A novel Cross-Component Context Model for End-to-End Wavelet Image Coding

Autor:	Meyer, Anna, Kaup, André
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	In contrast to traditional compression techniques performing linear transforms, the latent space of popular compressive autoencoders is obtained from a learned nonlinear mapping and hard to interpret. In this paper, we explore a promising alternative approach for neural compression, with an autoencoder whose latent space represents a nonlinear wavelet decomposition. Previous work has shown that neural wavelet image coding can outperform HEVC. However, the approach codes color components independently, thereby ignoring inter-component dependencies. Hence, we propose a novel cross-component context model (CCM). With CCM, the entropy model for the chroma latent space can be conditioned on previously coded components exploiting correlations in the learned wavelet space. The proposed CCM outperforms the baseline model with average Bj{\o}ntegaard delta rate savings of 2.6 % and 1.6 % for the Kodak and Tecnick image sets. Also, our method is competitive with VVC and learning-based methods. Comment: Accepted for publication at ICASSP 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.05121 Zobrazit plný text záznamu View this record from Arxiv