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http://dx.doi.org/10.5909/JBE.2019.24.3.420

Comparative Analysis of Deep Learning Researches for Compressed Video Quality Improvement  

Lee, Young-Woon (Department of IT Engineering, Sookmyung Women's University)
Kim, Byung-Gyu (Department of IT Engineering, Sookmyung Women's University)
Publication Information
Journal of Broadcast Engineering / v.24, no.3, 2019 , pp. 420-429 More about this Journal
Abstract
Recently, researches using Convolutional Neural Network (CNN)-based approaches have been actively conducted to improve the reduced quality of compressed video using block-based video coding standards such as H.265/HEVC. This paper aims to summarize and analyze the network models in these quality enhancement studies. At first the detailed components of CNN for quality enhancement are overviewed and then we summarize prior studies in the image domain. Next, related studies are summarized in three aspects of network structure, dataset, and training methods, and present representative models implementation and experimental results for performance comparison.
Keywords
CNN; HEVC; Noise Reduction; Quality Enhancement;
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