# Conditional Latent Coding with Learnable Synthesized Reference for Deep Image Compression

arXiv: https://arxiv.org/abs/2502.09971 · HTML: https://mlsanity.com/paper/2502.09971/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 20 of 20)

- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [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)
- [Deep Contextual Video Compression](https://mlsanity.com/paper/2109.15047/) (2021)
- [Learned Image Compression with Mixed Transformer-CNN Architectures](https://mlsanity.com/paper/2303.14978/) (2023)
- [Full Resolution Image Compression with Recurrent Neural Networks](https://mlsanity.com/paper/1608.05148/) (2016)
- [Neural Video Compression with Diverse Contexts](https://mlsanity.com/paper/2302.14402/) (2023)
- [Context-adaptive Entropy Model for End-to-end Optimized Image Compression](https://mlsanity.com/paper/1809.10452/) (2018)
- [Conditional Probability Models for Deep Image Compression](https://mlsanity.com/paper/1801.04260/) (2018)
- [Temporal Context Mining for Learned Video Compression](https://mlsanity.com/paper/2111.13850/) (2021)
- [Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models](https://mlsanity.com/paper/2310.06313/) (2023)
- [A Unified End-to-End Framework for Efficient Deep Image Compression](https://mlsanity.com/paper/2002.03370/) (2020)
- [SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization](https://mlsanity.com/paper/2401.06385/) (2024)
- [MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert](https://mlsanity.com/paper/2412.12704/) (2024)
- [Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation](https://mlsanity.com/paper/2308.09917/) (2023)
- [Mask Factory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation](https://mlsanity.com/paper/2412.19080/) (2024)
- [TINC: Tree-structured Implicit Neural Compression](https://mlsanity.com/paper/2211.06689/) (2022)
- [T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency](https://mlsanity.com/paper/2312.01529/) (2023)
- [Ultra-High Resolution Segmentation via Boundary-Enhanced Patch-Merging Transformer](https://mlsanity.com/paper/2412.10181/) (2024)
- [RFD-ECNet: Extreme Underwater Image Compression with Reference to Feature Dictionar](https://mlsanity.com/paper/2308.08721/) (2023)

## Built on by (most cited first, 11 of 11)

- [OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrieval](https://mlsanity.com/paper/2507.05631/) (2025)
- [HUD: Hierarchical Uncertainty-Aware Disambiguation Network for Composed Video Retrieval](https://mlsanity.com/paper/2512.02792/) (2025)
- [Next-Frame Decoding for Ultra-Low-Bitrate Image Compression with Video Diffusion Priors](https://mlsanity.com/paper/2603.15129/) (2026)
- [Scaling Learned Image Compression Models up to 1 Billion](https://mlsanity.com/paper/2508.09075/) (2025)
- [Dual form Complementary Masking for Domain-Adaptive Image Segmentation](https://mlsanity.com/paper/2507.12008/) (2025)
- [Adaptive Learned Image Compression with Graph Neural Networks](https://mlsanity.com/paper/2603.25316/) (2026)
- [FaSDiff: Balancing Perception and Semantics in Face Compression via Stable Diffusion Priors](https://mlsanity.com/paper/2505.05870/) (2025)
- [HCF: Hierarchical Cascade Framework for Distributed Multi-Stage Image Compression](https://mlsanity.com/paper/2508.02051/) (2025)
- [HiDE: Hierarchical Dictionary-Based Entropy Modeling for Learned Image Compression](https://mlsanity.com/paper/2603.06766/) (2026)
- [Every Packet Counts: Dispersing Information for Loss-Resilient Learned Image Compression](https://mlsanity.com/paper/2608.11096/) (2026)
- [Recasting DiT for Extreme Image Compression with Hierarchical Visual Priors](https://mlsanity.com/paper/2610.18607/) (2026)

## Abstract (arXiv)

In this paper, we study how to synthesize a dynamic reference from an external dictionary to perform conditional coding of the input image in the latent domain and how to learn the conditional latent synthesis and coding modules in an end-to-end manner. Our approach begins by constructing a universal image feature dictionary using a multi-stage approach involving modified spatial pyramid pooling, dimension reduction, and multi-scale feature clustering. For each input image, we learn to synthesize a conditioning latent by selecting and synthesizing relevant features from the dictionary, which significantly enhances the model's capability in capturing and exploring image source correlation. This conditional latent synthesis involves a correlation-based feature matching and alignment strategy, comprising a Conditional Latent Matching (CLM) module and a Conditional Latent Synthesis (CLS) module. The synthesized latent is then used to guide the encoding process, allowing for more efficient compression by exploiting the correlation between the input image and the reference dictionary. According to our theoretical analysis, the proposed conditional latent coding (CLC) method is robust to perturbations in the external dictionary samples and the selected conditioning latent, with an error bound that scales logarithmically with the dictionary size, ensuring stability even with large and diverse dictionaries. Experimental results on benchmark datasets show that our new method improves the coding performance by a large margin (up to 1.2 dB) with a very small overhead of approximately 0.5% bits per pixel. Our code is publicly available at https://github.com/ydchen0806/CLC.