All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation

• URL: http://arxiv.org/abs/2409.19660v1
• Date: Sun, 29 Sep 2024 11:14:21 GMT
• Title: All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation
• Authors: Xu Zhang, Peiyao Guo, Ming Lu, Zhan Ma,
• Abstract summary: We propose Multi-Path Aggregation (MPA) integrated into existing coding models for joint human-machine vision. MPA employs a predictor to allocate latent features among task-specific paths. MPA achieves performance comparable to state-of-the-art methods in both task-specific and multi-objective optimization.
• Score: 28.62276713652864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image coding for multi-task applications, catering to both human perception and machine vision, has been extensively investigated. Existing methods often rely on multiple task-specific encoder-decoder pairs, leading to high overhead of parameter and bitrate usage, or face challenges in multi-objective optimization under a unified representation, failing to achieve both performance and efficiency. To this end, we propose Multi-Path Aggregation (MPA) integrated into existing coding models for joint human-machine vision, unifying the feature representation with an all-in-one architecture. MPA employs a predictor to allocate latent features among task-specific paths based on feature importance varied across tasks, maximizing the utility of shared features while preserving task-specific features for subsequent refinement. Leveraging feature correlations, we develop a two-stage optimization strategy to alleviate multi-task performance degradation. Upon the reuse of shared features, as low as 1.89% parameters are further augmented and fine-tuned for a specific task, which completely avoids extensive optimization of the entire model. Experimental results show that MPA achieves performance comparable to state-of-the-art methods in both task-specific and multi-objective optimization across human viewing and machine analysis tasks. Moreover, our all-in-one design supports seamless transitions between human- and machine-oriented reconstruction, enabling task-controllable interpretation without altering the unified model. Code is available at https://github.com/NJUVISION/MPA.

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