The Rate-Distortion-Perception-Classification Tradeoff: Joint Source Coding and Modulation via Inverse-Domain GANs

• URL: http://arxiv.org/abs/2312.14792v2
• Date: Thu, 6 Jun 2024 10:52:14 GMT
• Title: The Rate-Distortion-Perception-Classification Tradeoff: Joint Source Coding and Modulation via Inverse-Domain GANs
• Authors: Junli Fang, João F. C. Mota, Baoshan Lu, Weicheng Zhang, Xuemin Hong,
• Abstract summary: We show the existence of a strict tradeoff between channel rate, distortion perception, and classification accuracy. We propose two image compression methods to navigate that tradeoff: theCO algorithm and ID-GAN, which is more general compression. They also demonstrate that the proposed ID-GAN algorithm balances image distortion, perception, classification accuracy, and significantly outperforms traditional separation-based methods.
• Score: 4.735670734773145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The joint source-channel coding (JSCC) framework leverages deep learning to learn from data the best codes for source and channel coding. When the output signal, rather than being binary, is directly mapped onto the IQ domain (complex-valued), we call the resulting framework joint source coding and modulation (JSCM). We consider a JSCM scenario and show the existence of a strict tradeoff between channel rate, distortion, perception, and classification accuracy, a tradeoff that we name RDPC. We then propose two image compression methods to navigate that tradeoff: the RDPCO algorithm which, under simple assumptions, directly solves the optimization problem characterizing the tradeoff, and an algorithm based on an inverse-domain generative adversarial network (ID-GAN), which is more general and achieves extreme compression. Simulation results corroborate the theoretical findings, showing that both algorithms exhibit the RDPC tradeoff. They also demonstrate that the proposed ID-GAN algorithm effectively balances image distortion, perception, and classification accuracy, and significantly outperforms traditional separation-based methods and recent deep JSCM architectures in terms of one or more of these metrics.

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