| 摘要(英) |
In the coding structure of HEVC, compared to previous image compression standards, the size of Coding Tree Units (CTU) has been reduced from a maximum of 64x64 to 8x8, lowering the bit rate of encoding units but increasing the computational time cost. Therefore, in this study, a Distributed Video Coding architecture based on CNN (Convolutional Neural Networks), is proposed for both the encoder and decoder of HEVC. The goal is to simplify the complexity of encoding process and enhance image quality during post-processing at the decoder.
In the encoder, optimization is applied to the SVM-CNN CU/PU algorithm by replacing the original interpolation filter with a bilinear interpolation filter to reduce computational load and save encoding time. However, simplifying fractional point estimation leads to image distortion. Hence, CNN is utilized for post-processing to improve the image, resulting in a reduction of BDBR% to 0.43% and an increase in TS% to 74.42%.
In the decoder, DenseNet/DAE three-channel CNN models are introduced to enhance decoded images, achieving a decrease in BDBR% to -5.96%.
In Chapter Four, we explore how to adjust the thresholds in the encoding algorithm to optimize the time-saving rate under the constraint of image quality improvement. By proportionally adjusting the thresholds for discriminative features such as SAD and RDO, we obtain spatial distributions for ΦSAD and ΦRDO concerning BDBR% and TS%. For more accurate predictions, experiments are conducted around BDBR% = 6.0%, resulting in relational equations between ΦSAD, ΦRDO, ZBDBR, and ZTS. We calculate the predicted optimal time-saving rate based on these equations. Finally, predictions are made based on the relationship curves between BDBR% and TS%. The results show an acceptable margin of error between predictions and experimental outcomes. Therefore, adjusting thresholds to optimize encoding calculations and predict BDBR% and TS% performance can further enhance overall efficiency in the future. |
| 參考文獻 |
[1]“Video coding for low bit rate communication, version 1,” ITU-T recommendation H.263, 1995.
[2] I. E. G. Richardson, H.264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia. Aberdeen, U.K.: John Wiley & Sons,2003.
[3] Gary J. Sullivan, Jens-Rainer Ohm, Woo-Jin Han and Thomas Wiegand “Overview of the high efficiency video coding (HEVC) Standard,” in Proc. IEEE Transactions on circuits and systems for video technology,vol. 22, no. 12, pp. 1649-1668, Dec. 2012.
[4] L.Zhao, X. Guo, S. Lei, S. Ma and D. Zhao, “Simplified AMVP for high efficiency video coding,” in Proc. IEEE ICIP, pp. 1-4, 27-30 Nov.2012.
[5] Y. Ismail and S. El-etriby, “Fast diamond search algorithm for real time video coding,” in Proc. IEEE ICNC, pp. 729-733, Feb. 2012.
[6] J.K. Liu, “Efficient HEVC inter prediction using SVM,” Department of Communication Engineering National Central University, Taiwan 32054, R.O.C..
[7] D.H Yang “ SVM/CNN-based CTU Partition for HEVC Inter Prediction” Department of Communication Engineering National Central University,Taiwan 32054, R.O.C., Jan 2021.
[8] C.H Chen,“CNN-Based Post-Processing for HEVC Intra Prediction,”Department of Communication Engineering National Central University, Taiwan 32054, R.O.C., July 2020.
[9] P.H Tsui, “Post-Processing for HEVC Intra Prediction with ResNet algorithm” Department of Communication Engineering National CentralUniversity, Taiwan 32054, R.O.C., January 2022
[10] R. Puri and K. Ramchandran, “PRISM: A new robust video coding architecture based on distributed compression principles,”in Proceedings of the Allerton Conference on Communication, Control an d Computing, Allerton, IL, Oct. 2002.
[11] A. Aaron, R. Zhang, and B. Girod, “Wyner-Ziv Coding for Motion Video,” Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, Nov. 2002.
[12] D. Slepian and J.K. Wolf, “Noiseless coding of correlated information sources,” IEEE Transactions on Information Theory, Vol. IT-19, July 1973, pp. 471–480.
[13] Wyner and J. Ziv, “The Rate-Distortion Function for Source Coding with Side Information at the Decoder”. IEEE Transactions on Information Theory, Vol. IT-22, Jan. 1976, pp. 1–10.
[14] T.Y Wei,“Efficient CU and PU Partitions for HEVC Inter Prediction”, Department of Communication Engineering National Central University,Taiwan 32054, R.O.C., January 2023.
[15] C,M Hang, “CNN-based HEVC interpolation filters”, Department of Communication Engineering National Central University, Taiwan 32054, R.O.C., July 2020.
[16] J. Kim, J.K. Lee, K.M. Lee, “Accurate Image Super-Resolution Using Very Deep Convolutional Networks”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1646-1654.
[17] Y.C Chen,“Post-Processing for HEVC and VVC Intra Prediction With DenseNet / Denoising Autoencoder Algorithms”, Department of Communication Engineering National Central University,Taiwan 32054, R.O.C., January 2023.
[18] Y.D Tsai, “Research on Fast HEVC Inter Prediction Coding” National Central University, National Central University, Taiwan 32054, R.O.C., Jan 2019.
[19] S.J Cai, “Reduction of computation complexity for HEVC intra prediction with support vector machine,” National Central University, Master Thesis, Jun 2017. |
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