Compression artifact reduction of low bit-rate videos via deep neural networks using self-similarity prior

[1]  Yanting Hu,et al.  Image Super-Resolution With Self-Similarity Prior Guided Network and Sample-Discriminating Learning , 2022, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Nam Ik Cho,et al.  A Pseudo-Blind Convolutional Neural Network for the Reduction of Compression Artifacts , 2020, IEEE Transactions on Circuits and Systems for Video Technology.

[3]  Tie Liu,et al.  MFQE 2.0: A New Approach for Multi-Frame Quality Enhancement on Compressed Video , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Nam Ik Cho,et al.  Reduction of Video Compression Artifacts Based on Deep Temporal Networks , 2018, IEEE Access.

[5]  Jie Liu,et al.  Sparse representation and adaptive mixed samples regression for single image super-resolution , 2018, Signal Process. Image Commun..

[6]  Jan Kautz,et al.  Loss Functions for Image Restoration With Neural Networks , 2017, IEEE Transactions on Computational Imaging.

[7]  Chih-Yuan Yang,et al.  Learning a No-Reference Quality Metric for Single-Image Super-Resolution , 2016, Comput. Vis. Image Underst..

[8]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[9]  Thomas S. Huang,et al.  Learning Super-Resolution Jointly From External and Internal Examples , 2015, IEEE Transactions on Image Processing.

[10]  Shu-Jhen Fan-Jiang,et al.  Self-learning-based post-processing for image/video deblocking via sparse representation , 2014, J. Vis. Commun. Image Represent..

[11]  Michael K. Ng,et al.  Reducing Artifacts in JPEG Decompression Via a Learned Dictionary , 2014, IEEE Transactions on Signal Processing.

[12]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[13]  Karen O. Egiazarian,et al.  Video Denoising, Deblocking, and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms , 2012, IEEE Transactions on Image Processing.

[14]  Licheng Jiao,et al.  Image deblocking via sparse representation , 2012, Signal Process. Image Commun..

[15]  Liang-Tien Chia,et al.  Study on the distribution of DCT residues and its application to R-D analysis of video coding , 2008, J. Vis. Commun. Image Represent..

[16]  Deqing Sun,et al.  Postprocessing of Low Bit-Rate Block DCT Coded Images Based on a Fields of Experts Prior , 2007, IEEE Transactions on Image Processing.

[17]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[18]  Joseph W. Goodman,et al.  A mathematical analysis of the DCT coefficient distributions for images , 2000, IEEE Trans. Image Process..

[19]  King Ngi Ngan,et al.  Reduction of blocking artifacts in image and video coding , 1999, IEEE Trans. Circuits Syst. Video Technol..

[20]  Zhenwei Shi,et al.  Hybrid-Scale Self-Similarity Exploitation for Remote Sensing Image Super-Resolution , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Xun Wang,et al.  Neural Network-Based Video Compression Artifact Reduction Using Temporal Correlation and Sparsity Prior Predictions , 2020, IEEE Access.

[22]  Jian Sun,et al.  BM3D-Net: A Convolutional Neural Network for Transform-Domain Collaborative Filtering , 2018, IEEE Signal Processing Letters.

[23]  Kristian Bredies,et al.  A Total Variation-Based JPEG Decompression Model , 2012, SIAM J. Imaging Sci..

[24]  Michael Elad,et al.  Generalizing the Nonlocal-Means to Super-Resolution Reconstruction , 2009, IEEE Transactions on Image Processing.

[25]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..