# Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression

arXiv: https://arxiv.org/abs/2004.10043 · HTML: https://mlsanity.com/paper/2004.10043/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 28 of 28)

- [Collaborative Intelligence: Challenges and Opportunities](https://mlsanity.com/paper/2102.06841/) (2021)
- [Preprocessing Enhanced Image Compression for Machine Vision](https://mlsanity.com/paper/2206.05650/) (2022)
- [All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation](https://mlsanity.com/paper/2409.19660/) (2024)
- [Generative Visual Compression: A Review](https://mlsanity.com/paper/2402.02140/) (2024)
- [Semantically Structured Image Compression via Irregular Group-Based Decoupling](https://mlsanity.com/paper/2305.02586/) (2023)
- [Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics](https://mlsanity.com/paper/2110.09241/) (2021)
- [Scalable Face Image Coding via StyleGAN Prior: Towards Compression for Human-Machine Collaborative Vision](https://mlsanity.com/paper/2312.15622/) (2023)
- [Rate-Distortion-Cognition Controllable Versatile Neural Image Compression](https://mlsanity.com/paper/2407.11700/) (2024)
- [Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling](https://mlsanity.com/paper/2211.06797/) (2022)
- [Prompt-ICM: A Unified Framework towards Image Coding for Machines with Task-driven Prompts](https://mlsanity.com/paper/2305.02578/) (2023)
- [Task-Aware Encoder Control for Deep Video Compression](https://mlsanity.com/paper/2404.04848/) (2024)
- [Scalable Image Coding for Humans and Machines Using Feature Fusion Network](https://mlsanity.com/paper/2405.09152/) (2024)
- [High Efficiency Image Compression for Large Visual-Language Models](https://mlsanity.com/paper/2407.17060/) (2024)
- [DT-UFC: Universal Large Model Feature Coding via Peaky-to-Balanced Distribution Transformation](https://mlsanity.com/paper/2506.16495/) (2025)
- [An Efficient Adaptive Compression Method for Human Perception and Machine Vision Tasks](https://mlsanity.com/paper/2501.04329/) (2025)
- [Towards Point Cloud Compression for Machine Perception: A Simple and Strong Baseline by Learning the Octree Depth Level Predictor](https://mlsanity.com/paper/2406.00791/) (2024)
- [Unified Coding for Both Human Perception and Generalized Machine Analytics with CLIP Supervision](https://mlsanity.com/paper/2501.04579/) (2025)
- [Compressed Feature Quality Assessment: Dataset and Baselines](https://mlsanity.com/paper/2506.07412/) (2025)
- [Explicit Residual-Based Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2506.19297/) (2025)
- [Guided Diffusion for the Extension of Machine Vision to Human Visual Perception](https://mlsanity.com/paper/2503.17907/) (2025)
- [Texture-guided Coding for Deep Features](https://mlsanity.com/paper/2405.19669/) (2024)
- [Coding for Intelligence from the Perspective of Category](https://mlsanity.com/paper/2407.01017/) (2024)
- [Hierarchical Semantic Compression for Consistent Image Semantic Restoration](https://mlsanity.com/paper/2502.16799/) (2025)
- [Towards Large Model Feature Coding](https://mlsanity.com/paper/2605.24025/) (2026)
- [Just Noticeable Difference Modeling for Face Recognition System](https://mlsanity.com/paper/2209.05856/) (2022)
- [UniMIC: Token-Based Multimodal Interactive Coding for Human-AI Collaboration](https://mlsanity.com/paper/2509.22570/) (2025)
- [FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence](https://mlsanity.com/paper/2605.22868/) (2026)
- [MoECodec: Image Compression for joint human and machine perception via Mixture-of-Experts](https://mlsanity.com/paper/2606.21033/) (2026)

## Abstract (arXiv)

It plays a fundamental role to compactly represent the visual information towards the optimization of the ultimate utility in myriad visual data centered applications. With numerous approaches proposed to efficiently compress the texture and visual features serving human visual perception and machine intelligence respectively, much less work has been dedicated to studying the interactions between them. Here we investigate the integration of feature and texture compression, and show that a universal and collaborative visual information representation can be achieved in a hierarchical way. In particular, we study the feature and texture compression in a scalable coding framework, where the base layer serves as the deep learning feature and enhancement layer targets to perfectly reconstruct the texture. Based on the strong generative capability of deep neural networks, the gap between the base feature layer and enhancement layer is further filled with the feature level texture reconstruction, aiming to further construct texture representation from feature. As such, the residuals between the original and reconstructed texture could be further conveyed in the enhancement layer. To improve the efficiency of the proposed framework, the base layer neural network is trained in a multi-task manner such that the learned features enjoy both high quality reconstruction and high accuracy analysis. We further demonstrate the framework and optimization strategies in face image compression, and promising coding performance has been achieved in terms of both rate-fidelity and rate-accuracy.