In this work, we propose a Wavelet-based Deep Auto Encoder-Decoder Network (WDAED) based image compression which takes care of the various frequency components present in an image. Specifically, we demonstrate improvements over prior approaches utilizing this framework by introducing: (a) wavelet transform pre-processing for decomposing image into different frequencies for their separate processing (b) a very deep super-resolution network as a decoder of the convolutional autoencoder in order to achieve a good quality decompressed image. The end-to-end learning is performed for four wavelet sub-bands in parallel, minimizing the computational time. The encoder compresses the image by generating the latent space representations, whereas the decoder transforms the latent space to image space. The algorithm has been tested on various standard datasets i.e., ImageNet, Set 5, Set 14, Live 1, Kodak, Classic 5, General 100 and CLIC 2019 dataset. The proposed algorithm clearly exhibited the compression performance improvement of approximately 5%, 5.5%, and 13% in terms of PSNR, PSNRB and SSIM respectively.

中文翻译：

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基于小波的深度自动编码器（WDAED）图像压缩

在这项工作中，我们提出了一种基于小波的深度自动编码器-解码器网络（WDAED）的图像压缩技术，该技术可以处理图像中存在的各种频率分量。具体来说，我们通过引入以下内容展示了利用该框架的现有方法的改进：（a）小波变换预处理，用于将图像分解为不同的频率以对其进行单独处理（b）非常深的超分辨率网络，作为卷积自动编码器的解码器为了获得高质量的解压缩图像。对四个小波子带并行执行端到端学习，从而最大程度地减少了计算时间。编码器通过生成潜在空间表示来压缩图像，而解码器将潜在空间转换为图像空间。该算法已在各种标准数据集上进行了测试，即ImageNet，Set 5，Set 14，Live 1，柯达，Classic 5，General 100和CLIC 2019数据集。所提出的算法显然在PSNR，PSNRB和SSIM方面分别表现出大约5％，5.5％和13％的压缩性能改进。