GIF2Video: Color Dequantization and Temporal Interpolation of GIF images
This addresses the issue of poor visual quality in widely used GIFs for internet users, though it is incremental as it builds on existing techniques like SuperSlomo.
The paper tackles the problem of visual artifacts in GIFs by proposing GIF2Video, a learning-based method that restores quality through color dequantization and temporal interpolation, achieving significant improvements over baseline and state-of-the-art approaches.
Graphics Interchange Format (GIF) is a highly portable graphics format that is ubiquitous on the Internet. Despite their small sizes, GIF images often contain undesirable visual artifacts such as flat color regions, false contours, color shift, and dotted patterns. In this paper, we propose GIF2Video, the first learning-based method for enhancing the visual quality of GIFs in the wild. We focus on the challenging task of GIF restoration by recovering information lost in the three steps of GIF creation: frame sampling, color quantization, and color dithering. We first propose a novel CNN architecture for color dequantization. It is built upon a compositional architecture for multi-step color correction, with a comprehensive loss function designed to handle large quantization errors. We then adapt the SuperSlomo network for temporal interpolation of GIF frames. We introduce two large datasets, namely GIF-Faces and GIF-Moments, for both training and evaluation. Experimental results show that our method can significantly improve the visual quality of GIFs, and outperforms direct baseline and state-of-the-art approaches.