Fowlkes-Mallows Correlated Cohen Kappa Coefficient Block Matching based Multi-Layer Perceptron Classification for Motion Estimation in VLSI

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 2

Year of Publication : 2023

Authors : M. Sunitha, G. Mary Valantina

10.14445/23488379/IJEEE-V10I2P110

How to Cite?

M. Sunitha, G. Mary Valantina, "Fowlkes-Mallows Correlated Cohen Kappa Coefficient Block Matching based Multi-Layer Perceptron Classification for Motion Estimation in VLSI," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 2, pp. 102-109, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I2P110

Abstract:

Motion Estimation (ME) constitutes an essential process in video coding with lesser processing time. Many methods were designed for efficient motion estimation in the VLSI architecture. However, the consumption of power was not reduced through existing techniques. Fowlkes–Mallows Correlated Cohen Kappa Coefficient Block Matching-based Multi-Layer Perceptron Classifier (FMCCKCBM-MLPC) Model is introduced to handle such limitations. FMCCKCBM-MLPC Model is used for increasing the motion estimation of video series in the VLSI architecture circuits. Multi-Layer Percepton is used for examining the feature and performing classification with the multiple layers. Input is sent to the hidden layer 1. In hidden layer 1, the segmentation process is performed by Fowlkes–Mallows Regularized Principal Component Regression to reduce the time loss. In hidden layer 2, the block matching is carried out by Cohen Kappa Coefficient with the segmented blocks. With Cohen Kappa Coefficient, the repeated blocks are predicted with lesser loss in the video series. This helps to enhance motion estimation in the VLSI architecture. FMCCKCBM-MLPC Model is computed in terms of power and time consumption. The simulation result of the FMCCKCBM-MLPC Model minimizes the area, PSNR, delay and power consumption of motion estimation in the video series with existing techniques.

Keywords:

Motion estimation, Video sequence, Segmentation, Cohen kappa coefficient, Block matching.

References:

[1] Francis H. Shajin, P. Rajesh, and M. Ramkumar Raja, "An Efficient VLSI Architecture for Fast Motion Estimation Exploiting Zero Motion Prejudgment Technique and a New Quadrant-Based Search Algorithm in HEVC," Circuits, Systems, and Signal Processing, Springer, vol. 41, pp. 1751–1774, 2022. Crossref, https://doi.org/10.1007/s00034-021-01850-2
[2] Shujiao Jin, and Hu Jin, "Optimization of Motion Estimation Algorithm Based on FPGA Hardware System and Video Tracking," Microprocessors and Microsystems, Elsevier, vol. 82, p. 103867, 2021. Crossref, https://doi.org/10.1016/j.micpro.2021.103867
[3] Rahul Bhandari et al., "Developing New Search Pattern in Fast Motion Estimation Algorithm Used for Compress a Video," Materials Today: Proceedings, Elsevier, pp. 1-18, 2021. Crossref, https://doi.org/10.1016/j.matpr.2020.12.002
[4] Bin Fan, Yuchao Dai, and Ke Wang, "Rolling-Shutter-Stereo-Aware Motion Estimation and Image Correction," Computer Vision and Image Understanding, Elsevier, vol. 213, p. 103296, 2021. Crossref, https://doi.org/10.1016/j.cviu.2021.103296
[5] G. Liu et al., "Structural Motion Estimation via Hilbert Transform Enhanced Phase-Based Video Processing," Mechanical Systems and Signal Processing, Elsevier, vol. 166, p. 108418, 2022. Crossref, https://doi.org/10.1016/j.ymssp.2021.108418
[6] Junhao Wu, Xuan Yang, and Ziyu Gan, "Left Ventricle Motion Estimation for Cine MR Images Using Sparse Representation with Shape Constraint," Physica Medica, Elsevier, vol. 87, pp. 49-64, 2021. Crossref, https://doi.org/10.1016/j.ejmp.2021.05.026
[7] Lifang Wu et al., "Global Motion Estimation with Iterative Optimization-Based Independent Univariate Model for Action Recognition," Pattern Recognition, Elsevier, vol. 116, p. 107925, 2021. Crossref, https://doi.org/10.1016/j.patcog.2021.107925
[8] Yang Tian, "Optimization of Volleyball Motion Estimation Algorithm Based on Machine Vision and Wearable Devices," Microprocessors and Microsystems, Elsevier, vol. 81, pp. 1-12, 2021. Crossref, https://doi.org/10.1016/j.micpro.2020.103750
[9] Zhao Zhang, Junsheng Ren, and Weiwei Bai, "MIMO Non-Parametric Modeling of Ship Maneuvering Motion for Marine Simulator Using Adaptive Moment Estimation Locally Weighted Learning," Ocean Engineering, Elsevier, vol. 261, pp. 1-15, 2022. Crossref, https://doi.org/10.1016/j.oceaneng.2022.112103
[10] Xiaoang Xu et al., "Rotational Motion Estimation of Non-Cooperative Target in Space Based on the 3D Point Cloud Sequence," Advances in Space Research, vol. 69, no. 3, pp. 1528-1537, 2022. Crossref, https://doi.org/10.1016/j.asr.2021.10.054
[11] Clemens Seibold, Anna Hilsmann, and Peter Eisert, "Model-Based Motion Blur Estimation for the Improvement of Motion Tracking," Computer Vision and Image Understanding, Elsevier, vol. 160, pp. 45-56, 2017. Crossref, https://doi.org/10.1016/j.cviu.2017.03.005
[12] Qiaochu Zhao, Ittetsu Taniguchi, and Takao Onoye, "Novel Object Motion Estimation Method for Industrial Vision Systems in Aligning Machines," Journal of Industrial Information Integration, Elsevier, vol. 25, p. 100295, 2022. Crossref, https://doi.org/10.1016/j.jii.2021.100295
[13] Guillermo Botella, and Carlos Garcia, "Real-Time Motion Estimation for Image and Video Processing Applications," Journal of Real-Time Image Processing, Springer, vol. 11, pp. 625–631, 2016. Crossref, https://doi.org/10.1007/s11554-014-0478-y
[14] Renaud Morin et al., "Motion Estimation-Based Image Enhancement in Ultrasound Imaging," Ultrasonics, Elsevier, vol. 60, pp. 19-26, 2015. Crossref, https://doi.org/10.1016/j.ultras.2015.02.003
[15] Suvendra Kumar Jayasingh, Jibendu Kumar Mantri, and P. Gahan, "Comparison between J48 Decision Tree, SVM and MLP in Weather Forecasting," SSRG International Journal of Computer Science and Engineering, vol. 3, no. 11, pp. 39-44, 2016. Crossref, https://doi.org/10.14445/23488387/IJCSE-V3I11P109
[16] Kun Wang, Yufeng Zhang, and Zhiyao Li, "Motion Estimation of the Common Carotid Artery Wall in Ultrasound Images Using an Improved Sub-Pixel Block Matching Method," Optik, Elsevier, vol. 270, pp. 1-15, 2022. Crossref, https://doi.org/10.1016/j.ijleo.2022.169929
[17] G. Senbagavalli, and R. Manjunath, "Motion Estimation Using Variable Size Block Matching with Cross Square Search Pattern," SN Applied Sciences, Springer, vol. 2, p. 1459, 2020. Crossref, https://doi.org/10.1007/s42452-020-03248-2
[18] Shengze Cai et al., "Dense Motion Estimation of Particle Images via a Convolutional Neural Network," Experiments in Fluids, vol. 60, no. 73, pp. 1-15, 2019. Crossref, https://doi.org/10.1007/s00348-019-2717-2
[19] Sachin Chaudhary et al., "Motion Estimation in Hazy Videos," Pattern Recognition Letters, Elsevier, vol. 150, pp. 130-138, 2021. Crossref, https://doi.org/10.1016/j.patrec.2021.06.029
[20] Sagi Monin, Evgeny Hahamovich, and Amir Rosenthal, "Single-Pixel Imaging of Dynamic Objects Using Multi-Frame Motion Estimation," Scientific Reports, vol. 11, p. 7712, 2021. Crossref, https://doi.org/10.1038/s41598-021-83810-z
[21] Farhana Wani, Adeel Ahmed Khan, and Dr. S Basavaraj Patil, "A Survey of Image Processing Techniques for Identification and Tracking of Objects from a Video Sequence," International Journal of Computer & organization Trends, vol. 4, no. 2, pp. 24-29, 2014. Crossref, https://doi.org/10.14445/22492593/IJCOT-V6P306
[22] Paramkusam A.V et al., "All Directional Search Motion Estimation Algorithm," Electronics, vol. 11, no. 22, p. 3736, 2022. Crossref, https://doi.org/10.3390/electronics11223736
[23] M. Maheswari, M. S. Josephine, and V. Jeyabalaraja, "YOLO Architecture-based Object Detection for Optimizing Performance in Video Streams," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 187-196, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P220
[24] Pavel Arnaudov, and Tokunbo Ogunfunmi, "Artificially Intelligent Adaptive Search Fast Motion Estimation Algorithm for HD Video," Journal of Signal Processing Systems, vol. 92, pp. 389-408, 2020. Crossref, https://doi.org/10.1007/s11265-019-01466-5
[25] Djoudi Kerfa, and AbdelKader Saidane, "An Efficient Algorithm for Fast Block Matching Motion Estimation Using an Adaptive Threshold Scheme," Multimedia Tools and Applications, vol. 79, pp. 24173–2418, 2020. Crossref, https://doi.org/10.1007/s11042-020-09040-z
[26] L .Bowns, "Motion Estimation: A Biologically Inspired Model," Vision Research, Elsevier, vol. 150, pp. 44-53, 2018. Crossref, https://doi.org/10.1016/j.visres.2018.07.003
[27] Niras C. Vayalil, Manoranjan Paul, and Yinan Kong, "Residue Number System Hardware Design of Fast-Search Variable-Motion-Estimation Acceleratorfor HEVC/H.265," IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 2, pp. 572 – 581, 2019. Crossref, https://doi.org/10.1109/TCSVT.2017.2787194
[28] Jui-Hung et al., "ML-Assisted DVFS-Aware HEVC Motion Estimation Design Scheme for Mobile APSoC," IEEE Systems Journal, vol. 13, no. 4, pp. 4464-4473, 2019. Crossref, https://doi.org/10.1109/JSYST.2018.2885538