| 摘要(英) |
Nowadays, technology is developing rapidly, people′s lives are closely related to technology, and high resolution images have become the daily needs of most people. For example, people want to pursue higher image quality when watching movies or playing video games. However, behind the quality of these high resolution images, the amount of data consumed is undoubtedly huge. In order to respond to these high-resolution images more effectively, both HEVC and VVC use many ways to effectively reduce transmission bit.
HEVC(High Efficiency Video Coding) adopts a quadtree encoding partition structure, which will cause image distortion at the encoding end. Therefore, this paper uses three post-processing methods of intra-screen prediction to enhance image quality. The first method uses Add Fusion instead of Concatenate Fusion and compare the differences. The second is to reconstruct the image better through the DenseNet architecture. The third is to add the Denoising Autoencoder at the end of the model to improve the overall performance. In the end, the post-processing for HEVC intra prediction can improve the BDPSNR by 0.35 (dB) and reduce the BDBR by 6.37 (%).
Compared with HEVC, VVC(Versatile Video Coding) not only adopts the quad-tree division method, but also has a multi-type (Multi-Type) encoding division architecture. It can support 4K~16K high resolution images, to provide better experiences of media consuming. This paper also uses the model architecture mentioned in the previous paragraph to improve and enhance the image in VVC. Final, the post-processing for VVC intra prediction can improve the BDPSNR by 0.288 (dB) and reduce the BDBR by 5.25 (%).
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| 參考文獻 |
[1] ITU-T Rec. H.263,“Video Codec for Low Bit Rate Communication, ”1996.
[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] “Generic coding of moving pictures and associated audio information,” ISO/IEC 13818-2: Video (MPEG-2),May 1996.
[4] “Coding of audio-visual objects - Part 2: Visual,” in ISO/IEC 14496-2 (MPEG-4 Visual Version 1),Apr.1999.
[5] G. J. Sullivan, J.R. Ohm, W.J. Han and T. Wiegand,” Overview of the High Efficiency Video Coding (HEVC) Standard,” IEEE Trans. CSVT, vol. 22, no. 12, Dec. 2012.
[6] 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.
[7] G. Huang, Z Liu, Laurens van der Maaten,and Laurens van der Maaten, “Densely Connected Convolutional Networks”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 47004708, 2017.
[8] P. Vincent, H. Larochelle, Y. Bengio, and P. Manzagol, "Extracting and composing robust features with denoising autoencoders", in Proceedings of the 25th International Conference on Machine Learning (ICML-08) 2008.
[9] X.He, Q.Hu, X.Han, X.Zhang, C.Zhang, W.Lin, "Enhancing HEVC Compressed Videos With A Partition-Masked Convolutional Neural Network", International Conference on Image Processing(ICIP) 2018, pp.216-220.
[10] 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.
[11] P.H Tsui, “Post-Processing for HEVC Intra Prediction with ResNet algorithm” Department of Communication Engineering National Central University, Taiwan 32054, R.O.C., January 2022.
[12] C.K Hsieh,“CNN-Based Post-Processing for HEVC Inter Prediction,” Department of Communication Engineering National Central University, Taiwan 32054, R.O.C., July 2020.
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