# Neural Distributed Image Compression with Cross-Attention Feature Alignment arXiv: https://arxiv.org/abs/2207.08489 ยท HTML: https://mlsanity.com/paper/2207.08489/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 41 references that are in the map. - First posted on arXiv 18 Jul 2022. ## Builds on (most cited first, 24 of 24) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016) - [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018) - [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019) - [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016) - [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018) - [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020) - [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019) - [High-Fidelity Generative Image Compression](https://mlsanity.com/paper/2006.09965/) (2020) - [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017) - [Generative Adversarial Networks for Extreme Learned Image Compression](https://mlsanity.com/paper/1804.02958/) (2018) - [Full Resolution Image Compression with Recurrent Neural Networks](https://mlsanity.com/paper/1608.05148/) (2016) - [Context-adaptive Entropy Model for End-to-end Optimized Image Compression](https://mlsanity.com/paper/1809.10452/) (2018) - [Real-Time Adaptive Image Compression](https://mlsanity.com/paper/1705.05823/) (2017) - [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015) - [Non-local Attention Optimized Deep Image Compression](https://mlsanity.com/paper/1904.09757/) (2019) - [Causal Contextual Prediction for Learned Image Compression](https://mlsanity.com/paper/2011.09704/) (2020) - [Learning Content-Weighted Deep Image Compression](https://mlsanity.com/paper/1904.00664/) (2019) - [DSIC: Deep Stereo Image Compression](https://mlsanity.com/paper/1908.03631/) (2019) - [Deep Image Compression using Decoder Side Information](https://mlsanity.com/paper/2001.04753/) (2020) - [Neural Distributed Source Coding](https://mlsanity.com/paper/2106.02797/) (2021) - [Neural Distributed Image Compression using Common Information](https://mlsanity.com/paper/2106.11723/) (2021) ## Built on by (most cited first, 15 of 15) - [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024) - [LDMIC: Learning-based Distributed Multi-view Image Coding](https://mlsanity.com/paper/2301.09799/) (2023) - [ECSIC: Epipolar Cross Attention for Stereo Image Compression](https://mlsanity.com/paper/2307.10284/) (2023) - [Task-aware Distributed Source Coding under Dynamic Bandwidth](https://mlsanity.com/paper/2305.15523/) (2023) - [Understanding Implosion in Text-to-Image Generative Models](https://mlsanity.com/paper/2409.12314/) (2024) - [Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information](https://mlsanity.com/paper/2310.04311/) (2023) - [CAMSIC: Content-aware Masked Image Modeling Transformer for Stereo Image Compression](https://mlsanity.com/paper/2403.08505/) (2024) - [Deep learning-based neurodevelopmental assessment in preterm infants](https://mlsanity.com/paper/2601.11944/) (2026) - [Federated Neural Compression Under Heterogeneous Data](https://mlsanity.com/paper/2305.16416/) (2023) - [Deeply-Conditioned Image Compression via Self-Generated Priors](https://mlsanity.com/paper/2510.24437/) (2025) - [Learned layered coding for Successive Refinement in the Wyner-Ziv Problem](https://mlsanity.com/paper/2311.03061/) (2023) - [SSRFlow: Semantic-aware Fusion with Spatial Temporal Re-embedding for Real-world Scene Flow](https://mlsanity.com/paper/2408.07825/) (2024) - [MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal Prostate MRI Segmentation](https://mlsanity.com/paper/2510.17529/) (2025) - [Awakening the Hydra: Stabilizing Multi-Concept Backdoor Injection in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2605.19698/) (2026) - [Distributed Image Compression with Multimodal Side Information at Extremely Low Bitrates](https://mlsanity.com/paper/2605.22061/) (2026) ## Abstract (arXiv) We consider the problem of compressing an information source when a correlated one is available as side information only at the decoder side, which is a special case of the distributed source coding problem in information theory. In particular, we consider a pair of stereo images, which have overlapping fields of view, and are captured by a synchronized and calibrated pair of cameras as correlated image sources. In previously proposed methods, the encoder transforms the input image to a latent representation using a deep neural network, and compresses the quantized latent representation losslessly using entropy coding. The decoder decodes the entropy-coded quantized latent representation, and reconstructs the input image using this representation and the available side information. In the proposed method, the decoder employs a cross-attention module to align the feature maps obtained from the received latent representation of the input image and a latent representation of the side information. We argue that aligning the correlated patches in the feature maps allows better utilization of the side information. We empirically demonstrate the competitiveness of the proposed algorithm on KITTI and Cityscape datasets of stereo image pairs. Our experimental results show that the proposed architecture is able to exploit the decoder-only side information in a more efficient manner compared to previous works.