计算机科学
比特流
图像压缩
计算机视觉
人工智能
数据压缩
图像分割
分割
模式识别(心理学)
算法
解码方法
图像处理
图像(数学)
作者
Ning Yan,Changsheng Gao,Dong Liu,Houqiang Li,Li Li,Feng Wu
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:30: 8939-8954
被引量:20
标识
DOI:10.1109/tip.2021.3121131
摘要
We address the requirement of image coding for joint human-machine vision, i.e., the decoded image serves both human observation and machine analysis/understanding. Previously, human vision and machine vision have been extensively studied by image (signal) compression and (image) feature compression, respectively. Recently, for joint human-machine vision, several studies have been devoted to joint compression of images and features, but the correlation between images and features is still unclear. We identify the deep network as a powerful toolkit for generating structural image representations. From the perspective of information theory, the deep features of an image naturally form an entropy decreasing series: a scalable bitstream is achieved by compressing the features backward from a deeper layer to a shallower layer until culminating with the image signal. Moreover, we can obtain learned representations by training the deep network for a given semantic analysis task or multiple tasks and acquire deep features that are related to semantics. With the learned structural representations, we propose SSSIC, a framework to obtain an embedded bitstream that can be either partially decoded for semantic analysis or fully decoded for human vision. We implement an exemplar SSSIC scheme using coarse-to-fine image classification as the driven semantic analysis task. We also extend the scheme for object detection and instance segmentation tasks. The experimental results demonstrate the effectiveness of the proposed SSSIC framework and establish that the exemplar scheme achieves higher compression efficiency than separate compression of images and features.
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