Service-based Processing and Provisioning of Image-Abstraction Techniques

Abstract

Digital images and image streams represent two major categories of media captured, delivered, and shared on the Web. Techniques for their analysis, classification, and processing are fundamental building blocks in today's digital media applications ranging from mobile image transformation apps to professional digital production suites. To efficiently process such digital media (1) independent of hardware requirements, (2) at different data complexity scales, while (3) yielding high-quality results, poses several challenges for software frameworks and hardware systems, in particular for mobile devices. With respect to these aspects, using service-based architectures is a common approach to strive for. However, unlike geodata, there is currently no standard approach for service definition, implementation, and orchestration in the domain of digital images and videos. This paper presents an approach for service-based image-processing and provisioning of processing techniques by the example of imageabstraction techniques. The generality and feasibility of the proposed system is demonstrated by different client applications that have been implemented for the Android operating system, for Google's G-Suite Software-as-a-Service infrastructure, as well as for desktop systems. The performance of the system is discussed at the example of complex, resource-intensive image-abstraction techniques, such as watercolor rendering.

CERVI: collaborative editing of raster and vector images

“…Server-side implementation based on microservices have the advantage of increased scalability, easy deployment and maintainability as well as, the possibility to introduce various technologies into one system [24]. However, high update latencies limit their usage in realtime applications [21,25]. Therefore, we favor the integration of client-side processing for our approach by developing a WebGL-based image processor.…”

Service-based Processing of Gigapixel Images

“…For our work, we are extending a microservice platform for cloud-based visual analysis and processing that was first presented by Richter et al [16]. Based on that, Wegen et al [19] present an approach for performing service-based image processing using software rendering to balance cost-performance relation.…”

Performance Evaluation and Comparison of Service-based Image Processing based on Software Rendering

“…This section describes the approach for enabling software rendering for an Open Graphics Library (OpenGL)-based rendering framework for image processing techniques [29]. In particular, based on our system requirements, this comprises justification of middle-ware choices and specifics for the deployment process that is suitable for cloud-computing providers.…”

“…As shown in Figure 3, one container runs the actual image processor instance, while the other container exposes a REST interface for communication with the client. It consists of a NodeJS [34] server that communicates with the image processor instance via WebSockets [29]. The image processor itself had a profiler implementation that could also be configured using the REST interface and performed measurements whenever an image was processed.…”