论文标题
学习驱动的有损图像压缩；全面的调查
Learning-Driven Lossy Image Compression; A Comprehensive Survey
论文作者
论文摘要
在图像处理和计算机视觉（CV）领域，机器学习（ML）体系结构被广泛应用。卷积神经网络（CNN）解决了广泛的图像处理问题，可以解决图像压缩问题。由于带宽和内存约束，需要压缩图像。有用，多余和无关的信息是图像中发现的三种不同形式的信息。本文旨在调查最近使用ML架构（包括不同自动编码器（AE））（例如卷积自动编码器（CAES），变量自动编码器（VAE））的ML体系结构的最新技术，以及与超优先模型，复发性神经网络（RNNS），cnns，cnns，gersative consartial consartial consartial网络（vAES），a gersative consartial consertial网络（vaes） （PCA）和模糊意味着聚类。我们根据体系结构将所有算法分为几个组。我们在此调查中涵盖了图像压缩。强调研究人员的各种发现，并可能对研究人员提出可能的未来指示。开放研究问题，例如与中央处理单元（CPU）（CPU）（CPU）和图形处理单元（GPU）的框架的框架，混叠和兼容性等开放研究问题。调查的压缩领域中的大多数出版物都来自过去的五年，并采用了各种方法。
In the realm of image processing and computer vision (CV), machine learning (ML) architectures are widely applied. Convolutional neural networks (CNNs) solve a wide range of image processing issues and can solve image compression problem. Compression of images is necessary due to bandwidth and memory constraints. Helpful, redundant, and irrelevant information are three different forms of information found in images. This paper aims to survey recent techniques utilizing mostly lossy image compression using ML architectures including different auto-encoders (AEs) such as convolutional auto-encoders (CAEs), variational auto-encoders (VAEs), and AEs with hyper-prior models, recurrent neural networks (RNNs), CNNs, generative adversarial networks (GANs), principal component analysis (PCA) and fuzzy means clustering. We divide all of the algorithms into several groups based on architecture. We cover still image compression in this survey. Various discoveries for the researchers are emphasized and possible future directions for researchers. The open research problems such as out of memory (OOM), striped region distortion (SRD), aliasing, and compatibility of the frameworks with central processing unit (CPU) and graphics processing unit (GPU) simultaneously are explained. The majority of the publications in the compression domain surveyed are from the previous five years and use a variety of approaches.
