文献类型：期刊文献

英文题名：VivID: A Visually Improved GIF Encoding Network Design

作者：Wang, Yifei[1];Liu, Gaozhi[1];Zhu, Zhiying[2];Zhang, Xinpeng[1];Qian, Zhenxing[1]

机构：[1]Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2025

卷号：35

期号：6

起止页码：6101

外文期刊名：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY

收录：;EI(收录号：20250517789358);WOS:【SCI-EXPANDED(收录号:WOS:001506726300011)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant U20B2051 and in part by the Natural Key Research and Development Program of China under Grant 2023YFF0905000.

语种：英文

外文关键词：Image color analysis; Quantization (signal); Encoding; Training; Image coding; Colored noise; Pipelines; Visualization; Circuits and systems; Partitioning algorithms; Graphics interchange format (GIF); dithering; banding artifact

摘要：Graphics Interchange Format (GIF) encoding is the art of reproducing an image with limited colors. Existing GIF encoding schemes often introduce unpleasant visual artifacts such as banding artifact, dotted-pattern noise and color shift, especially when the palette size is small. To address the issues above, we propose VivID, a Visually Improved GIF Encoding Network Design, which is compatible with exiting GIF decoders. VivID consists of three modules and two of them provide the functionality within the GIF encoding pipeline. Firstly, in order to reduce the color shift introduced by color quantization, we design the multi-palette extractor to create a GIF image with minimal distortion by extracting a near-optimal palette. This module can significantly improve the image fidelity and gains adaptability to multiple palette sizes after only one-time training. Furthermore, to reduce banding artifact and the dotted-pattern noise caused by dithering process, we propose banding remover which can randomize quantization error to neighbourhood by utilizing a learnable dithering pattern. Moreover, to further eliminate the banding artifacts, we design the banding scorer module, which is a novel metric for evaluating banding artifact and it correlates well with subjective perception. We adopt it as a customized loss for training dithering module. Extensive experiments across various aspects demonstrate that VivID produces visually pleasing results even when the palette size is extremely small, outperforming both traditional and existing learning based GIF encoding methods.