@inproceedings{4ec27db3eae24f24bbd965ed17cd5008,
title = "Study on deep CNN as preprocessing for video compression",
abstract = "In the recent years, video compression and picture quality have become more intense topic in research areas. In addition, user prerequisite for better resolution and higher quality video compression is increasing. Versatile video coding (VVC) is the latest emerging video coding standard specially designed for video compression. However, its frequency-based transform techniques are vulnerable on high-frequency noise, which results in increased bitrate or low picture quality. To resolve such unintended attack, we apply denoising convolutional neural network (DnCNN) to input video of codecs as a preprocessing since the DnCNN model was studied for image denoising with the capability of handling Gaussian denoising with residual learning strategy. In this paper we demonstrate experimental results that how DnCNN model helps for noised video data in terms of quality and bitrate.",
keywords = "Deep learning, Denoising, HEVC, Preprocessing, Video compression, Video quality",
author = "Bhosale, {Kavita Arjun} and Seungho Kuk and Park, {Sang Hyo}",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Applications of Digital Image Processing XLIV 2021 ; Conference date: 01-08-2021 Through 05-08-2021",
year = "2021",
doi = "10.1117/12.2596227",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Tescher, {Andrew G.} and Touradj Ebrahimi",
booktitle = "Applications of Digital Image Processing XLIV",
address = "United States",
}