Screen Content Image Segmentation Using Sparse Decomposition and Total Variation Minimization
This addresses the need for efficient segmentation in screen content coding, but it is incremental as it builds on existing sparse decomposition techniques with specific regularizations.
The paper tackles the problem of segmenting screen content images into background and foreground text/graphics by proposing a new algorithm using sparse decomposition and total variation minimization, achieving superior performance over prior methods like least absolute deviation fitting and k-means clustering on a dataset from HEVC test sequences.
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.