Development Of Image Processing Applications Research Articles

Ограничения применимости дифференцируемых эталонных показателей качества изображений

Subjective image-quality measurement plays a critical role in the development of image-processing applications. The purpose of a visual-quality metric is to approximate the results of subjective assessment. In this regard, more and more metrics are under development, but little research has considered their limitations. This paper addresses that deficiency: we show how image preprocessing before compression can artificially increase the quality scores provided by the popular metrics DISTS, LPIPS, HaarPSI, and VIF as well as how these scores are inconsistent with subjective-quality scores. We propose a series of neural-network preprocessing models that increase DISTS by up to 34.5%, LPIPS by up to 36.8%, VIF by up to 98.0%, and HaarPSI by up to 22.6% in the case of JPEG-compressed images. A subjective comparison of preprocessed images showed that for most of the metrics we examined, visual quality drops or stays unchanged, limiting the applicability of these metrics.

Visual programming system for development of image processing applications

Visualprogramming using reusable software components and a dataflow model of computation is proposed as a method to reduce software development time and cost in the construction of image processing applications. A graphical editor is described that allows interactive definition of logical relationships among components of systems through direct manipulation of on-screen icons. This editor manages the syntax of datafiow graphs and catalogs of component parts while the semantics of the operation of these parts is provided by a separate postprocessor. The postprocessor includes a user extensible set of primitive (i.e., low-level) component parts for image processing from which more complex algorithms may be constructed. This environment provides a powerful visual programming system for rapid interactive development of machine vision algorithms and management of reusable image processing software. A case study describing construction of a medical image processing application is included.