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

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.

• The paper introduces the ID-GAN algorithm that achieves extreme compression while preserving semantic information, perceptual quality, and classification accuracy.
• It explores the intricate tradeoff in joint source coding and modulation by balancing compression rate, image distortion, and classification performance.
• The study develops the RDPCO heuristic to reveal optimal compressed dimensions and validate its approach using experimental results on the MNIST dataset.

Understanding the Balance Between Compression and Information Preservation in AI-Enabled Image Transmission

Introduction to the Tradeoff Problem

In the field of transmitting images through noisy channels, such as those found in underwater communication, a significant challenge exists. Traditional methods, which separate source coding (compression) and channel coding (error correction), may fail dramatically under conditions of limited bandwidth or rapidly changing channels. To overcome such limitations, a paradigm known as Joint Source Coding and Modulation (JSCM) has gained prominence. JSCM benefits from advancements in deep learning to automatically learn optimal compression and modulation schemes in an end-to-end fashion. A key aspect of JSCM is managing a complex tradeoff between channel rate, image distortion, perceptual quality, and classification accuracy, encapsulated in what's dubbed as the rate-distortion-perception-classification (RDPC) function.

Delving into RDPC

The RDPC function is introduced to examine this tradeoff within the JSCM framework. Traditional studies have allowed the assessment of features based on their ability to compress, yet considering metrics like rate, distortion, and classification accuracy simultaneously introduces a new level of complexity. The study explores this convoluted tradeoff and attempts to manage the differing needs of various metrics. Enhancing rates may lead to increased distortion or may adversely impact classification accuracy or perceived image quality. Therefore, it's crucial to understand how varying rates, distortion limits, and classification constraints affect system performance in JSCM.

Introducing ID-GAN: A Solution for Extreme Compression

One of the primary contributions of this research is the introduction of the inverse-domain generative adversarial network (ID-GAN) algorithm, designed to handle extreme image compression. ID-GAN boasts its ability to sustain semantic information, maintain perceptual quality, and ensure fidelity in image reconstruction, even within the strenuous constraints of low-capacity channels. Contrasting ID-GAN against traditional methods and other deep JSCM architectures, such as D-JSCC and AE+GAN, shows significant improvements in system performance across various assessment metrics.

Algorithmic Insights from RDPCO

Another important development is a heuristic algorithm called RDPCO, designed to provide insights by simplifying the RDPC problem's assumptions. Despite its approximate nature, RDPCO unveils crucial understandings related to the JSCM framework's constraints and responses. The experimental results and the visual examples using the MNIST dataset illustrate the balance and tradeoffs the system must achieve between maintaining low-rate transmission and high-fidelity reconstruction, hinting at the existence of an optimal compressed dimension for minimizing rates while meeting quality constraints.

Conclusion and Future Thought

The paper's findings not only deepen our understanding of the intricate RDPC tradeoff but also introduce practical solutions such as ID-GAN for real-world applications in image transmission over constrained channels. This research lays the groundwork for further developments in JSCM systems that seek to balance compression efficacy with the preservation of crucial image information.