论文标题
gifnets：可区分的GIF编码框架
GIFnets: Differentiable GIF Encoding Framework
论文作者
论文摘要
图形交换格式（GIF）是一种广泛使用的图像文件格式。由于调色板颜色数量有限，GIF编码通常会引入颜色带伪像。传统上，抖动用于减少颜色谱带，但引入了虚拟图案。为了减少工件并提供更好，更有效的GIF编码，我们引入了一条可区分的GIF编码管道，其中包括三个新型的神经网络：Palettenet，dithernet和bandingnet。这三个网络中的每一个都提供了GIF编码管道中的重要功能。 Palettenet预测给定输入图像的近乎最佳的调色板。 DeThernet操纵输入图像以减少颜色带伪像，并为传统抖动提供了替代方案。最后，BandingNet旨在检测颜色带，并为GIF图像提供了新的感知损失。据我们所知，这是基于深神经网络的第一个完全可分辨的GIF编码管道，并且与现有的GIF解码器兼容。用户研究表明，我们的算法比基于Floyd-Steinberg的GIF编码更好。
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.
