.png){kind=icon}
返回
Towards Coding for Human and Machine Vision: Scalable Face Image Coding
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2021.3068580.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The past decades have witnessed the rapid development of image and video coding techniques in the era of big data. However, the signal fidelity-driven coding pipeline design limits the capability of the existing image/video coding frameworks to fulfill the needs of both machine and human vision. In this paper, we come up with a novel face image coding framework by leveraging both the compressive and the generative models, to support machine vision and human perception tasks jointly. Given an input image, the feature analysis is first applied, and then the generative model is employed to reconstruct image with compact structure and color features, where sparse edges are extracted to connect both kinds of vision and a key reference pixel selection method is proposed to determine the priorities of the reference color pixels for scalable coding. The compact edge map serves as the basic layer for machine vision tasks, and the reference pixels act as an enhanced layer to guarantee signal fidelity for human vision. By introducing advanced generative models, we train a decoding network to reconstruct images from compact structure and color representations, which is flexible to accept inputs in a scalable way and to control the imagery effect of the outputs between signal fidelity and visual realism. Experimental results and comprehensive performance analysis over the face image dataset demonstrate the superiority of our framework in both human vision tasks and machine vision tasks, which provide useful evidence on the emerging standardization efforts on MPEG VCM (Video Coding for Machine).
Keyword:
Image coding
Machine vision
Task analysis
Image reconstruction
Visualization
Feature extraction
Image color analysis
Generative compression
image coding
scalable coding
video coding for machine
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.7
论文数:
4.6K
被引数:
2.4W
机构
引用论文
.png){kind=icon}
分享收藏
Cartoon-Texture Image Decomposition Using Blockwise Low-Rank Texture Characterization使用逐块低秩纹理表征的卡通纹理图像分解
PREAI
分享收藏
查看更多内容