Deep Image Compression Using Scene Text Quality Assessment
Shohei Uchigasaki Tomo Miyazaki Shinichiro Omachi
Image compression is a fundamental technology for Internet communication
engineering. However, a high compression rate with general methods may degrade
images, resulting in unreadable texts. In this paper, we propose an image
compression method for maintaining text quality. We developed a scene text
image quality assessment model to assess text quality in compressed images. The
assessment model iteratively searches for the best-compressed image holding
high-quality text. Objective and subjective results showed that the proposed
method was superior to existing methods. Furthermore, the proposed assessment
model outperformed other deep-learning regression models.