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A quality enhancement framework with noise distribution characteristics for high efficiency video coding

  • Published:2020-10 Issue: Volume:411 Page:428-441
  • ISSN:0925-2312
  • Container-title:Neurocomputing
  • language:en
  • Short-container-title:Neurocomputing

Author:

Sun Weiheng,He Xiaohai,Chen Honggang,Sheriff Ray E.,Xiong Shuhua

Funder

National Natural Science Foundation of China

Chengdu Science and Technology Bureau

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference59 articles.

1. Overview of the high efficiency video coding (HEVC) standard;Sullivan;IEEE Trans. Circuits Syst. Video Technol.,2012

2. Video quality evaluation methodology and verification testing of hevc compression performance;Tan;IEEE Trans. Circuits Syst. Video Technol.,2016

3. A deep learning approach for multi-frame in-loop filter of HEVC;Li;IEEE Trans. Image Process.,2019

4. Quality enhancement network via multi-reconstruction recursive residual learning for video coding;Yu;IEEE Signal Process Lett.,2019

5. Multi-scale residual network for image super-resolution;Li,2018

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1. An enhanced video compression approach through RLAH encoding and KDENN algorithms;EURASIP Journal on Advances in Signal Processing;2024-01-22

2. A quality enhancement network with coding priors for constant bit rate video coding;Knowledge-Based Systems;2022-12

3. Exploring Spatiotemporal Relationships for Improving Compressed Video Quality;2022 26th International Conference on Pattern Recognition (ICPR);2022-08-21

4. Compressed Video Quality Enhancement with Motion Approximation and Blended Attention;2022 26th International Conference on Pattern Recognition (ICPR);2022-08-21

5. HEVC Video Quality Enhancement Using Deep Learning with Super Interpolation and Laplacian Filter;IETE Journal of Research;2022-06-22
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