signal may result in aliasing problem. The narrower the bandwidth of the signal, the lower the sampling frequency without aliasing problem will be. The published papers [33–38] mainly focus on the subsample pattern based on the intraframe highfrequency pixels (i.e., edges). Instead of considering spatial frequency bandwidth, to be aware of the content motion, we determine the subsample ratio by temporal bandwidth. Applying high subsample ratio for slow motion blocks would not reduce the accuracy for slow motion or result in large amount of prediction residual. Note that the amount of prediction residual is a good measure of the compressibility. Under a fixed bitrate constraint, the compressibility affects the compression quality. Our algorithm can adaptively adjust the subsample ratio with the motionlevel of video sequence. When the interframe variation becomes high, we consider the motionlevel of interframe as the fastmotion and apply low subsample ratio for motion estimation. When the interframe variation becomes low, we apply high subsample ratio for motion estimation.
2. Generic Subsample Algorithm
3. HighFrequency Aliasing Problem
According to sampling theory [41], the decrease of sampling frequency will result in aliasing problem for highfrequency band. On the other hand, when the bandwidth of signal is narrow, higher downsample ratio or lower sampling frequency is allowed without aliasing problem. When applying the generic subsample algorithm for video compression, for highvariation sequences, the aliasing problem occurs and leads to considerable quality degradation because the highfrequency band is messed up. Papers [42, 43] hence propose adaptive subsample algorithms to solve the problem. They employed the variable subsample pattern for spatial highfrequency band, that is, edge pixels. However, the motion estimation is used for interframe prediction and temporal highfrequency band should be mainly treated carefully. Therefore, we determine the subsample ratio by the interframe variation. The interframe variation can be characterized by the motionlevel of content. The ZMVC is a good sign for the motionlevel detection because it is feasible for measurement and requires low computation load. The high ZMVC means that the interframe variation is low and vice versa. Hence, we can set high subsample ratio for high ZMVCs and low subsample ratio for low ZMVCs. Doing so, the aliasing problem can be alleviated and the quality can be frozen within an acceptable range.
4. Adaptive Motion Estimation with Variable Subsample Ratios
In the proposed algorithm, we determine the subsample ratio at the beginning of each GOP because the ZMVC of the first interframe prediction is the most accurate. The reference frame in the first interframe prediction is a reconstructed Iframe but others are not for each GOP. Only the reconstructed Iframe does not incur the influence resulted from the quality degradation of the inaccurate interframe prediction. That is, we only calculate the ZMVC of the first Pframe for the subsample ratio selection to efficiently save the computational load of ZMVC. Note that the ZMVC of the first Pframe is calculated by using 16 : 16 subsample ratio. Given the ZMVC of the first Pframe, the motionlevel is determined by comparing the ZMVC with preestimated threshold values. The threshold values is decided statistically using popular video clips.
Threshold setting for different conditions under the 0.3 of visual quality degradation.





 

 393  387  376  344  305  232 
 368  356  344  251  239  190 
 265  242  227  297  179  49 
5. Selection of ZMVC Threshold and Simulation Results
Testing video sequences.
Video sequence  Number of frames  

Fast Motion  Dancer  250 
Foreman  300  
Flower  250  
Normal Motion  Table  300 
Mother_Daughter (M_D)  300  
Weather  300  
Children  300  
Paris  300  
Slow Motion  News  300 
Akiyo  300  
Silent  300  
Container  300 
Analysis of quality degradation using three adaptive subsample rate decisions.





 

Dancer  −0.02  −0.02  −0.02  −0.09  −0.36  −0.77 
Foreman  −0.09  −0.15  −0.16  −0.31  −0.33  −0.59 
Flower  0  −0.04  −0.04  −0.15  −0.27  −0.44 
Table  −0.05  −0.06  −0.11  −0.19  −0.26  −0.34 
M_D  −0.2  −0.22  −0.23  −0.33  −0.36  −0.45 
Weather  −0.2  −0.22  −0.25  −0.29  −0.33  −0.33 
Children  −0.13  −0.16  −0.19  −0.28  −0.29  −0.29 
Paris  −0.17  −0.22  −0.21  −0.31  −0.35  −0.35 
News  −0.08  −0.1  −0.12  −0.15  −0.2  −0.20 
Akiyo  −0.09  −0.12  −0.12  −0.15  −0.15  −0.15 
Silent  −0.06  −0.05  −0.04  −0.06  −0.09  −0.09 
Container  −0.02  −0.02  −0.02  −0.02  −0.02  −0.02 
Analysis of average subsample ratio using three adaptive subsample rate decisions.




 

Dancer  16 : 15.55  16 : 15.55  16 : 15.55  16 : 14.43  16 : 11.75 
Foreman  16 : 14.32  16 : 13.31  16 : 12.93  16 : 10.61  16 : 10.24 
Flower  16 : 16.00  16 : 15.10  16 : 15.10  16 : 11.98  16 : 8.80 
Table  16 : 9.50  16 : 9.03  16 : 7.17  16 : 5.32  16 : 4.57 
M_D  16 : 7.08  16 : 6.43  16 : 6.34  16 : 3.92  16 : 3.55 
Weather  16 : 5.87  16 : 5.32  16 : 4.39  16 : 3.18  16 : 3.00 
Children  16 : 7.82  16 : 7.27  16 : 6.43  16 : 3.83  16 : 3.27 
Paris  16 : 6.52  16 : 6.25  16 : 5.22  16 : 3.46  16 : 3.00 
News  16 : 7.45  16 : 6.71  16 : 4.95  16 : 3.09  16 : 3.00 
Akiyo  16 : 4.76  16 : 3.83  16 : 3.46  16 : 3.00  16 : 3.00 
Silent  16 : 7.27  16 : 7.08  16 : 6.34  16 : 3.92  16 : 3.00 
Container  16 : 3.18  16 : 3.00  16 : 3.00  16 : 3.00  16 : 3.00 
Average  16 : 8.58  16 : 8.04  16 : 7.35  16 : 5.60  16 : 4.87 
Performance analysis of quality degradation for various video sequences using various methods. (Note that the proposed algorithm can always keep the quality degradation low.)
Full search block matching (FSBM) algorithm  

Generic  Generic  Generic  Generic  Generic  Generic  Generic  Generic  Proposed  
Video  16 : 16  16 : 14  16 : 12  16 : 10  16 : 8  16 : 6  16 : 4  16 : 2  algorithm 
sequence  subsample  subsample  subsample  subsample  subsample  subsample  subsample  subsample  (70%) 
ratio  ratio  ratio  ratio  ratio  ratio  ratio  ratio  
PSNRY  PSNRY  PSNRY  PSNRY  PSNRY  PSNRY  PSNRY  PSNRY  PSNRY  
Dancer  34.42  −0.18  −0.33  −0.53  −0.7  −0.86  −0.92  −0.93  −0.36 
Foreman  30.51  −0.09  −0.18  −0.27  −0.4  −0.55  −0.72  −0.78  −0.33 
Flower  20.58  −0.05  −0.1  −0.18  −0.28  −0.4  −0.49  −0.51  −0.27 
Table  32.04  −0.02  −0.04  −0.09  −0.13  −0.16  −0.24  −0.35  −0.26 
M_D  40.34  −0.03  −0.02  −0.08  −0.15  −0.25  −0.35  −0.46  −0.36 
Weather  33.26  −0.06  −0.1  −0.09  −0.15  −0.22  −0.28  −0.33  −0.33 
Children  30  −0.01  −0.05  −0.11  −0.14  −0.17  −0.22  −0.29  −0.29 
Paris  31.67  0  −0.04  −0.05  −0.1  −0.13  −0.27  −0.33  −0.35 
News  38.27  −0.02  −0.01  −0.04  −0.06  −0.09  −0.13  −0.22  −0.2 
Akiyo  43.36  0.01  −0.01  −0.02  −0.03  −0.05  −0.09  −0.16  −0.15 
Silent  35.62  −0.03  −0.03  −0.03  −0.02  −0.02  −0.06  −0.08  −0.09 
Container  36.47  0  −0.01  −0.01  0  −0.02  −0.02  −0.02  −0.02 
Performance analysis of speedup ratio.
Full search block matching (FSBM) algorithm  

Generic  Generic  Generic  Generic  Generic  Generic  Generic  Generic  Proposed  
Video  16 : 16  16 : 14  16 : 12  16 : 10  16 : 8  16 : 6  16 : 4  16 : 2  algorithm 
sequence  subsample  subsample  subsample  subsample  subsample  subsample  subsample  subsample  (70%) 
ratio  ratio  ratio  ratio  ratio  ratio  ratio  ratio  ratio  
Speedup  Speedup  Speedup  Speedup  Speedup  Speedup  Speedup  Speedup  Speedup  
Dancer  1  1.143  1.3334  1.60011  2.0001  2.6671  4.0006  8.0012  1.36 
Foreman  1  1.143  1.3334  1.60013  2.0002  2.6669  4.0003  8.0006  1.56 
Flower  1  1.143  1.3334  1.60011  2.0001  2.6671  4.0006  8.0012  1.82 
Table  1  1.143  1.3334  1.60013  2.0002  2.6669  4.0003  8.0006  3.50 
M_D  1  1.143  1.3334  1.60013  2.0002  2.6669  4.0003  8.0006  4.50 
Weather  1  1.143  1.3334  1.60013  2.0002  2.6669  4.0003  8.0006  5.33 
Children  1  1.143  1.3334  1.60013  2.0002  2.6669  4.0003  8.0006  4.89 
Paris  1  1.143  1.3334  1.60013  2.0002  2.6669  4.0003  8.0006  5.33 
News  1  1.143  1.3334  1.60013  2.0002  2.6669  4.0003  8.0006  5.33 
Akiyo  1  1.143  1.3334  1.60013  2.0002  2.6669  4.0003  8.0006  5.33 
Silent  1  1.143  1.3334  1.60013  2.0002  2.6669  4.0003  8.0006  5.33 
Container  1  1.143  1.3334  1.60013  2.0002  2.6669  4.0003  8.0006  5.33 
Performance analysis of quality degradation for various video sequences using various methods. (Note that the proposed algorithm can always keep the quality degradation low.)
Fast motion estimation (FME) algorithm  

Generic  Generic  Generic  Generic  Generic  Generic  Generic  Generic  Proposed  
Video  16 : 16  16 : 14  16 : 12  16 : 10  16 : 8  16 : 6  16 : 4  16 : 2  algorithm 
sequence  subsample  subsample  subsample  subsample  subsample  subsample  subsample  subsample  (70%) 
ratio  ratio  ratio  ratio  ratio  ratio  ratio  ratio  
PSNRY  PSNRY  PSNRY  PSNRY  PSNRY  PSNRY  PSNRY  PSNRY  PSNRY  
Dancer  33.48  −0.17  −0.31  −0.47  −0.63  −0.84  −1.01  −0.99  −0.05 
Foreman  29.63  −0.06  −0.11  −0.17  −0.21  −0.29  −0.45  −0.69  −0.08 
Flower  19.64  −0.01  −0.03  −0.06  −0.08  −0.15  −0.25  −0.48  −0.01 
Table  31.07  −0.02  −0.03  −0.06  −0.07  −0.11  −0.17  −0.25  −0.09 
M_D  39.44  0  0  −0.02  −0.02  −0.05  −0.12  −0.31  −0.24 
Weather  32.34  −0.01  −0.02  −0.05  −0.09  −0.07  −0.13  −0.27  −0.26 
Children  29.12  −0.06  −0.08  −0.02  −0.15  −0.16  −0.23  −0.3  −0.27 
Paris  30.69  0.04  0.02  0.04  0.04  0.01  −0.05  −0.21  −0.15 
News  37.29  0.03  0.05  0.03  0.05  0.05  0.03  −0.05  −0.05 
Akiyo  42.38  0.03  0.04  0.03  0.02  −0.01  −0.02  −0.07  −0.08 
Silent  34.64  0  0  0  0  0.04  0.05  0.02  0 
Container  35.5  0  0.02  0.01  0  0  0.01  −0.03  −0.02 
Performance analysis of speedup ratio.
Fast motion estimation (FME) algorithm  

Generic  Generic  Generic  Generic  Generic  Generic  Generic  Generic  Proposed  
Video  16 : 16  16 : 14  16 : 12  16 : 10  16 : 8  16 : 6  16 : 4  16 : 2  Algorithm 
sequence  subsample  subsample  subsample  subsample  subsample  subsample  subsample  subsample  (70%) 
ratio  ratio  ratio  ratio  ratio  ratio  ratio  ratio  ratio  
Speedup  Speedup  Speedup  Speedup  Speedup  Speedup  Speedup  Speedup  Speedup  
Dancer  1  1.147252  1.346325  1.626553  2.051174  2.768337  4.208802  8.5202  1.056017 
Foreman  1  1.14796  1.34685  1.6294  2.05981  2.78782  4.25265  8.2275502  1.16797 
Flower  1  1.143542  1.335488  1.603778  2.006855  2.63666  3.975399  8.096571  1.061454 
Table  1  1.150301  1.352315  1.637259  2.067149  2.7824  4.210231  8.497531  2.50664 
M_D  1  1.150295  1.349931  1.627438  2.040879  2.724727  4.086932  8.16456  4.611836 
Weather  1  1.153651  1.36162  1.653674  2.092012  2.815901  4.250473  8.529343  5.379942 
Children  1  1.219562  1.488654  1.719515  2.569355  3.51429  5.697292  12.43916  5.056478 
Paris  1  1.15079  1.354444  1.645437  2.083324  2.812825  4.270938  8.627448  5.422681 
News  1  1.150716  1.351302  1.631096  2.04845  2.740255  4.12047  8.253857  5.260884 
Akiyo  1  1.145874  1.340152  1.61157  2.017577  2.692641  4.04448  8.080182  5.35473 
Silent  1  1.15267  1.355195  1.63785  2.060897  2.7634  4.160839  8.338212  4.845362 
Container  1  1.149457  1.348652  1.626109  2.0412  2.731408  4.109702  8.226404  5.428775 
6. Conclusion
In this paper, we present a qualitystationary ME that is aware of content motion. By setting the subsample ratio according to the motionlevel, the proposed algorithm can have the quality degradation low all over the video frames and require low computation load. As shown in the experimental results, with the optimal threshold values, the algorithm can make the quality degradation less than 0.36 dB while saving 69.6% ( )) power consumption for FSBM. For the application of FBMA, the quality is stationary with the degradation of 0.27 dB and the power consumption is reduced by the factor of 62.2% ( )). The estimation of power consumption reduction is referred to the average subsampling ratio in that the power consumption should be proportional to the subsampling amount. The higher the subsampling amount, the more the power consumption. One can also adjust the size of search range or calculation precision for achieving the qualitystationary. However, either approach cannot have high degree of flexibility for hardware implementation.
Declarations
Acknowledgment
This work was supported in part by the National Science Council, R.O.C., under the grant number NSC 952221E009337MY3.
Authors’ Affiliations
References
 ISO/IEC CD 111722(MPEG1 Video) : Information technologycoding of moving pictures and asociated audio for digitsl storage media at up about 1.5 Mbits/s: Video. 1993.Google Scholar
 ISO/IEC CD 138182–ITUT H.262(MPEG2 Video) : Information technologygeneric coding of moving pictures and asociated audio information: Video. 1995.Google Scholar
 ISO/IEC 144962 (MPEG4 Video) : Information TechnologyGeneric Coding of AudioVisual Objects. Part2:Visual, 1999Google Scholar
 Wiegand T, Sullivan GJ, Luthra A: Draft ITUT Recommendation H.264 and Final Draft International Standard 1449610 AVC. VT of ISO/IEC JTC1/SC29/WG11 and ITUT SG16/Q.6, Doc. JVTG050r1, Geneva, Switzerland, May 2003Google Scholar
 Richardson I: H.264 and MPEG4 Video Compression. John Wiley & Sons, New York, NY, USA; 2003.View ArticleGoogle Scholar
 Wiegand T, Sullivan GJ, Bjøntegaard G, Luthra A: Overview of the H.264/AVC video coding standard. IEEE Transactions on Circuits and Systems for Video Technology 2003, 13(7):560576.View ArticleGoogle Scholar
 Kuhn P: Algorithm, Complexity Analysis and VLSI Architecture for MPEG4 Motion Estimation. Kluwer Academic Publishers, Dordrecht, The Netherlands; 1999.View ArticleMATHGoogle Scholar
 Do VL, Yun KY: A lowpower VLSI architecture for fullsearch blockmatching motion estimation. IEEE Transactions on Circuits and Systems for Video Technology 1998, 8(4):393398. 10.1109/76.709406View ArticleGoogle Scholar
 Shen JF, Wang TC, Chen LG: A novel lowpower fullsearch blockmatching motionestimation design for H.263+. IEEE Transactions on Circuits and Systems for Video Technology 2001, 11(7):890897. 10.1109/76.931116View ArticleGoogle Scholar
 Brünig M, Niehsen W: Fast fullsearch block matching. IEEE Transactions on Circuits and Systems for Video Technology 2001, 11(2):241247. 10.1109/76.905989View ArticleGoogle Scholar
 Sousa L, Roma N: Lowpower array architectures for motion estimation. Proceedings of the 3rd IEEE Workshop on Multimedia Signal Processing, 1999 679684.Google Scholar
 Jain JR, Jain AK: Displacement measurement and its application in interframe image coding. IEEE Transactions on Communications 1981, 29(12):17991808. 10.1109/TCOM.1981.1094950View ArticleGoogle Scholar
 Ogura E, Ikenaga Y, Iida Y, Hosoya Y, Takashima M, Yamashita K: Cost effective motion estimation processor LSI using a simple and efficient algorithm. IEEE Transactions on Consumer Electronics 1995, 41(3):690698. 10.1109/30.468021View ArticleGoogle Scholar
 Mietens S, De With PHN, Hentschel C: Computationalcomplexity scalable motion estimation for mobile MPEG encoding. IEEE Transactions on Consumer Electronics 2004, 50(1):281291. 10.1109/TCE.2004.1277875View ArticleGoogle Scholar
 Chen M, Chen L, Chiueh T: Onedimensional full search motion estimation algorithm for video coding. IEEE Transactions on Circuits and Systems for Video Technology 1994, 4(5):504509. 10.1109/76.322998View ArticleGoogle Scholar
 Li R, Zeng B, Liou ML: A new threestep search algorithm for block motion estimation. IEEE Transactions on Circuits and Systems for Video Technology 1994, 4(4):438442. 10.1109/76.313138View ArticleGoogle Scholar
 Tham JY, Ranganath S, Ranganath M, Kassim AA: A novel unrestricted centerbiased diamond search algorithm for block motion estimation. IEEE Transactions on Circuits and Systems for Video Technology 1998, 8(4):369377. 10.1109/76.709403View ArticleGoogle Scholar
 Zhu C, Lin X, Chau LP: Hexagonbased search pattern for fast block motion estimation. IEEE Transactions on Circuits and Systems for Video Technology 2002, 12(5):349355. 10.1109/TCSVT.2002.1003474View ArticleGoogle Scholar
 Kim KB, Jeon YG, Hong MC: Variable step search fast motion estimation for H.264/AVC video coder. IEEE Transactions on Consumer Electronics 2008, 54(3):12811286.View ArticleGoogle Scholar
 Sarwer MG, Wu QMJ: Adaptive variable blocksize early motion estimation termination algorithm for H.264/AVC video coding standard. IEEE Transactions on Circuits and Systems for Video Technology 2009, 19(8):11961201.View ArticleGoogle Scholar
 Chen Z, Xu J, He Y, Zheng J: Fast integerpel and fractionalpel motion estimation for H.264/AVC. Journal of Visual Communication and Image Representation 2006, 17(2):264290. 10.1016/j.jvcir.2004.12.002View ArticleGoogle Scholar
 Cai C, Zeng H, Mitra SK: Fast motion estimation for H.264. Signal Processing: Image Communication 2009, 24(8):630636. 10.1016/j.image.2009.02.012Google Scholar
 Li W, Salari E: Successive elimination algorithm for motion estimation. IEEE Transactions on Image Processing 1995, 4(1):105107. 10.1109/83.350809View ArticleGoogle Scholar
 Luo JH, Wang CN, Chiang T: A novel allbinary motion estimation (ABME) with optimized hardware architectures. IEEE Transactions on Circuits and Systems for Video Technology 2002, 12(8):700712. 10.1109/TCSVT.2002.800859View ArticleGoogle Scholar
 Kim NJ, Ertürk S, Lee HJ: Twobit transform based block motion estimation using second derivatives. IEEE Transactions on Consumer Electronics 2009, 55(2):902910.View ArticleGoogle Scholar
 Lee S, Chae SI: Motion estimation algorithm using low resolution quantisation. Electronics Letters 1996, 32(7):647648. 10.1049/el:19960420MathSciNetView ArticleGoogle Scholar
 Cheng HW, Dung LR: EFBLA: a twophase matching algorithm for fast motion estimation. Proceedings of the 3rd IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing, December 2002 2532: 112119.Google Scholar
 Su CL, Jen CW: Motion estimation using MSDfirst processing. IEE Proceedings: Circuits, Devices and Systems 2003, 150(2):124133. 10.1049/ipcds:20030332Google Scholar
 Huang SY, Cho CY, Wang JS: Adaptive fast blockmatching algorithm by switching search patterns for sequences with widerange motion content. IEEE Transactions on Circuits and Systems for Video Technology 2005, 15(11):13731384.View ArticleGoogle Scholar
 Ng KH, Po LM, Wong KM, Ting CW, Cheung KW: A search patterns switching algorithm for block motion estimation. IEEE Transactions on Circuits and Systems for Video Technology 2009, 19(5):753759.View ArticleGoogle Scholar
 Nie Y, Ma KK: Adaptive irregular pattern search with matching prejudgment for fast blockmatching motion estimation. IEEE Transactions on Circuits and Systems for Video Technology 2005, 15(6):789794.View ArticleGoogle Scholar
 Lim JH, Choi HW: Adaptive motion estimation algorithm using spatial and temporal correlation. Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM '01), August 2001, Victoria, Canada 2: 473476.Google Scholar
 Liu B, Zaccarin A: New fast algorithms for the estimation of block motion vectors. IEEE Transactions on Circuits and Systems for Video Technology 1993, 3(2):148157. 10.1109/76.212720View ArticleGoogle Scholar
 Cheung C, Po L: A hierarchical block motion estimation algorithm using partial distortion measure. Proceedings of the International Conference on Image Processing (ICIP '97), October 1997 3: 606609.View ArticleGoogle Scholar
 Cheung CK, Po LM: Normalized partial distortion search algorithm for block motion estimation. IEEE Transactions on Circuits and Systems for Video Technology 2000, 10(3):417422. 10.1109/76.836286View ArticleGoogle Scholar
 Wang CN, Yang SW, Liu CM, Chiang T:A hierarchical decimation lattice based on queen with an application for motion estimation. IEEE Signal Processing Letters 2003, 10(8):228231. 10.1109/LSP.2003.814403View ArticleGoogle Scholar
 Wang CN, Yang SW, Liu CM, Chiang T:A hierarchical queen decimation lattice and hardware architecture for motion estimation. IEEE Transactions on Circuits and Systems for Video Technology 2004, 14(4):429440. 10.1109/TCSVT.2004.825550View ArticleGoogle Scholar
 Cheng HW, Dung LR: A variopower ME architecture using contentbased subsample algorithm. IEEE Transactions on Consumer Electronics 2004, 50(1):349354. 10.1109/TCE.2004.1277884View ArticleGoogle Scholar
 Chan YL, Siu WC: New adaptive pixel decimation for block motion vector estimation. IEEE Transactions on Circuits and Systems for Video Technology 1996, 6(1):113118. 10.1109/76.486426View ArticleGoogle Scholar
 Wang YK, Wang YQ, Kuroda H: A globally adaptive pixeldecimation algorithm for blockmotion estimation. IEEE Transactions on Circuits and Systems for Video Technology 2000, 10(6):10061011. 10.1109/76.867940View ArticleGoogle Scholar
 Oppenheim AV, Schafer RW, Buck JR: DiscreteTime Signal Processing. PrenticeHall, Upper Saddle River, NJ, USA; 1999.Google Scholar
 Chan YL, Siu WC: New adaptive pixel decimation for block motion vector estimation. IEEE Transactions on Circuits and Systems for Video Technology 1996, 6(1):113118. 10.1109/76.486426View ArticleGoogle Scholar
 Wang Y, Wang Y, Kuroda H: A globally adaptive pixeldecimation algorithm for blockmotion estimation. IEEE Transactions on Circuits and Systems for Video Technology 2000, 10(6):10061011. 10.1109/76.867940View ArticleGoogle Scholar
 Dung LR, Lin MC: Widerange motion estimation architecture with dual search windows for high resolution video coding. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 2008, E91A(12):36383650. 10.1093/ietfec/e91a.12.3638View ArticleGoogle Scholar
 Joint Video Team : Reference Software JM10.2. http://iphome.hhi.de/suehring/tml/download/old_jm/
 http://www.m4if.org/resources.php#section26
Copyright
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.