  Error-bounded lossy compression is becoming an indispensable technique for
the success of today's scientific projects with vast volumes of data produced
during simulations or instrument data acquisitions. Not only can it
significantly reduce data size, but it also can control the compression errors
based on user-specified error bounds. Autoencoder (AE) models have been widely
used in image compression, but few AE-based compression approaches support
error-bounding features, which are highly required by scientific applications.
To address this issue, we explore using convolutional autoencoders to improve
error-bounded lossy compression for scientific data, with the following three
key contributions. (1) We provide an in-depth investigation of the
characteristics of various autoencoder models and develop an error-bounded
autoencoder-based framework in terms of the SZ model. (2) We optimize the
compression quality for the main stages in our designed AE-based error-bounded
compression framework, fine-tuning the block sizes and latent sizes and also
optimizing the compression efficiency of latent vectors. (3) We evaluate our
proposed solution using five real-world scientific datasets and compare them
with six other related works. Experiments show that our solution exhibits a
very competitive compression quality among all the compressors in our tests. In
absolute terms, it can obtain a much better compression quality (100% ~ 800%
improvement in compression ratio with the same data distortion) compared with
SZ2.1 and ZFP in cases with a high compression ratio.
