  Lossy Image compression is necessary for efficient storage and transfer of
data. Typically the trade-off between bit-rate and quality determines the
optimal compression level. This makes the image quality metric an integral part
of any imaging system. While the existing full-reference metrics such as PSNR
and SSIM may be less sensitive to perceptual quality, the recently introduced
learning methods may fail to generalize to unseen data. In this paper we
propose the largest image compression quality dataset to date with human
perceptual preferences, enabling the use of deep learning, and we develop a
full reference perceptual quality assessment metric for lossy image compression
that outperforms the existing state-of-the-art methods. We show that the
proposed model can effectively learn from thousands of examples available in
the new dataset, and consequently it generalizes better to other unseen
datasets of human perceptual preference.
