Learned Image Compression with Text Quality Enhancement
Chih-Yu Lai Dung Tran Kazuhito Koishida
Learned image compression has gained widespread popularity for their
efficiency in achieving ultra-low bit-rates. Yet, images containing substantial
textual content, particularly screen-content images (SCI), often suffers from
text distortion at such compressed levels. To address this, we propose to
minimize a novel text logit loss designed to quantify the disparity in text
between the original and reconstructed images, thereby improving the perceptual
quality of the reconstructed text. Through rigorous experimentation across
diverse datasets and employing state-of-the-art algorithms, our findings reveal
significant enhancements in the quality of reconstructed text upon integration
of the proposed loss function with appropriate weighting. Notably, we achieve a
Bjontegaard delta (BD) rate of -32.64% for Character Error Rate (CER) and
-28.03% for Word Error Rate (WER) on average by applying the text logit loss
for two screenshot datasets. Additionally, we present quantitative metrics
tailored for evaluating text quality in image compression tasks. Our findings
underscore the efficacy and potential applicability of our proposed text logit
loss function across various text-aware image compression contexts.