Study on deep CNN as preprocessing for video compression

초록

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.

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