Brand F, Fischer K, Kaup A (2021)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2021
Pages Range: 1-5
DOI: 10.1109/cvprw53098.2021.00211
Deep-learning-based compressive autoencoders consist of a single non-linear function mapping the image to a latent space which is quantized and transmitted. Afterwards, a second non-linear function transforms the received latent space back to a reconstructed image. This method achieves superior quality than many traditional image coders, which is due to a non-linear generalization of linear transforms used in traditional coders. However, modern image and video coder achieve large coding gains by applying rate-distortion optimization on dynamic block-partitioning. In this paper, we present RDONet, a novel approach to achieve similar effects in compression with full image autoencoders by using different hierarchical levels, which are transmitted adaptively after performing an external rate-distortion optimization. Using our model, we are able to save up to 20% rate over comparable non-hierarchical models while maintaining the same quality.
APA:
Brand, F., Fischer, K., & Kaup, A. (2021). Rate-Distortion Optimized Learning-Based Image Compression using an Adaptive Hierachical Autoencoder with Conditional Hyperprior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 1-5). Virtual, US.
MLA:
Brand, Fabian, Kristian Fischer, and André Kaup. "Rate-Distortion Optimized Learning-Based Image Compression using an Adaptive Hierachical Autoencoder with Conditional Hyperprior." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Virtual 2021. 1-5.
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