  Nowadays, there is an explosive growth of screen contents due to the wide
application of screen sharing, remote cooperation, and online education. To
match the limited terminal bandwidth, high-resolution (HR) screen contents may
be downsampled and compressed. At the receiver side, the super-resolution (SR)
of low-resolution (LR) screen content images (SCIs) is highly demanded by the
HR display or by the users to zoom in for detail observation. However, image SR
methods mostly designed for natural images do not generalize well for SCIs due
to the very different image characteristics as well as the requirement of SCI
browsing at arbitrary scales. To this end, we propose a novel Implicit
Transformer Super-Resolution Network (ITSRN) for SCISR. For high-quality
continuous SR at arbitrary ratios, pixel values at query coordinates are
inferred from image features at key coordinates by the proposed implicit
transformer and an implicit position encoding scheme is proposed to aggregate
similar neighboring pixel values to the query one. We construct benchmark SCI1K
and SCI1K-compression datasets with LR and HR SCI pairs. Extensive experiments
show that the proposed ITSRN significantly outperforms several competitive
continuous and discrete SR methods for both compressed and uncompressed SCIs.
