Screen content image evaluation and processing
Huan Yang · 2015
Screen Content Image (SCI) is a typical kind of compound images that contain texts, graphics and pictures concurrently. With the rapid development of digital devices and computing techniques, SCIs have increasingly appeared in multi-client communication systems. The related applications bring many challenges on SCI processing, such as acquisition, segmentation, compression, transmission, quality evaluation, etc. SCIs have different characteristics from natural scene images and scanned document images, which result in the fact that existing classical image processing methods cannot effectively process SCIs. Hence, specialized algorithms for SCI processing are much desired. Currently, there is no much research work in the literature for SCI processing. In this research work, we try to understand the basic properties of SCIs and focus on addressing challenging problems in the following three aspects, i.e., segmentation, compression and perceptual quality assessment of SCIs. SCI segmentation, which aims to distinguish texts from other components, is a fundamental step in various SCI processing techniques. In this research work, we firstly propose a coarse-to-fine framework to segment texts with arbitrary scales and orientations from other components in SCIs. A Local Image Activity Measure (LIAM) is designed to enhance the difference between textual and pictorial regions and eliminate most of pictorial regions with low frequency. In order to remove survived pictorial regions (mistaken as texts), a new Scale and Orientation Invariant Grouping (SOIG) algorithm is proposed to construct Textual Connected Components (TCCs) with uniform geometrical features. False positive components are finally filtered out by three verification criteria. The proposed text segmentation algorithm can maintain integrity of texts with varied scales and orientations, which benefits the compression and evaluation procedures for SCIs. v