Internet Access, Spillover and Regional Development in China

Journal ArticleAs Internet access grows at different rates across regions, the Internet has had variable effects on regional economies through agglomeration and spillover effects. This paper uses province-level panel data from 2000 to 2013 to study inequality in Internet access, its spatial effect on regional economies in China and the channels through which the spillover effects are most evident. We find that the Internet has dispersed quickly from core cities, such as Beijing and Shanghai, to coastal provinces; and has had increasingly significant effects on neighboring regions. However, the Internet speed is still comparatively low outside the core cities. We then use endogenous growth models to quantify the effect of Internet access on regional economies. Our results show that, while Internet dispersion is positively associated with economic growth, the spillover effect varies significantly by region and is more pronounced in developed regions. So is the effect of the science and technology environment. Developed regions have benefited the most in the process. The three channels of spillover are listed here in order of relative significance: economy, proximity and urbanization. The spillover effect of the Internet may lead to the divergence of regional economies, working against the national goal of reducing regional inequalit

PEA265: Perceptual Assessment of Video Compression Artifacts

The most widely used video encoders share a common hybrid coding framework that includes block-based motion estimation/compensation and block-based transform coding. Despite their high coding efficiency, the encoded videos often exhibit visually annoying artifacts, denoted as Perceivable Encoding Artifacts (PEAs), which significantly degrade the visual Qualityof- Experience (QoE) of end users. To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs. In this work, we make the first attempt to build a large-scale subjectlabelled database composed of H.265/HEVC compressed videos containing various PEAs. The database, namely the PEA265 database, includes 4 types of spatial PEAs (i.e. blurring, blocking, ringing and color bleeding) and 2 types of temporal PEAs (i.e. flickering and floating). Each containing at least 60,000 image or video patches with positive and negative labels. To objectively identify these PEAs, we train Convolutional Neural Networks (CNNs) using the PEA265 database. It appears that state-of-theart ResNeXt is capable of identifying each type of PEAs with high accuracy. Furthermore, we define PEA pattern and PEA intensity measures to quantify PEA levels of compressed video sequence. We believe that the PEA265 database and our findings will benefit the future development of video quality assessment methods and perceptually motivated video encoders.Comment: 10 pages,15 figures,4 table