Image Decomposition Using a Robust Regression Approach
This addresses the segmentation problem for screen content coding, but it is incremental as it builds on existing robust regression techniques.
The paper tackles the problem of separating text/graphics from smooth backgrounds in screen content images by proposing an algorithm that uses robust regression to model the background as a smooth function, with inliers as background and outliers as foreground. It shows superior performance over methods like hierarchical k-means clustering and least absolute deviation fitting on a new dataset extracted from HEVC test sequences.
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.