Removing Banding Artifacts in HDR Videos Generated From Inverse Tone Mapping

Abstract

Displaying standard dynamic range (SDR) videos on high dynamic range (HDR) devices requires inverse tone mapping (ITM). However, such mapping can introduce banding artifacts. This paper presents a banding removal method for inversely tone mapped HDR videos based on deep convolutional neural networks (DCNNs) and adaptive filtering. Three banding relevant feature maps are first extracted and then fed to two DCNNs, a ShapeNet and a PositionNet. The PositionNet learns a soft mask indicating the locations where banding is likely to have occurred and filtering is required while the ShapeNet predicts the filter shapes appropriate for different locations. An advantage of the method is that the adaptive filters can be jointly optimized with a learning-based ITM algorithm for creating high-quality HDR videos. Experimental results show that our method outperforms state-of-the-art algorithms qualitatively and quantitatively.