.png){kind=icon}
返回
An overhead-free region-based JPEG framework for task-driven image compression
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2022.11.020.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
An increasing amount of captured images are streamed to a remote server or stored in a device for deep neural network (DNN) inference. In most cases, raw images are compressed with encoding algorithms such as JPEG to cope with resource limitations. However, the standard JPEG optimized for human visual systems may induce significant accuracy loss in DNN inference tasks. In addition, the standard JPEG com-presses all regions in an image at the same quality level, while some areas may not contain valuable information for the target task. In this paper, we propose a target-driven JPEG compression framework that performs region-adaptive quantization of the DCT coefficients. The region-based quality map is gen-erated from an end-to-end trainable neural network. In addition, we present a deep learning approach to remove the requirement of storing the overhead information induced by the region-based encoding pro-cess. Our framework can be easily implemented on devices with commonly used JPEG and also produce images that achieve a higher compression rate with minimum degradation of the classification accuracy. The codes are available at https://github.com/jshye/qf- map-generator .(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Image compression
JPEG
Neural networks
Overhead-free
Accuracy-aware
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
Target-Dependent Scalable Image Compression Using a Reconfigurable Recurrent Neural Network.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
Erratum: “Probing ultrafast carrier and phonon dynamics in semiconductors” [J. Appl. Phys. 83, 1789 (1998)]勘误：“探测半导体中载流子和声子超快动力学” [J. Appl. Phys. 83, 1789 (1998)]OAAI
OAAI分享收藏Saliency for free: Saliency prediction as a side-effect of object recognition免费的显着性: 显着性预测是对象识别的副作用OAAI
OAAI分享收藏 没有更多内容
没有更多内容