旷视科技：GIF到视频增强算法，提升网络图形质量
下载需积分: 49 | PDF格式 | 4.55MB |
更新于2024-09-07
| 112 浏览量 | 举报
视频插帧技术是现代视频处理领域的重要研究方向，特别是在增强低质量或压缩视频的视觉效果方面。这篇旷视科技的论文《GIF2Video: ColorDequantization and Temporal Interpolation of GIF images》深入探讨了如何利用深度学习方法提升网络上广泛使用的Graphics Interchange Format (GIF)图像的质量。GIF由于其小巧便携，但常常因为帧采样、颜色量化和色彩抖动过程导致出现平滑颜色区域、虚假轮廓、颜色漂移和点状图案等视觉问题。
论文的核心贡献在于提出了一种新颖的卷积神经网络（CNN）架构，用于解决颜色去量化问题。这个架构基于多步骤的颜色校正设计，特别注重处理大范围的颜色量化误差，通过一个综合的损失函数来确保恢复过程中尽可能减少失真。这种方法旨在弥补GIF制作过程中丢失的信息，尤其是在颜色的精确度上。
此外，论文还引入了SuperSlomo网络的思想，将其应用于GIF帧的时序插值，旨在提升GIF视频在连续动作表现上的流畅度。作者构建了两个大型的GIF图像数据集，GIF-Faces和GIF-Moments，这些数据集包含了丰富的GIF图像样本，供模型训练和评估。
通过GIF2Video方法，研究人员能够将GIF从一种常见的低质量图像格式提升到接近于视频的视觉体验，这对于在线内容创作者来说，提供了改进动态图像质量和创作灵活性的新途径。同时，这项技术也为其他领域的视频处理和压缩算法提供了新的思考角度，展示了深度学习在解决传统图像处理难题中的巨大潜力。这篇论文对于提高网络上动态图像的质量和用户体验具有重要意义。
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images
Yang Wang
1
, Haibin Huang
2
, Chuan Wang
2
, Tong He
3
, Jue Wang
2
, Minh Hoai
1
1
Stony Brook University,
2
Megvii Research USA,
3
UCLA
Abstract
Graphics Interchange Format (GIF) is a highly portable
graphics format that is ubiquitous on the Internet. De-
spite their small sizes, GIF images often contain undesir-
able visual artifacts such as flat color regions, false con-
tours, color shift, and dotted patterns. In this paper, we
propose GIF2Video, the first learning-based method for en-
hancing 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 de-
signed 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. Ex-
perimental results show that our method can significantly
improve the visual quality of GIFs, and outperforms direct
baseline and state-of-the-art approaches.
1. Introduction
GIFs [1] are everywhere, being created and consumed
by millions of Internet users everyday on the Internet. The
widespread of GIFs can be attributed to its high portability
and small file sizes. However, due to heavy quantization
in the creation process, GIFs often have much worse visual
quality than their original source videos. Creating an ani-
mated GIF from a video involves three major steps: frame
sampling, color quantization, and optional color dithering.
Frame sampling introduces jerky motion, while color quan-
tization and color dithering create flat color regions, false
contours, color shift, and dotted pattern, as shown in Fig. 1.
In this paper, we propose GIF2Video, the first learning-
based method for enhancing the visual quality of GIFs. Our
algorithm consists of two components. First, it performs
color dequantization for each frame of the animated gif
sequence, removing the artifacts introduced by both color
quantization and color dithering. Second, it increases the
Color
Quantization
Color
Dithering
Artifacts:
1. False Contour
2. Flat Region
3. Color Shift
Artifacts:
4. Dotted Pattern
!"#"$ %&#'(('
)$$"$ *+,,-.+"/
Figure 1. Color quantization and color dithering. Two major
steps in the creation of a GIF image. These are lossy compression
processes that result in undesirable visual artifacts. Our approach
is able to remove these artifacts and produce a much more natural
image.
temporal resolution of the image sequence by using a mod-
ified SuperSlomo [19] network for temporal interpolation.
The main effort of this work is to develop a method for
color dequantization, i.e., removing the visual artifacts in-
troduced by heavy color quantization. Color quantization is
a lossy compression process that remaps original pixel col-
ors to a limited set of entries in a small color palette. This
process introduces quantization artifacts, similar to those
observed when the bit depth of an image is reduced. For
example, when the image bit depth is reduced from 48-bit
to 24-bit, the size of the color palette shrinks from 2.8×10
14
colors to 1.7 × 10
7
colors, leading to a small amount of ar-
tifacts. The color quantization process for GIF, however,
is far more aggressive with a typical palette of 256 dis-
tinct colors or less. Our task is to perform dequantization
from a tiny color palette (e.g., 256 or 32 colors), and it is
much more challenging than traditional bit depth enhance-
ment [15, 24, 36].
Of course, recovering all original pixel colors from the
quantized image is nearly impossible, thus our goal is ren-
der a plausible version of what the original image might
look like. The idea is to collect training data and train a
convolutional neural network [22, 32] to map a quantized
image to its original version. It is however difficult to ob-
1
arXiv:1901.02840v1 [cs.CV] 9 Jan 2019
下载后可阅读完整内容，剩余9页未读，立即下载
相关推荐
240 浏览量
yuankaojiao475
- 粉丝: 0
我的内容管理
展开
最新资源
- Ruby语言集成Mandrill API的gem开发
- 开源嵌入式qt软键盘SYSZUXpinyin可移植源代码
- Kinect2.0实现高清面部特征精确对齐技术
- React与GitHub Jobs API整合的就业搜索应用
- MATLAB傅里叶变换函数应用实例分析
- 探索鼠标悬停特效的实现与应用
- 工行捷德U盾64位驱动程序安装指南
- Apache与Tomcat整合集群配置教程
- 成为JavaScript英雄：掌握be-the-hero-master技巧
- 深入实践Java编程珠玑：第13章源代码解析
- Proficy Maintenance Gateway软件：实时维护策略助力业务变革
- HTML5图片上传与编辑控件的实现
- RTDS环境下电网STATCOM模型的应用与分析
- 掌握Matlab下偏微分方程的有限元方法解析
- Aop原理与示例程序解读
- projete大语言项目登陆页面设计与实现
