Technology Category
Machine Learning: Information TheoryComputer Vision: Learning & Optimization for CVData Mining & Knowledge Management: Data Compression
Task-Oriented Lossy Compression with Data, Perception, and Classification Constraints
🤖 AI Summary
This work addresses lossy image compression for multi-task scenarios, proposing a framework that jointly optimizes reconstruction fidelity, perceptual quality, and classification accuracy. We establish, for the first time, an information-theoretic rate–distortion–classification (RDC/RPC) triadic model and derive its closed-form solution. Theoretically, we prove that under RPC constraints, classification performance and perceptual fidelity are not fundamentally trade-offs, and reveal the critical regulatory role of source noise in task-oriented compression. Leveraging the information bottleneck principle, we unify generative and discriminative objectives and derive optimal rate bounds for binary and Gaussian sources. Experiments demonstrate that our deep compression network achieves Pareto-optimality across PSNR, LPIPS, and classification accuracy—providing both theoretical foundations and a practical paradigm for task-driven compression.
Technology Category
Application Category
📝 Abstract
By extracting task-relevant information while maximally compressing the input, the information bottleneck (IB) principle has provided a guideline for learning effective and robust representations of the target inference. However, extending the idea to the multi-task learning scenario with joint consideration of generative tasks and traditional reconstruction tasks remains unexplored. This paper addresses this gap by reconsidering the lossy compression problem with diverse constraints on data reconstruction, perceptual quality, and classification accuracy. Firstly, we study two ternary relationships, namely, the rate-distortion-classification (RDC) and rate-perception-classification (RPC). For both RDC and RPC functions, we derive the closed-form expressions of the optimal rate for binary and Gaussian sources. These new results complement the IB principle and provide insights into effectively extracting task-oriented information to fulfill diverse objectives. Secondly, unlike prior research demonstrating a tradeoff between classification and perception in signal restoration problems, we prove that such a tradeoff does not exist in the RPC function and reveal that the source noise plays a decisive role in the classification-perception tradeoff. Finally, we implement a deep-learning-based image compression framework, incorporating multiple tasks related to distortion, perception, and classification. The experimental results coincide with the theoretical analysis and verify the effectiveness of our generalized IB in balancing various task objectives.
Problem
Research questions and friction points this paper is trying to address.
Image Compression
Visual Quality Preservation
Classification Accuracy
Innovation
Methods, ideas, or system contributions that make the work stand out.
Information Bottleneck Principle
Multi-task Image Compression
Deep Learning Image Compression System
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