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.
翻译:目前,由于广泛应用屏幕共享、远程合作和在线教育,屏幕内容爆炸性增长,由于屏幕内容广泛应用、远程合作和在线教育,屏幕内容爆炸性增长。为了匹配有限的终端带宽,高分辨率(HR)屏幕内容可能会被降色和压缩。在接收器一侧,低分辨率(LR)屏幕内容图像的超级分辨率(SR)是人力资源显示或用户为放大以进行详细观察而高度要求的。然而,主要为自然图像设计的图像SR方法,由于图像特征非常不同,以及SCI在任意的尺度上浏览的要求,对 SCI 的图像特征没有很好地概括。为此,我们提议为SCISR建立一个新型的隐性超分辨率变异器超级分辨率网络(ITSRN)。对于任意比率的高质量连续SR,查询坐标上的等离子值是从关键坐标的图像特征推断出来的,建议采用隐性变异器和隐性定位编码方案,将相近像像像值汇总到查询的像值。我们为SCI1K和SCI1压缩机的压缩压缩机(IS)和SR(SR)的连续和高压性磁性磁制)试验。