Prefetching and Caching Strategies for Remote and Interactive Browsing of JPEG2000 Images

Abstract

This paper considers the issues of scheduling and caching JPEG2000 data in client/server interactive browsing applications, under memory and channel bandwidth constraints. It analyzes how the conveyed data have to be selected at the server and managed within the client cache so as to maximize the reactivity of the browsing application. Formally, to render the dynamic nature of the browsing session, we assume the existence of a reaction model that defines when the user launches a novel command as a function of the image quality displayed at the client. As a main outcome, our work demonstrates that, due to the latency inherent to client/server exchanges, a priori expectation about future navigation commands may help to improve the overall reactivity of the system. In our study, the browsing session is defined by the evolution of a rectangular window of interest (WoI) along the time. At any given time, the WoI defines the position and the resolution of the image data to display at the client. The expectation about future navigation commands is then formalized based on a stochastic navigation model, which defines the probability that a given WoI is requested next, knowing previous WoI requests. Based on that knowledge, several scheduling scenarios are considered. The first scenario is conventional and transmits all the data corresponding to the current WoI before prefetching the most promising data outside the current WoI. Alternative scenarios are then proposed to anticipate prefetching, by scheduling data expected to be requested in the future before all the current WoI data have been sent out. Our results demonstrate that, for predictable navigation commands, anticipated prefetching improves the overall reactivity of the system by up to 30% compared to the conventional scheduling approach. They also reveal that an accurate knowledge of the reaction model is not required to get these significant improvements.

SEVQER: Automatic Semantic Visual Query Builder to Support Intelligent Image Search in Traffic Images

“…Most of the browsing models (Pečenovió et al, 2000;Chen et al, 2000a;Descampe et al, 2007;Bederson, 2001;Chen et al, 2000b) arrange the images into a hierarchy or structure that can be browsed interactively by users. The greatest challenge of a browsing approach is to identify structures that contribute to effective search.…”

Optimized mobile thin clients through a MPEG-4 BiFS semantic remote display framework

“…Furthermore, an integrated content block cache helps in reducing the mobile client latency to fetch HTML content from a web server. Similarly, in [5], a model is presented that tries to predict the future navigation commands during an interactive browsing session to optimize the client cache of mobile devices. The presented work focuses on improving the remote browsing of large scale images, which is challenging because of the small size of mobile displays and the cost and latency associated to data transmission.…”

“…In order to benchmark the performance of the BiFS prototype, three other open source remote display architectures were installed on the testbed: RealVNC v4.1.1 [27], TightVNC v1.3.10 [37] and FreeNX v3.4.0 [8]. Although all these architectures are Linux based, their bandwidth consumption is in the same order of magnitude of commercially available Windows-based architectures [5,31], such as Microsoft RDP or Citrix XenApp, and consequently they can be considered as a relevant benchmark.…”

Sistemas de microscopía virtual: análisis y perspectivas

“…Por otra parte, las estrategias de prefetching permiten cargar anticipadadamente la información que va a ser solicitada por el usuario, es decir, esta clase de estrategias busca predecir los intereses del usuario y las regiones de la imagen que serán visitadas en un futuro próximo. Esta clase de modelos han sido utilizados en navegación de ambientes virtuales (33) y en navegación de grandes imágenes (34). No obstante, su éxito depende de la capacidad del sistema para predecir el patrón de navegación del usuario.…”