  Graphics Interchange Format (GIF) is a widely used image file format. Due to
the limited number of palette colors, GIF encoding often introduces color
banding artifacts. Traditionally, dithering is applied to reduce color banding,
but introducing dotted-pattern artifacts. To reduce artifacts and provide a
better and more efficient GIF encoding, we introduce a differentiable GIF
encoding pipeline, which includes three novel neural networks: PaletteNet,
DitherNet, and BandingNet. Each of these three networks provides an important
functionality within the GIF encoding pipeline. PaletteNet predicts a
near-optimal color palette given an input image. DitherNet manipulates the
input image to reduce color banding artifacts and provides an alternative to
traditional dithering. Finally, BandingNet is designed to detect color banding,
and provides a new perceptual loss specifically for GIF images. As far as we
know, this is the first fully differentiable GIF encoding pipeline based on
deep neural networks and compatible with existing GIF decoders. User study
shows that our algorithm is better than Floyd-Steinberg based GIF encoding.
