End-to-end Compression Towards Machine Vision: Network Architecture Design and Optimization

Abstract

The visual signal compression is a long-standing problem. Fueled by the recent advances of deep learning, exciting progress has been made. Despite better compression performance, existing end-to-end compression algorithms are still designed towards better signal quality in terms of rate-distortion optimization. In this paper, we show that the design and optimization of network architecture could be further improved for compression towards machine vision. We propose an inverted bottleneck structure for the encoder of the end-to-end compression towards machine vision, which specifically accounts for efficient representation of the semantic information. Moreover, we quest the capability of optimization by incorporating the analytics accuracy into the optimization process, and the optimality is further explored with generalized rate-accuracy optimization in an iterative manner. We use object detection as a showcase for end-to-end compression towards machine vision, and extensive experiments show that the proposed scheme achieves significant BD-rate savings in terms of analysis performance. Moreover, the promise of the scheme is also demonstrated with strong generalization capability towards other machine vision tasks, due to the enabling of signal-level reconstruction.

References in corpus (6)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Joint Autoregressive and Hierarchical Priors for Learned Image Compression
• Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics
• CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
• Towards Image Understanding from Deep Compression without Decoding
• End-to-End Facial Deep Learning Feature Compression with Teacher-Student Enhancement