TY - GEN
T1 - SCALABLE FACIAL IMAGE COMPRESSION WITH DEEP FEATURE RECONSTRUCTION
AU - Wang, Shurun
AU - Wang, Shiqi
AU - Zhang, Xinfeng
AU - Wang, Shanshe
AU - Ma, Siwei
AU - Gao, Wen
PY - 2019/9
Y1 - 2019/9
N2 - In this paper, we propose a scalable image compression scheme, including the base layer for feature representation and enhancement layer for texture representation. More specifically, the base layer is designed as the deep learning feature for analysis purpose, and it can also be converted to the fine structure with deep feature reconstruction. The enhancement layer, which serves to compress the residuals between the input image and the signals generated from the base layer, aims to faithfully reconstruct the input texture. The proposed scheme can feasibly inherit the advantages of both compress-then-analyze and analyze-then-compress schemes in surveillance applications. The performance of this framework is validated with facial images, and the conducted experiments provide useful evidences to show that the proposed framework can achieve better rate-accuracy and rate-distortion performance over conventional image compression schemes.
AB - In this paper, we propose a scalable image compression scheme, including the base layer for feature representation and enhancement layer for texture representation. More specifically, the base layer is designed as the deep learning feature for analysis purpose, and it can also be converted to the fine structure with deep feature reconstruction. The enhancement layer, which serves to compress the residuals between the input image and the signals generated from the base layer, aims to faithfully reconstruct the input texture. The proposed scheme can feasibly inherit the advantages of both compress-then-analyze and analyze-then-compress schemes in surveillance applications. The performance of this framework is validated with facial images, and the conducted experiments provide useful evidences to show that the proposed framework can achieve better rate-accuracy and rate-distortion performance over conventional image compression schemes.
KW - Image compression
KW - deep learning feature
KW - feature reconstruction
KW - scalable coding
UR - http://www.scopus.com/inward/record.url?scp=85076805988&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85076805988&origin=recordpage
U2 - 10.1109/ICIP.2019.8803255
DO - 10.1109/ICIP.2019.8803255
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2691
EP - 2695
BT - 2019 IEEE International Conference on Image Processing - Proceedings
PB - IEEE
T2 - 26th IEEE International Conference on Image Processing (ICIP 2019)
Y2 - 22 September 2019 through 25 September 2019
ER -