تخمین حرکت مبتنی بر سرعت و جهت در فشرده‌سازی ویدیو برای کاهش محاسبات و افزایش کیفیت ویدیو

الموضوعات :

دادور حسینی اواشانق ¹ , مهدی نوشیار ² , سعید برغندان ³ , مجید قندچی ⁴

1 - گروه مهندسی برق، دانشگاه آزاد اسلامی، واحد اهر، اهر، ایران
2 - گروه مهندسی برق و کامپیوتر، دانشگاه محقق اردبیلی، اردبیل، ایران
3 - گروه مهندسی برق، دانشگاه آزاد اسلامی، واحد اهر، اهر، ایران
4 - گروه مهندسی برق، دانشگاه آزاد اسلامی، واحد اهر، اهر، ایران

تاريخ الإرسال : 25 الخميس , صفر, 1446 تاريخ التأكيد : 11 الإثنين , ربيع الثاني, 1446 تاريخ الإصدار : 20 الأحد , ربيع الثاني, 1447

الکلمات المفتاحية: پیکسل, تخمین حرکت, فشرده‌سازی, کدینگ ویدیو, HEVC.,

ملخص المقالة :

کدگذاری ویدیویی با کارایی بالا (HEVC) آخرین استاندارد کدگذاری ویدیویی است. این استاندارد با ارائه دو برابر راندمان فشرده‌سازی در مقایسه با استاندارد H.264، از فیلم‌های با وضوح بالا (HD) پشتیبانی می‌کند. محاسبات تخمین حرکت در HEVC پیچیده‌ترین قسمت از کدینگ ویدیو است که بیش‌ترین زمان کدینگ ویدیو را به این پردازش اختصاص می‌دهد. روش‌های زیادی برای کاهش زمان تخمین حرکت پیشنهاد شده که بسیاری از آن‌ها به‌صورت عملی مؤثر بوده است. با وجود به‌کارگیری الگوریتم‌های مؤثر در تخمین حرکت باز هم مقدار زمان پردازش نسبت به زمان آنی (real time) بسیار زیاد است. در این تحقیق یک الگوریتم تخمین حرکت زیر پیکسل سریع با تعداد نقاط جستجوی کمتر برای کدینگ ویدیو پیشنهاد شده است. روش پیشنهادی بر پایه مدلی از فیزیک حرکت و ویژگی‌های تصویر در قاب‌های متوالی است که از اطلاعات آماری حرکت عناصر قاب‌های دنباله‌‌ی ویدیویی استفاده کرده است. این الگوریتم با کاهش تعداد نقاط جستجو و بهبود نسبی پارامترهای کیفی ویدیو، پیچیدگی محاسبات را کاهش می‌دهد.

highlights:

• Reducing the number of search points in video motion estimation.
• Reducing the complexity of motion estimation calculations.
• Increasing the speed of motion estimation calculations.
• Increasing the quality of the final image and video: This increase in quality in terms of PSNR-HVS and PEVQ-MOS made the image better from the point of view of human vision.

 

 Citation: D. Hosseini Avashanagh, M. Nooshyar, S. Barghandan,  and M. Ghandchi, “Velocity and Direction-Based Motion Estimation in Video Compression to Reduce Computation and Increase Video Quality,” Journal of Southern Communication Engineering, vol. 15, no. 57, pp. 28-44 , 2025, doi:10.30495/jce.2025.1993480.1340  [in Persian].

المصادر:

[1] Y. Zhang, Ch. Zhang, R. Fan, S. Ma, Zh. Chen, and C. C. J. Kuo, “Recent advances on HEVC inter-frame coding: From optimization to implementation and beyond,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 11, pp. 4321-4339, November 2020, doi: 10.1109/TCSVT.2019.2954474.
[2] S. Gogoi and R. Peesapati, “Design and implementation of an efficient multi-pattern motion estimation search algorithm for HEVC/H.265,” IEEE Transactions on Consumer Electronics, vol. 67, no. 4, pp. 319-328, November 2021, doi: 10.1109/TCE.2021.3126670.
[3] W. Bao, W. Sh. Lai, X. Zhang, and Zh. Gao, “MEMC-Net: Motion estimation and motion compensation driven neural network for video interpolation and enhancement,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, pp. 1-16, March 2021, doi: 10.48550/arXiv.1810.08768.
[4] T. Koga, K. Iinuma, A. Hirano, Y. Iijima, and T. Ishiguro, “Motion compensated interframe coding for video conferencing,” National Telecommunication Conference, New Orleans, LA, 1981, pp. 531-535.
[5] R. Li, B. Zeng and M. L. Liou, “A new three-step search algorithm for block motion estimation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 4, no. 4, pp. 438-442, August 1994, doi: 10.1109/76.313138.
[6] L. M. Po and W. C. Ma, “A novel four-step search algorithm for fast block motion estimation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 6, no. 3, pp. 313-317, June 1996, doi: 10.1109/76.499840.
[7] J. Y. Tham, S. Ranganath, M. Ranganath, and A. A. Kassim, “A novel unrestricted center-biased diamond search algorithm for block motion estimation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 8, no. 4, pp. 369-377, August 1998, doi: 10.1109/76.709403.
[8] C. Zhu, X. Lin, and L.-P. Chau, “Hexagon-based search pattern for fast block motion estimation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 12, no. 5, pp. 349-355, May 2002, doi: 10.1109/TCSVT.2002.1003474.
[9] K. C. R. C. Varma and S. Mahapatra, “Complexity reduction of test zonal search for fast motion estimation in uni-prediction of high efficiency video coding,” Journal of Real-Time Image Processing, vol. 18, no. 3, pp. 511-524, June 2021, doi: 10.1007/s11554-020-00983-y.
[10] O. Ndili and T. Ogunfunmi, “Fast algorithm and efficient architecture for integer and fractional motion estimation,” Journal of Signal Processing Systems, vol. 75, no. 1, pp. 55-64, June 2014, doi: 10.1007/s11265-013-0793-8.
[11] Y. Nie and K. K. Ma, “Adaptive rood pattern search for fast blockmatching motion estimation,” IEEE Transaction on Image Processing, vol. 11, no. 12, pp. 1442-1449, December 2002, doi: 10.1109/TIP.2002.806251.
[12] N. Purnachand, L. N. Alves, and A. Navarro, "Fast Motion Estimation Algorithm for HEVC," IEEE Second International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, Germany, 2012, pp. 34-37, doi: 10.1109/ICCE-Berlin.2012.6336494.
[13] K. Singh and S. R. Ahamed, “Computationally efficient motion estimation algorithm for HEVC,” Journal of Signal Processing Systems, vol. 90, no. 12, pp. 1713-1727, December 2018, doi: 10.1007/s11265-017-1321-z.
[14] N. Kim and J. W. Kang, “Dynamic motion estimation and evolution video prediction network,” IEEE Transactions on Multimedia, Vol. 23, PP. 3986-3998, November 2020, doi: 10.1109/TMM.2020.3035281.
[15] S. Gogoi and R. Peesapati, “A hybrid motion estimation search algorithm for HEVC/H.265,” IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS), India, Rourkela, 2019, pp. 129-132, doi: 10.1109/iSES47678.2019.00037.
[16] Y. S. Ho, “Advanced video coding for next-generation multimedia services,” Published by InTech Janeza Trdine 9, Croatia. 2012, pp. 1-214.
[17] E. P. Simoncelli and D. J. Heeger, “A model of neuronal responses in visual area MT,” Vision Research, vol. 38, no. 5, pp. 743-761, March 1998, doi: 10.1016/S0042-6989(97)00183-1.
[18] S. K. Chatterjee, S. K. Vittapu, and S. Kundu, “Prediction-biased diamond search algorithm: a new approach to reduce motion estimation complexity,” Microsystem Technologies, vol. 27, pp. 2027-2032, January 2021, doi: 10.1007/s00542-020-05167-z.
[19] Sh. Agha, M. Khan, and F. Jan, “Efficient fast motion estimation algorithm for real time applications,” Journal of Real-Time Image Processing, vol. 19, pp. 403-413, January 2022, doi: 10.1007/s11554-021-01188-7.
[20] B. Bross, J. Chen, J. -R. Ohm, G. J. Sullivan, and Y. -K. Wang, "Developments in International Video Coding Standardization After AVC, With an Overview of Versatile Video Coding (VVC)," in Proceedings of the IEEE, vol. 109, no. 9, pp. 1463-1493, Sept. 2021, doi: 10.1109/JPROC.2020.3043399.
[21] G. J. Sullivan, J. -R. Ohm, W. -J. Han, and T. Wiegand, "Overview of the High Efficiency Video Coding (HEVC) Standard," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 12, pp. 1649-1668, Dec. 2012, doi: 10.1109/TCSVT.2012.2221191.
[22] R. Yang, F. Mentzer, L. Van Gool, and R. Timofte, "Learning for Video Compression With Recurrent Auto-Encoder and Recurrent Probability Model," in IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 2, pp. 388-401, Feb. 2021, doi: 10.1109/JSTSP.2020.3043590.
[23] J. Lin, D. Liu, H. Li, and F. Wu, "M-LVC: Multiple Frames Prediction for Learned Video Compression," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 3543-3551, doi: 10.1109/CVPR42600.2020.00360.
[24] G. Lu, X. Zhang, W. Ouyang, L. Chen, Z. Gao, and D. Xu, "An End-to-End Learning Framework for Video Compression," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 10, pp. 3292-3308, 1 Oct. 2021, doi: 10.1109/TPAMI.2020.2988453.
[25] J. Li, B. Li, and Y. Lu, “Deep contextual video compression,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 34, 2021, pp. 18114–18125.
[26] Y.-H. Ho, C.-P. Chang, P.-Y. Chen, A. Gnutti, and W.-H. Peng, “CANFVC: Conditional augmented normalizing flows for video compression,” in Proc. 17th Eur. Conf. Comput. Vis. Tel Aviv, Israel: Springer, 2022, pp. 207–223.
[27] X. Sheng, J. Li, B. Li, L. Li, D. Liu, and Y. Lu, "Temporal Context Mining for Learned Video Compression," in IEEE Transactions on Multimedia, vol. 25, pp. 7311-7322, 2023, doi: 10.1109/TMM.2022.3220421.
[28] J. Li, B. Li and Y. Lu, “Hybrid spatial–temporal entropy modelling for neural video compression,” in Proc. 30th ACM Int. Conf. Multimedia, pp. 1503–1511, 2022, doi: 10.1145/3503161.3547845.
[29] X. Sheng, L. Li, D. Liu, and H. Li, "Spatial Decomposition and Temporal Fusion Based Inter Prediction for Learned Video Compression," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 7, pp. 6460-6473, July 2024, doi: 10.1109/TCSVT.2024.3360248.