Meyer A, Kaup A (2023)
Publication Type: Conference contribution, Conference Contribution
Publication year: 2023
Event location: Rhodes Island, Greece
URI: https://arxiv.org/abs/2303.05121
DOI: 10.1109/ICASSP49357.2023.10096867
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 Bjøntegaard 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.
APA:
Meyer, A., & Kaup, A. (2023). A novel Cross-Component Context Model for End-to-End Wavelet Image Coding. In Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Rhodes Island, Greece.
MLA:
Meyer, Anna, and André Kaup. "A novel Cross-Component Context Model for End-to-End Wavelet Image Coding." Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece 2023.
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