Abstract
Scalable image compression allows reconstructing complete images through partially decoding. It plays an important role for image transmission and storage. In this paper, we study the problem of feature decorrelation for Deep Neural Network (DNN) based image codec. Inspired by self-attention mechanism [1], we design a transformer-based decorrelation unit (DU) and adopt it in our scalable image compression framework to reduce the redundancy of feature representations at different levels. Experimental results demonstrate that proposed framework outperforms the state-of-the-art DNN-based scalable image codec and conventional scalable image codecs in terms of MS-SSIM. We also conduct ablation experiments which explicitly verify the effectiveness of decorrelation unit in our scheme.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
