Publication: Flexible luma-chroma bit allocation in learned image compression for high-fidelity sharper images
Program
KU-Authors
KU Authors
Co-Authors
Publication Date
Language
Embargo Status
Journal Title
Journal ISSN
Volume Title
Alternative Title
Abstract
High-fidelity learned image/video compression solutions are typically optimized with respect to l1 or l2 loss in RGB 444 format and evaluated by RGB PSNR. It is well-known that optimization of a fidelity criterion results in blurry images, which is typically alleviated by adding a content-based and/or adversarial loss terms. However, such conditional generative models result in loss of fidelity. In this paper, we propose a simple solution to obtain sharper images without losing fidelity based on learned flexible-rate coding using gained variational auto-encoder (gained-VAE) in the luma-chroma (YCrCb 444) domain. This allows us to implement image-adaptive luma-chroma bit allocation during inference, i.e., to increase Y PSNR at the expense of slightly lower chroma PSNR to obtain sharper images without introducing color artifacts based on the observation that Y PSNR correlates with image sharpness better than RGB PSNR. We note that the proposed inference-time image-adaptive luma-chroma bit allocation strategy can be incorporated into any VAE-based image compression model. Experimental results show that sharper images with better VMAF and Y PSNR can be obtained by optimizing models for YCrCb MSE with the proposed image-adaptive luma-chroma bit/quality allocation compared to stateof-the-art models optimizing RGB MSE at the same bpp.
Source
Publisher
IEEE
Subject
Electrical electronic engineering, Imaging system, Neural computers, Neural networks (Computer science), Image processing, Photography
Citation
Has Part
Source
2022 Picture Coding Symposium (Pcs)
Book Series Title
Edition
DOI
10.1109/PCS56426.2022.10017994