Image Decomposition Using a Robust Regression Approach

TL;DR

This paper introduces a robust regression-based algorithm for segmenting text and graphics from smooth backgrounds in screen content images, outperforming existing methods in accuracy.

Contribution

The paper presents a novel robust regression approach for background-foreground segmentation in screen content images, along with a new dataset for benchmarking.

Findings

The proposed method achieves superior segmentation accuracy.

It outperforms hierarchical k-means and shape primitive methods.

The algorithm effectively separates sharp foreground from smooth background.

Abstract

This paper considers how to separate text and/or graphics from smooth background in screen content and mixed content images and proposes an algorithm 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. This algorithm separates the background and foreground pixels by trying to fit pixel values in the block into a smooth function using a robust regression method. The inlier pixels that can be well represented with the smooth model will be considered as background, while remaining outlier pixels will be considered foreground. We have also created a dataset of screen content images extracted from HEVC standard test sequences for screen content coding with their ground truth segmentation result which can be used for this task. The proposed algorithm has been tested on the dataset mentioned above and is shown to have superior performance over other methods, such as the hierarchical k-means clustering algorithm, shape primitive extraction and coding, and the least absolute deviation fitting scheme for foreground segmentation.