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Machine learning approaches with HEVC intra prediction on CU partition for complexity reduction

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Abstract

In the current context, transmitting videos and the subsequent maintaining of a repository have become vital; hence, there is a greater need for compression. High Efficiency Video Coding (HEVC) is a video compression standard and is more efficient than all counterparts. Coding unit (CU) blocks while encoding the video with intra prediction causes a great deal of complexity. To obtain efficient CU split, the support vector machine (SVM) with grey level co-occurrence matrix (GLCM) based textural feature extraction, is used for binary classification. Classifier decides the split or no_split of CU blocks early and based on the results from the classifier, the depth level for the CU partition is determined. The experiments are done using the dataset which created using standard test sequences of different resolutions. The metrics are tabulated with the experimental results compared with the HEVC compression model. The proposed algorithm using SVM with GLCM feature extraction gives an average of 59.11% reduction in time complexity without compromising the quality of videos compared to other prevailing algorithms.

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References

  1. Bakheet S, Al-hamadi A (2020) Automatic detection of COVID-19 using pruned GLCM-Based texture features and LDCRF classification. Comput Biol Med 137(October):104781

  2. Bossen F (2012) Common test conditions and software reference configurations. JCTVC of ISO/IEC and ITU-T, JCTVC-J1100, Stockholm, SE

  3. Bouaafia S, Khemiri R, Sayadi FE, Atri M (2020) Fast CU partition-based machine learning approach for reducing HEVC complexity. J Real-Time Image Process 17(1):185–196. https://doi.org/10.1007/s11554-019-00936-0

    Article  Google Scholar 

  4. Du B, Siu WC, Yang X (2015) Fast CU partition strategy for HEVC intra-frame coding using learning approach via random forests. Asia-Pacific Signal Inf. Process. Assoc. Annu. Summit Conf. APSIPA ASC 2015, no. 0, pp 1085–1090. https://doi.org/10.1109/APSIPA.2015.7415439

  5. FFmpeg. [Online] Available: https://ffmpeg.org/. Accessed from 2021

  6. Grellert M, Zatt B, Bampi S, Da Silva Cruz LA (2019) Fast coding unit partition decision for HEVC using support vector machines. IEEE Trans Circuits Syst Video Technol 29(6):1741–1753. https://doi.org/10.1109/TCSVT.2018.2849941

    Article  Google Scholar 

  7. Ha JM, Bae JH, Sunwoo MH (2016) Texture-based fast CU size decision algorithm for HEVC intra coding. 2016 IEEE Asia Pacific Conf. Circuits Syst. APCCAS pp 702–705. https://doi.org/10.1109/APCCAS.2016.7804070

  8. Haralick IDRM (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC–3:610–621 [Online]. https://doi.org/10.1109/TSMC.1973.4309314 

  9. Hassan M, Shanableh T (2019) Predicting split decisions of coding units in HEVC video compression using machine learning techniques. Multimed Tools Appl 78(23):32735–32754. https://doi.org/10.1007/s11042-018-6882-8

  10. Hsu HY, Huang SE, Lin Y (2017) Computational complexity reduction for HEVC intra prediction with SVM. 2017 IEEE 6th Glob. Conf. Consum. Electron. GCCE, vol 2017-Janua, no. Gcce, pp 1–2. https://doi.org/10.1109/GCCE.2017.8229195

  11. Huang Y, Wang D, Sun Y, Hang B (2020) A fast intra coding algorithm for HEVC by jointly utilizing naive bayesian and SVM. Multimed Tools Appl 79:45–46. https://doi.org/10.1007/s11042-020-08882-x

    Article  Google Scholar 

  12. JCT_VT test sequences. [Online] Available: https://media.xiph.org/video/derf/. Accessed from 2020

  13. Kim IK, Min J, Lee T, Han WJ, Park JH (2012) Block partitioning structure in the HEVC standard. IEEE Trans Circuits Syst Video Technol 22(12):1697–1706. https://doi.org/10.1109/TCSVT.2012.2223011

    Article  Google Scholar 

  14. Lainema J, Bossen F, Han WJ, Min J, Ugur K (2012) Intra coding of the HEVC standard. IEEE Trans Circuits Syst Video Technol 22(12):1792–1801. https://doi.org/10.1109/TCSVT.2012.2221525

    Article  Google Scholar 

  15. Lee D, Jeong J (2017) Fast CU size decision algorithm using machine learning for HEVC intra coding. Signal Process Image Commun 62(June):33–41. https://doi.org/10.1016/j.image.2017.12.005

  16. Lin YC, Lai JC (2014) Edge density early termination algorithm for HEVC coding tree block. In: Proceedings – 2014 International Symposium on Computer, Consumer and Control, IS3C 2014, pp 39–42. https://doi.org/10.1109/IS3C.2014.23

  17. Liu X, Liu Y, Wang P, Lai CF, Chao HC (2017) An adaptive mode decision algorithm based on video texture characteristics for HEVC intra prediction. IEEE Trans Circuits Syst Video Technol 27(8):1737–1748. https://doi.org/10.1109/TCSVT.2016.2556278

    Article  Google Scholar 

  18. Maazouz M, Batel N, Bahri N, Masmoudi N (2019) Homogeneity-based fast CU partitioning algorithm for HEVC intra coding. Eng Sci Technol 22:706–714

    Google Scholar 

  19. Manual, HM software. [Online]. Available: https://hevc.hhi.fraunhofer.de/. Accessed from 2020

  20. McCann K, Han, WJ, Kim I-K, Min J-H, Alshina E, Alshin A et al (2010) Samsung’s Response to the Call for Proposals on Video Compression Technology, JCTVC of ISO/IEC and ITU-T, JCTVC-A124, 1st Meeting: Dresden, DE

  21. Nair PS, Rao KR, Nair MS (2019) A machine learning approach for fast mode decision in HEVC intra prediction based on statistical features. J Intell Fuzzy Syst 36(3):2095–2106. https://doi.org/10.3233/JIFS-169921

    Article  Google Scholar 

  22. Özcan C, Ersoy O, Oğul İÜ (2020) Fast texture classification of denoised SAR image patches using GLCM on spark. Turkish J Electr Eng Comput Sci 28(1):182–195. https://doi.org/10.3906/elk-1904-7

    Article  Google Scholar 

  23. Pourazad MT, Doutre C, Azimi M, Nasiopoulos P (2012) The new gold standard for video compression: how does HEVC compare with H.264/AVC? IEEE Consum Electron Mag 1(3):36–46. https://doi.org/10.1109/MCE.2012.2192754

    Article  Google Scholar 

  24. Radosavljevic M, Georgakarakos G, Lafond S, Vukobratovic D (2015) Fast coding unit selection based on local texture characteristics for HEVC intra frame. 2015 IEEE Glob. Conf. Signal Inf. Process. Glob, pp 1377–1381. https://doi.org/10.1109/GlobalSIP.2015.7418424

  25. Ramasamy U, K P (2018) SVM classification of brain images from MRI scans using morphological transformation and GLCM texture features. Int J Comput Syst Eng 5(1). https://doi.org/10.1504/ijcsyse.2018.10011250

  26. Ruiz-Coll D, Adzic V, Fernández-Escribano G, Kalva H, Martínez JL, Cuenca P (2014) Fast partitioning algorithm for HEVC Intra frame coding using machine learning. 2014 IEEE Int. Conf. Image Process. ICIP, pp 4112–4116. https://doi.org/10.1109/ICIP.2014.7025835

  27. Sara U, Akter M, Uddin MS (2019) Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. J Comput Commun 7(3):8–18

    Article  Google Scholar 

  28. Schiopu I, Huang H, Munteanu A (2020) CNN-based intra-prediction for lossless HEVC. IEEE Trans Circuits Syst Video Technol 30(7):1816–1828. https://doi.org/10.1109/TCSVT.2019.2940092

    Article  Google Scholar 

  29. Shi W, Jiang X, Song T, Shimamoto T (2015) Edge information based fast selection algorithm for intra prediction of HEVC. IEEE Asia-Pacific Conf. Circuits Syst. Proceedings, APCCAS, no. February, pp 17–20. https://doi.org/10.1109/APCCAS.2014.7032708

  30. Sullivan WT, Ohm GJ, Woo-Jin J-RH (2012) Overview of the high efficiency video coding (HEVC) standard. IEEE Trans Circuits Syst Video Technol 22(12):1649–1668

    Article  Google Scholar 

  31. Sun C, Fan X, Zhao D (2018) A fast intra cu size decision algorithm based on canny operator and classifier SVM. Proc. - Int. Conf. Image Process. ICIP, pp 1787–1791. https://doi.org/10.1109/ICIP.2018.8451178

  32. Tao P, Yi H, Wei C, Ge LY, Xu L (2013) A method based on weighted F-score and SVM for feature selection. 2013 25th Chinese Control Decis. Conf. CCDC 2013, no. 3, pp 4287–4290. https://doi.org/10.1109/CCDC.2013.6561705

  33. Tian R, Zhang Y, Duan M, Li X (2019) Adaptive intra mode decision for HEVC based on texture characteristics and multiple reference lines. Multimed Tools Appl 78(1):289–310. https://doi.org/10.1007/s11042-018-6001-x

    Article  Google Scholar 

  34. Yin J, Chen Y, Yang X, Fang R, Lin J (2018) A fast block partitioning algorithm based on SVM for HEVC intra coding. ACM Int. Conf. Proceeding Ser., no. 1199, pp 176–181. https://doi.org/10.1145/3301506.3301527

  35. Zhang Y, Wang G, Tian R, Xu M, Kuo CCJ (2019) “Texture-Classification Accelerated CNN Scheme for Fast Intra CU Partition in HEVC,” Data Compression Conf. Proc., vol. 2019-March, no. April, pp.241–249, doi: https://doi.org/10.1109/DCC.2019.00032

  36. Zhu L, Zhang Y, Kwong S, Wang X, Zhao T (2018) Fuzzy SVM-based coding unit decision in HEVC. IEEE Trans Broadcast 64(3):681–694. https://doi.org/10.1109/TBC.2017.2762470

    Article  Google Scholar 

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Acknowledgements

We would like to thank Department of Computer Science, University of Madras for providing us valuable help and support.

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  1. Department of Computer Science, University of Madras, Chennai, Tamil Nadu, India

    Chithra Palaniappan & Roselin Clara Angel

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  1. Chithra Palaniappan
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Palaniappan, C., Angel, R.C. Machine learning approaches with HEVC intra prediction on CU partition for complexity reduction. Multimed Tools Appl 82, 45127–45143 (2023). https://doi.org/10.1007/s11042-023-15259-3

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