Quantization and Bitrate Control in GAN-Based Coding

EasyChair Preprint 14314

Abstract

Generative Adversarial Networks (GANs) have emerged as a transformative approach in digital media coding and compression, offering significant advancements over traditional methods. This paper explores the crucial aspects of quantization and bitrate control within the context of GAN-based coding. Quantization plays a pivotal role in translating continuous-valued GAN outputs into discrete values suitable for compression, while bitrate control manages the trade-off between data rate and quality. We examine various quantization techniques, including scalar and vector quantization, and their impact on the fidelity and efficiency of GAN-generated content. Additionally, we delve into bitrate control mechanisms, comparing fixed and variable bitrate approaches and highlighting adaptive methods tailored for GAN-based systems. Performance is evaluated through key metrics such as Peak Signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM), and compression efficiency. The paper concludes with a discussion on future directions, addressing current challenges and proposing potential avenues for improvement. This study provides a comprehensive overview of how quantization and bitrate control interact with GAN-based coding, offering insights for enhancing digital media compression techniques.

Keyphrases: Bitrate Control, Coding Techniques, Generative Adversarial Networks, Quantization, compression efficiency, data compression

@booklet{EasyChair:14314,
  author    = {Harold Jonathan and Samon Daniel},
  title     = {Quantization and Bitrate Control in GAN-Based Coding},
  howpublished = {EasyChair Preprint 14314},
  year      = {EasyChair, 2024}}