论文标题
卷积神经网络（CNN）减少由离散傅立叶变换（DFT）引起的JPEG压缩中的施工损失
Convolutional Neural Network (CNN) to reduce construction loss in JPEG compression caused by Discrete Fourier Transform (DFT)
论文作者
论文摘要
近几十年来，数字图像处理已广受欢迎。因此，已经提出了许多数据压缩策略，目的是最大程度地减少表示图像所需的信息量。其中，JPEG压缩是广泛应用于多媒体和数字应用的最流行方法之一。 DFT的周期性不可能在不产生严重伪像的情况下满足图像相对边缘的周期性状况，从而降低了图像的感知视觉质量。另一方面，深度学习最近在语音识别，减少图像和自然语言处理等应用方面取得了出色的成果。卷积神经网络（CNN）比大多数其他类型的深神经网络受到更多关注。在特征提取中使用卷积会导致冗余特征图和较小的数据集，这两个数据集对于图像压缩至关重要。在这项工作中，有效的图像压缩方法是使用自动编码器的。该研究的发现揭示了许多重要的趋势，这些趋势表明，可以使用自动编码器实现更好的重建以及良好的压缩。
In recent decades, digital image processing has gained enormous popularity. Consequently, a number of data compression strategies have been put forth, with the goal of minimizing the amount of information required to represent images. Among them, JPEG compression is one of the most popular methods that has been widely applied in multimedia and digital applications. The periodic nature of DFT makes it impossible to meet the periodic condition of an image's opposing edges without producing severe artifacts, which lowers the image's perceptual visual quality. On the other hand, deep learning has recently achieved outstanding results for applications like speech recognition, image reduction, and natural language processing. Convolutional Neural Networks (CNN) have received more attention than most other types of deep neural networks. The use of convolution in feature extraction results in a less redundant feature map and a smaller dataset, both of which are crucial for image compression. In this work, an effective image compression method is purposed using autoencoders. The study's findings revealed a number of important trends that suggested better reconstruction along with good compression can be achieved using autoencoders.
