User-Guided Variable Rate Learned Image Compression

Rushil Gupta, Suryateja BV, Nikhil Kapoor, Rajat Jaiswal, Sharmila Nangi, Kuldeep Kulkarni

Abstract: We propose a learning-based image compression method that achieves any arbitrary input bitrate via user-guided bit allocation to preferred regions. We verify our hypothesis of incorporating user guidance for bitrate control by experimenting with alternatives that do not have any guidance. We conduct extensive evaluation on CelebA-HQ and CityScapes dataset using standard quantitative metrics and human studies showing that our single model for multiple bitrates achieves similar or better performance as com-pared to previous learned image compression methods that require re-training for each new bitrate.