Screen Content Image Segmentation Using Sparse Decomposition and Total Variation Minimization

TL;DR

This paper introduces a novel segmentation algorithm for screen content images that combines sparse decomposition with total variation minimization to effectively separate background and foreground elements.

Contribution

The proposed method uniquely models background as smoothly varying and foreground as sparse, improving segmentation accuracy over prior techniques.

Findings

Outperforms previous segmentation methods on HEVC screen content images.

Effectively separates background and foreground with high accuracy.

Demonstrates robustness across different screen content scenarios.

Abstract

Sparse decomposition has been widely used for different applications, such as source separation, image classification, image denoising and more. This paper presents a new algorithm for segmentation of an image into background and foreground text and graphics using sparse decomposition and total variation minimization. The proposed method is designed based on the assumption that the background part of the image is smoothly varying and can be represented by a linear combination of a few smoothly varying basis functions, while the foreground text and graphics can be modeled with a sparse component overlaid on the smooth background. The background and foreground are separated using a sparse decomposition framework regularized with a few suitable regularization terms which promotes the sparsity and connectivity of foreground pixels. This algorithm has been tested on a dataset of images extracted from HEVC standard test sequences for screen content coding, and is shown to have superior performance over some prior methods, including least absolute deviation fitting, k-means clustering based segmentation in DjVu and shape primitive extraction and coding (SPEC) algorithm.