Two-bit transform using local binary pattern method for low-complexity block motion estimation

Two-bit transform using local binary pattern method for low-complexity block motion estimation

Low bit-depth representation-based motion estimation approaches have been drawing considerable attention recently, mainly because of their small hardware footprint. In this paper, a new two-bit transform using a local binary pattern (LBP) for low-complexity motion estimation is proposed. The proposed approach utilizes the LBP method to obtain two-bit representations of video frames in the binarization process. Video frames are transformed into their low bit-depth representations by the LBP and then a motion estimation process is carried out using these binary frames. A Boolean exclusive-OR operation is used to calculate the number of nonmatching points metric instead of the conventional sum of absolute differences metric in the motion estimation stage. The proposed method reduces the computational complexity, especially in the binarization stage, while improving the motion estimation accuracy compared to existing one-bit and two-bit transform-based low-complexity motion estimation approaches in the literature.

___

  • [1] Chen TC, Chen YH, Tsai SF, Chien SY, Chen LG. Fast algorithm and architecture design of low-power integer motion estimation. IEEE T Circ Syst Vid 2007; 17: 568-577.
  • [2] Koga T, Linuma K, Hirano A, Lijima Y, Ishiguro T. Motion compensated interframe coding for video conferencing. In: Proceedings of the Natational Telecommunications Conference; 1981. pp. C9.6.1-C9.6.5.
  • [3] Zhu S, Ma KK. A new diamond search algorithm for fast block-matching motion estimation. IEEE T Circ Syst Vid 2000; 9: 287-290.
  • [4] Zhu C, Lin X, Chau LP. Hexagon-based search pattern for fast block motion estimation. IEEE T Circ Syst Vid 2002; 12: 349-355.
  • [5] Lee J, Choi M, Cho Y, Kim J, Cho WK. Fast H.264/AVC motion estimation algorithm using adaptive search range. In: 12th International Symposium on Integrated Circuits; 14{16 December 2009; Singapore. pp. 336-339.
  • [6] Bierling M. Displacement estimation by hierarchical block matching. In: SPIE Conference on Visual Communica- tions and Image Processing; 25 October 1998; San Jose, CA, USA. pp. 942-951.
  • [7] Lengwehasatit K, Ortega A. Probabilistic partial-distance fast matching algorithms for motion estimation. IEEE T Circ Syst Vid 2001; 11: 139-152.
  • [8] Wang CN, Yang SW, Liu CM, Chiang T. A hierarchical n-queen decimation lattice and hardware architecture for motion estimation. I IEEE T Circ Syst Vid 2004; 14: 429-440.
  • [9] Natarajan B, Bhaskaran V, Konstantinides K. Low-complexity block-based motion estimation via one-bit trans- forms. IEEE T Circ Syst Vid 1997; 7: 702-706.
  • [10] Erturk S. Multiplication-free one-bit transform for low-complexity block-based motion estimation. IEEE Signal Proc Let 2007; 14: 109-112.
  • [11] Erturk A, Erturk S. Two-bit transform for binary block motion estimation. IEEE T Circ Syst Vid 2005;15: 938-946.
  • [12] Urhan O, Erturk S. Constrained one-bit transform for low-complexity block motion estimation. IEEE T Circ Syst Vid 2007; 17: 478-482.
  • [13] Choi C, Jeong J. Enhanced two-bit transform based motion estimation via extension of matching criterion. IEEE T Consum Electr 2010; 56: 1883-1889.
  • [14] Lee S, Jeon G, Jeong G. Fast motion estimation based on enhanced constrained one-bit transform : Electron Lett 2014; 50: 746-748.
  • [15] Gullu MK. Weighted constrained one-bit transform based fast block motion estimation. IEEE T Consum Electr 2011; 57: 751-755.
  • [16] Ojala T, Pietikainen M, Harwood D. A comparative study of texture measures with classi?cation based on feature distributions. Pattern Recogn 1996; 29: 51-59.
  • [17] Liu L, Zhao L, Kuang G, Fieguth P. Extended local binary patterns for texture classi cation. Image Vision Comput 2012; 39: 86-99.
  • [18] Zhao XM, Zhang SQ. Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding. EURASIP J Adv Sig Pr 2012; 2012: 20.
  • [19] Verma R, Dabbagh MY. Binary pattern based edge detection for motion estimation in H.264/AVC. In: 26th IEEE Canadian Conference of Electrical and Computer Engineering; 2013. New York, NY, USA: IEEE. pp. 1-4.
  • [20] Kr B, Kurt M, Urhan O. Local binary pattern based fast digital image stabilization. IEEE Signal Proc Let 2015; 22: 341-345.
Turkish Journal of Electrical Engineering and Computer Sciences-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK
Sayıdaki Diğer Makaleler

Power search algorithm (PSA) for combined economic-emission dispatch problems considering valve point effects in economic load dispatch

Prakash ARUMUGAM, Ravichandran COIMBATORE SUBRAMANIAN

Optimal reactive power ow in BDFMs for converter cost reduction and efficiency improvement

Hamed GORGINPOUR, Behzad JANDAGHI, Mohammad Naser HASHEMNIA, Hashem ORAEE

Markovian model for reliability assessment of microgrids considering load transfer restriction

Mohammad ALMUHAINI, Abdulrahman AL-SAKKAF

A new video forgery detection approach based on forgery line

Güzin ULUTAŞ, Mustafa Hakan BOZKURT, Isılay BOZKURT

Method of singular integral equations in diffraction by semi-in nite grating: H -polarization case

Sergey POGARSKY, Mstislav KALIBERDA, Leonid LYTVYNENKO

A novel approach to solve transient stability constrained optimal power ow problems

Huy NGUYEN-DUC, Linh TRAN-HOAI, Dieu VO NGOC

ANN-based SHEPWM using a harmony search on a new multilevel inverter topology

Rachid TALEB, Fayçal CHABNI, M'hamed HELAIMI

Discovering the relationships between yarn and fabric properties using association rule mining

Tuba ALPYILDIZ, Pelin YILDIRIM, Derya BİRANT

Calculation of current limiting reactance of hybrid SFCL for low voltage ride-through capability enhancement in DFIG wind farms

Sillawat ROMPHOCHAI, Komsan HONGESOMBUT

Low-power voltage to a frequency-based smart temperature sensor with +0.8/{0.75 ◦ C accuracy for {55 ◦ C to 125 ◦ C

Krishna PRASAD, Mudasir BASHIR, Sreehari Rao PATRI