Screen Content Image Segmentation Using Least Absolute Deviation Fitting
This work addresses the need for efficient pre-processing in text extraction and compression for screen content images, though it appears incremental as it builds on prior segmentation techniques.
The paper tackles the problem of segmenting foreground text and graphics from smoothly varying backgrounds in screen content images by using a least absolute deviation fitting method, achieving superior performance over existing methods like k-means clustering and SPEC algorithm in tests on HEVC standard sequences.
We propose an algorithm for separating the foreground (mainly text and line graphics) from the smoothly varying background in screen content images. 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 create sharp discontinuity and cannot be modeled by this smooth representation. The algorithm separates the background and foreground using a least absolute deviation method to fit the smooth model to the image pixels. This algorithm has been tested on several images from HEVC standard test sequences for screen content coding, and is shown to have superior performance over other popular methods, such as k-means clustering based segmentation in DjVu and shape primitive extraction and coding (SPEC) algorithm. Such background/foreground segmentation are important pre-processing steps for text extraction and separate coding of background and foreground for compression of screen content images.