Improved Image Coding Autoencoder With Deep Learning
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In this paper, we build autoencoder based pipelines for extreme end-to-end image compression based on Ballé's approach, which is the state-of-the-art open source implementation in image compression using deep learning. We deepened the network by adding one more hidden layer before each strided convolutional layer with exactly the same number of down-samplings and up-samplings. Our approach outperformed Ballé's approach, and achieved around 4.0 structural similarity (MS-SSIM), and only 0.47 signal-to-noise ratio (PSNR), It also outperforms all traditional image compression methods including JPEG2000 and HEIC by at least 20 compression efficiency at similar reconstruction image quality. Regarding encoding and decoding time, our approach takes similar amount of time compared with traditional methods with the support of GPU, which means it's almost ready for industrial applications.
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