End-to-End Blind Quality Assessment of Compressed Videos Using Deep Neural Networks

Author:

Liu Wentao1,Duanmu Zhengfang1,Wang Zhou1

Affiliation:

1. University of Waterloo, Waterloo, ON, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

ACM

Reference43 articles.

1. J. Ballé V. Laparra and E. P. Simoncelli. 2015. Density modeling of images using a generalized normalization transformation. arXiv preprint arXiv:1511.06281 (2015) 1--14. J. Ballé V. Laparra and E. P. Simoncelli. 2015. Density modeling of images using a generalized normalization transformation. arXiv preprint arXiv:1511.06281 (2015) 1--14.

2. J. Ballé V. Laparra and E. P. Simoncelli. 2016. End-to-end optimized image compression. arXiv preprint arXiv:1611.01704 (2016) 1--27. J. Ballé V. Laparra and E. P. Simoncelli. 2016. End-to-end optimized image compression. arXiv preprint arXiv:1611.01704 (2016) 1--27.

3. Video super-resolution using controlled subpixel detector shifts

4. On the use of deep learning for blind image quality assessment

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