Survey on Visual Signal Coding and Processing With Generative Models: Technologies, Standards, and Optimization

IF 3.7 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-03-21 DOI:10.1109/JETCAS.2024.3403524
Zhibo Chen;Heming Sun;Li Zhang;Fan Zhang
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Abstract

This paper provides a survey of the latest developments in visual signal coding and processing with generative models. Specifically, our focus is on presenting the advancement of generative models and their influence on research in the domain of visual signal coding and processing. This survey study begins with a brief introduction of well-established generative models, including the Variational Autoencoder (VAE) models, Generative Adversarial Network (GAN) models, Autoregressive (AR) models, Normalizing Flows and Diffusion models. The subsequent section of the paper explores the advancements in visual signal coding based on generative models, as well as the ongoing international standardization activities. In the realm of visual signal processing, our focus lies on the application and development of various generative models in the research of visual signal restoration. We also present the latest developments in generative visual signal synthesis and editing, along with visual signal quality assessment using generative models and quality assessment for generative models. The practical implementation of these studies is closely linked to the investigation of fast optimization. This paper additionally presents the latest advancements in fast optimization on visual signal coding and processing with generative models. We hope to advance this field by providing researchers and practitioners a comprehensive literature review on the topic of visual signal coding and processing with generative models.
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使用生成模型的视觉信号编码和处理调查:技术、标准和优化
本文概述了使用生成模型进行视觉信号编码和处理的最新进展。具体来说,我们的重点是介绍生成模型的发展及其对视觉信号编码和处理领域研究的影响。本调查研究首先简要介绍了成熟的生成模型,包括变异自动编码器(VAE)模型、生成对抗网络(GAN)模型、自回归(AR)模型、归一化流和扩散模型。本文随后的章节将探讨基于生成模型的视觉信号编码技术的发展,以及正在进行的国际标准化活动。在视觉信号处理领域,我们的重点是各种生成模型在视觉信号还原研究中的应用和发展。我们还介绍了生成式视觉信号合成和编辑的最新发展,以及使用生成式模型进行的视觉信号质量评估和生成式模型的质量评估。这些研究的实际应用与快速优化研究密切相关。本文还介绍了快速优化在使用生成模型进行视觉信号编码和处理方面的最新进展。我们希望通过为研究人员和从业人员提供关于使用生成模型进行视觉信号编码和处理这一主题的全面文献综述,推动这一领域的发展。
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来源期刊
CiteScore
8.50
自引率
2.20%
发文量
86
期刊介绍: The IEEE Journal on Emerging and Selected Topics in Circuits and Systems is published quarterly and solicits, with particular emphasis on emerging areas, special issues on topics that cover the entire scope of the IEEE Circuits and Systems (CAS) Society, namely the theory, analysis, design, tools, and implementation of circuits and systems, spanning their theoretical foundations, applications, and architectures for signal and information processing.
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Introducing IEEE Collabratec Table of Contents IEEE Journal on Emerging and Selected Topics in Circuits and Systems Information for Authors IEEE Circuits and Systems Society Information IEEE Journal on Emerging and Selected Topics in Circuits and Systems Publication Information
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