Improving Multiple Machine Vision Tasks in the Compressed Domain

Jinming Liu, Heming Sun*, Jiro Katto

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There is a growing number of images that are analyzed by machines rather than just humans. Recently, most machine vision tasks are based on decoded images which require an image compression (encoding/decoding) framework. However, using the decoded images in the pixel-domain has two drawbacks: 1) the complexity is high for the decoder part, 2) the accuracy (e.g., mIoU, mean absolute error, and average precision) of machine vision tasks will be degraded since decoded images only aim to optimize the human perceived quality (e.g., PSNR) so that information required for machine vision tasks will be lost during the decoding process. In this paper, we improve the machine vision tasks in the compressed domain. 1) A gate module is utilized to effectively select some compressed-domain features. 2) Knowledge distillation is introduced to improve the accuracy. 3) A training strategy is explored to support multiple tasks including the image compression. The experimental results show that we can achieve better rate-accuracy/distortion and lower complexity compared with the state-of-the-art pixel-domain work that can take both machine and human vision tasks.

Original languageEnglish
Title of host publication2022 26th International Conference on Pattern Recognition, ICPR 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages331-337
Number of pages7
ISBN (Electronic)9781665490627
DOIs
Publication statusPublished - 2022
Event26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, Canada
Duration: 2022 Aug 212022 Aug 25

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2022-August
ISSN (Print)1051-4651

Conference

Conference26th International Conference on Pattern Recognition, ICPR 2022
Country/TerritoryCanada
CityMontreal
Period22/8/2122/8/25

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'Improving Multiple Machine Vision Tasks in the Compressed Domain'. Together they form a unique fingerprint.

Cite this