Screen Content Image Segmentation Using Robust Regression and Sparse Decomposition

TL;DR

This paper introduces two novel algorithms for segmenting text and graphics from smooth backgrounds in screen content images, leveraging robust regression and sparse decomposition to improve accuracy over previous methods.

Contribution

The paper presents two new segmentation algorithms that effectively distinguish foreground from background in screen content images using robust regression and sparse decomposition.

Findings

Algorithms outperform previous methods in accuracy.

Effective in applications like text extraction and image compression.

Validated on HEVC test sequences.

Abstract

This paper considers how to separate text and/or graphics from smooth background in screen content and mixed document images and proposes two approaches to perform this segmentation task. The proposed methods make use of the fact that the background in each block is usually smoothly varying and can be modeled well by a linear combination of a few smoothly varying basis functions, while the foreground text and graphics create sharp discontinuity. The algorithms separate the background and foreground pixels by trying to fit background pixel values in the block into a smooth function using two different schemes. One is based on robust regression, where the inlier pixels will be considered as background, while remaining outlier pixels will be considered foreground. The second approach uses a sparse decomposition framework where the background and foreground layers are modeled with a smooth and sparse components respectively. These algorithms have been tested on images extracted from HEVC standard test sequences for screen content coding, and are shown to have superior performance over previous approaches. The proposed methods can be used in different applications such as text extraction, separate coding of background and foreground for compression of screen content, and medical image segmentation.