Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference

• URL: http://arxiv.org/abs/2507.01608v1
• Date: Wed, 02 Jul 2025 11:21:38 GMT
• Title: Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference
• Authors: Xu Zhang, Ming Lu, Yan Chen, Zhan Ma,
• Abstract summary: Perception-Oriented Latent Coding (POLC) is an approach that enriches the semantic content of latent features for high-performance semantic inference.<n>POLC requires only a plug-and-play adapter for fine-tuning, significantly reducing the parameter count compared to previous MSE-oriented methods.
• Score: 30.78149130760627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, compressed domain semantic inference has primarily relied on learned image coding models optimized for mean squared error (MSE). However, MSE-oriented optimization tends to yield latent spaces with limited semantic richness, which hinders effective semantic inference in downstream tasks. Moreover, achieving high performance with these models often requires fine-tuning the entire vision model, which is computationally intensive, especially for large models. To address these problems, we introduce Perception-Oriented Latent Coding (POLC), an approach that enriches the semantic content of latent features for high-performance compressed domain semantic inference. With the semantically rich latent space, POLC requires only a plug-and-play adapter for fine-tuning, significantly reducing the parameter count compared to previous MSE-oriented methods. Experimental results demonstrate that POLC achieves rate-perception performance comparable to state-of-the-art generative image coding methods while markedly enhancing performance in vision tasks, with minimal fine-tuning overhead. Code is available at https://github.com/NJUVISION/POLC.

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