An Extended Hybrid Image Compression Based on Soft-to-Hard Quantification

Abstract

Recently, the deep learning methods have been widely used in lossy compression schemes, greatly improving image compression performance. In this paper, we propose an extended hybrid image compression scheme based on soft-to-hard quantification, which has only two layers. The compact representation of the input image is encoded by the FLIF codec as the base layer. The residual of the input image and the reconstructed image is encoded by the BPG codec as the enhancement layer. The results using the Kodak and Tecnick datasets show that the performance of our proposed methods exceeds some image compression schemes based on deep learning methods and some traditional coding standards including BPG in SSIM metric across a wide range of bit rates, when the images are coded in the RGB444 domain. We explore the issue of bit rates allocation of the base layer and enhancement layer and the impact of enhancement layer codecs. Also, we analyze the limitations of the hybrid coding scheme.

Keywords

• Extend hybrid image compression scheme
• soft-to-hard quantification
• bit rates allocation
• enhancement layer codecs